assessing the impact of urban expansion...

ASSESSING THE IMPACT OF URBAN EXPANSION ON

LAND SURFACE TEMPERATURE IN LAHORE USING

REMOTE SENSING TECHNIQUES

By

Muhammad Nasar u Minallah

Thesis Submitted in Partial Fulfillment of

The Requirements for the Degree of

DOCTOR OF PHILOSOPHY

IN

GEOGRAPHY

DEPARTMENT OF GEOGRAPHY

UNIVERSITY OF THE PUNJAB

LAHORE

II

ABSTRACT

Lahore, a metropolis of Pakistan, has been experiencing rapid urban growth over

the past few decades. The growth of urban population and socio-economic development

has resulted in the rapid increase of urban expansion. A major impact of this urban

expansion is being observed in the form of an increase in land surface temperature which

can be determined by using thermal infrared band of Landsat images. In order to

comprehend urban climate, LST is essential to be observed. The present research is an

effort to evaluate urban expansion and its impact on Land Surface Temperature (LST) of

Lahore, Pakistan through remote sensing technique. It reveals LST’s spatial patterns,

explores its relationship with the dynamics of urban expansion relative to the land use

change. Two sets of temperature data are used in this research: satellite image data

utilizing six dates of Landsat 5/TM, 7/ETM+ and 8/OLI_TIRs imagery obtained in 1973,

1980, 1990, 2000, 2010 and 2015, respectively and the ground weather station

atmospheric temperature data, over a long period (1950 to 2015) for the urban and the

nearby rural stations. The observations gathered from the weather stations indicate a

significant climatic change in terms of urban climate of the city through the time series

when compared with the spatial trends of the urban and the rural station (mainly for mean

minimum temperature). The satellite images are also utilized to develop a map of NDBI,

NDVI and LSE of Lahore, which in turn is utilized to estimate the land surface

temperature variations of Lahore. The relationship between NDVI and LST denotes high

negative correlation which reflects that the vegetation cover can significantly reduce the

influence of urban heat island, whereas the relationship between NDBI and LST indicates

high positive correlation suggesting that the urban built-up area strengthens the heat

influence on UHI. The massive scale of hot spots is observed over the densely populated,

and industrial areas where the urban heat islands are likely to develop, whereas the much

colder areas are observed over vegetation and water bodies. The integration of GIS and

satellite remote sensing techniques has proved to be efficient and effective for evaluating

and monitoring urban expansion and making assessments of its impact on land surface

temperature of Lahore.

III

ACKNOWLEDGMENT

I have the pearl of my eyes to admire blessings of the Compassionate and Omnipotent

because the words are less, knowledge is limited and time is short to express His Dignity It is one

of the infinite blessings of Allah that He bestowed me with potential and ability to

complete the present research. I offer my humblest gratitude from the hearts of my heart

to the Holy Prophet Hazrat Muhammad (Peace be upon Him), who is always a paragon

of guidance and knowledge, for humanity.

Foremost, I would like to extend my deepest sense of gratitude to my kind

supervisor Prof. Dr. Abdul Ghaffar, Chairman Department of Geography, University of

the Punjab, Lahore, Pakistan. My Research work is indebted to him for his enthusiastic

guidance, considerate attitude, ever-pouring inspiration and ushering supervision. I offer

full fathom feeling of sheer admiration to my honoured guide for his thankless help, wise

suggestions, patience, motivation, enthusiasm, and immense experience. His unceasing

guidance facilitated me throughout the research and compilation of this thesis. I could

not have imagined having a better advisor and mentor for my PhD study.

I would like to expresses my orisons and indebtedness to my affectionate parents,

who made my studies their prior interest. I am tongue-tied to express my love and

gratitude for my parents, whose prayers still prompt me to attain my objectives. I find it

hard to transcribe my gratitude and profound admiration to my mother (Sajjad Akhtar

Bhalli), I have always been stimulated and encouraged to move forward in delivering my

best by my affectionate father Dr. Muhammad Nawaz Sajjad Bhalli (Late). It is

being without him that I'll never get used to. “Long, long shall I rue thee.” I owe my

gratitude to my dearest sisters, and brothers, especially Dr. Muhammad Waseem Nawaz

Bhalli and Dr. Muhammad Zain-ul-Abbeedin Bhalli for their spiritual and intellectual

inspiration and support to carry out my nobler ideals of life. I can do no more than

reaffirm my timeless devotion to all members of my family.

Muhammad Nasar-u-Minallah

IV

DEDICATED

TO

My Affable Father

Dr. Muhammad Nawaz Sajjad Bhalli (Late)

A symbol of success for me!

Who always behaved like a friend

Whose love, valuable guidance and

Financial assistance enabled me to perceive and pursue higher goals in life

&

My Adorable Mother

The Embodiment of love and Kindness

Who enlightened my spirit of learning

In her lap

V

DECLARATION

The work reported in this thesis was carried out by me under the supervision of

Prof. Dr. Abdul Ghaffar, Chairman, Department of Geography, University of the Punjab,

Lahore, Pakistan.

I hereby declare that the title of thesis is “Assessing the Impact of Urban

Expansion on Land Surface Temperature in Lahore Using Remote Sensing

Techniques” and the contents of thesis are the product of my own research and no part

has been copied from any published source (except the references, standard mathematical

or genetic models /equations /formulas /protocols etc.). I further declare that this work has

not been submitted for award of any other degree/diploma. The University may take

action if the information provided is found inaccurate at any stage.

Signature of the Scholar

Muhammad Nasar u Minallah Registration No. : 2012-GEO-17

VI

LIST OF ABBREVIATIONS

ACGR………………………………………………….…Annual Compound Growth Rate

AOI…………………………………………………….…………………...Area of Interest

ASTER .................Advanced Space borne Thermal Emissions and Reflection Radiometer

ATLAS…………………………………...Advance Thermal and Land Application Sensor

AVHRR ..........................................................Advanced Very high Resolution Radiometer

CA…………………………….…………………………………………Cellular Automata

CBD………………………………………………………………Central Business District

CDGL…………………………………………………………...City District Govt. Lahore

CIR .................................................................................................................Color Infrared

DCR………………………………………………………………...District Census Report

DEM ...............................................................................................Digital Elevation Model

DN………………………………………………………………………….Digital Number

EPA ................................................…………………….Environmental Protection Agency

ESP…………………………………………………………..Economic Survey of Pakistan

ESRI…………………………………………...Environmental Systems Research Institute

ET………………………………………………………………………..Evpotranspiration

ETM+……………………………………………………..Enhance Thematic Mapper Plus

GCPs………………………………………………………………..Ground Control Points

GIS .......................................................................………..Geographic Information System

GLCF…………………………………………………………..Global Land Cover Facility

GOP………………………………………………………………………Govt. of Pakistan

GPS .............................................................................................Global Positioning System

IBI……………………………………………………………...Index based Built-up Index

IPCC…………………………………….…...Intergovernmental Panel on Climate Change

VII

ISA ...............................................................................................Impervious Surface Area

JICA……………………………..……………….Japan International Cooperation Agency

KIA…………………………………………………………….Kappa Index of Agreement

LANDSAT ....................................................……………...Land Remote Sensing Satellite

LDA……………………………………………………….Lahore Development Authority

LDCs…………………………………………………………...Least Developed Countries

LST ....................................................................……………….Land Surface Temperature

LULC .................................................................................................Land Use Land Cover

LULCC..................................................................................Land Use Land Cover Change

MAT……………………………………………………………Mean Annual Temperature

MMiT……………………………………………...Mean Monthly Minimum Temperature

MMxT………………………………………...…..Mean Monthly Maximum Temperature

MLA……………………………………………………..Maximum Likelihood Algorithm

MODIS ....................................................Moderate Resolution Imaging Spectroradiometer

MODTRAN .......................................................Moderate Resolution Transmission Model

MSS………………………………………………………………...Multi Spectral Scanner

MUHI .................................................................………………...Micro Urban Heat Island

NASA………………..……………………..National Aeronautics and Space Administration

NESPAK………………………………………….National Engineering Services Pakistan

NDVI ...................................................................Normalized Difference Vegetation Index

NDBI ........................................................................Normalized Difference Built-up Index

NDBeI .....................................................................Normalized Difference Bareness Index

NDSI ............................................................................Normalized Difference Snow Index

NDWI ..........................................................................Normalized Difference Water Index

NIPS……………………………………………......National Institute of Population Study

NIR…………………………………………………………………………...Near Infrared

VIII

NOAA .......................................National Oceanographic and Atmospheric Administration

OBIA………………………………………………………..Object Based Image Analysis

OLI………………………………………………….…………....Operational Land Imager

PCO…………………………………………………………Pakistan Census Organization

PDS………………………………………………………...Punjab Development Statistics

PMD……………………………………………………Pakistan Metrological Department

RBG………………………………………………………………………..Red Green Blue

RS…………………………………………………………………………Remote Sensing

SAARC……………………………….South Asian Association for Regional Corporation

SAVI……………………………………………………….Soil Adjusted vegetation Index

SD……………………………………………………………………….System Dynamics

SHI……………………………………………………………………..Surface Heat Island

SLC…………………………………………………………………….Scan line Corrector

SRS………………………………………………………………Satellite Remote Sensing

SUHI ...........................................................................................Surface Urban Heat Island

SWIR………………………………………………………………….Short Wave Infrared

TIRs………………………………………………………………Thermal Infrared Sensor

TMAs……………………………………………………Town Municipal Administrations

TM ............................................................................................................Thematic Mapper

TOA………………………………………………………………...Top of the Atmosphere

UCL .....................................................................................................Urban Canopy Layer

UES……………………………………………………………..Urban Expansion Scenario

UHI ...........................................................................................................Urban Heat Island

UHS……………………………………………………………………….Urban Heat Sink

ULU……………………………………………………………………….Urban Land Use

UN…………………………………………………………………………..United Nations

IX

UNFCCC………..……United Nations Framework of the Convention on Climate Change

UNEP……………………………………………United Nations Environment Programme

UNFPA…………………………………………………….United Nation Population Fund

UNO………………………………………………………….United Nations Organization

USCCSP…………………………………………….US Climate Change Science Program

USGS ................................................................................United States Geological Survey

VWI………………………………………………………Vegetation Water Content Index

WGS ................................................................................................World Geodetic System

X

TABLE OF CONTENTS

S. No…………………………………. Contents… …………………………………Page

Title……………………………………………………...…………………………………i

Abstract ……………………………………………………….…………………………..ii

Acknowledgements….........................................................................................................iii

Dedication ……………………………………………………….……………………….iv

Declaration …........................................................................................... ...........................v

List of Abbreviation…........................................................................................................vi

Table of Contents………………………………………………………...………………..x

List of Tables….................................................................................................................xvi

List of Figures…...............................................................................................................xix

CHAPTER 1: INTRODUCTION…………...………………….……...………………01

1.1. Introduction…………………………………………………………..…………..01

1.2. Background of the Study….…………………………………………..……...…..03

1.3. Statement of the Problem…………………….………………………….……….06

1.4. Hypothesis of the Research .…………………………………………..…………10

1.5. Aim of the Study ………………………………………………..…..…...……....10

1.6. Objectives of the Study………………………………..…………………………10

1.7. Research Questions……………………………………….…………………..….11

1.8. Study Area……………...………...………………………………………………11

1.8.1. Physiography………………..………………………………………………..13

1.8.1.1. Topography……………..……………………………………………14

1.8.2. Climatic Conditions…..………..………………………………......................14

XI

1.8.2.1. Temperature…………..………………………………………………14

1.8.2.2. Rainfall…………………………………………………………….....16

1.8.2.3. Humidity………………………………………………………….......16

1.8.2.4. Wind Speed and Direction…………………………………………...17

1.9. Significance of the Study.………………………………………………………..18

1.10. Thesis Organization………………………………………….…………………...20

CHAPTER 2: URBAN EXPANSION AND ITS IMPACT ON LAND SURFACE

TEMEPERATURE: A REVIEW……………………………………………...............23

2.1. Introduction………………………………………………………..……………..23

2.2. Urban Expansion and Land use Changes……………..………………………….24

2.3. Role of Remote Sensing in Assessing Urban Expansion…………….…………..27

2.4. Urban Climate Patterns and Land Surface Temperature……..…………………..31

2.5. Use of Remotely Sensed Data in Land Surface Temperature Estimation………..36

2.6. Relationship between NDVI, NDBI and LST……………………………………41

2.7. Urban Expansion and its Impact on Land Surface Temperature ………………...43

2.8. Urban Heat Island………………………………………………………………...47

CHAPTER 3: MATERIAL AND METHODS……..……………..………………......54

3.1. Introduction ……………………………………………………………..……….54

3.2. Data and its Sources……….……………………………………………...……...54

3.2.1. Primary Data…………………………………..…………………….......55

3.2.1.1. Satellite Images……………………………………………..........55

3.2.2. Secondary Data……………………………………………………...........57

3.2.2.1. In-Situ Atmospheric Temperature Data………….…..…………..58

3.2.2.2. Census Data………………………………..…………..................60

3.2.2.3. Land use Data…………………………………..…….……..........60

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3.3. Methodology………………………………………………..……………………61

3.3.1. Image Pre-Processing…………………………..………..……………….63

3.3.1.1. Geometric and Radiometric Correction …..……..………………64

3.3.1.2. Generating Subset Images…..……………………..……………..65

3.3.1.3. Image Enhancement……………………………….……………..66

3.3.1.4. Bands Combination for Visual Interpretation…………..………..66

3.3.2. Image Classification………………………...……………………………68

3.3.2.1. Supervised Classification………………………………………...69

3.3.2.2. Training Stage…………………………………...……………….72

3.3.2.3. Classification Stage………………………...…………………….73

3.3.3. Classification Accuracy Assessment………………………..…...……….74

3.3.4. Post-classification Change Detection………………………...…………..75

3.3.5. Urban Expansion Change Detection ……………………………..……...76

3.3.6. Methods of Retrieving Land Surface Temperature………..…...………...76

3.3.6.1. Brightness Temperature Retrieval………………………...……...78

3.3.6.2. Method of Derivation of NDVI………………..……..…………..80

3.3.6.3. Land Surface Emissivity (LSE)…..………………...…………….81

3.3.6.4. Land Surface Temperature Retrieval………………...…………..81

3.3.6.5. Thermal Map Generation………………………………………...82

3.3.7. Relationship between LST and Land use………………………………...82

3.3.8. Regression Analysis Determining the Relationship between NDVI,

NDBI and LST…………………………………………………………...83

3.3.9. Atmospheric Temperature Trends from 1951 to 2015…………………...85

3.3.10. Software used in Analysis……………………………………………......87

CHAPTER 4: URBAN EXPANSION OF LAHORE, 1951-2015…………................88

XIII

4.1. Introduction……………………………………………………………………....88

4.2. Population Growth of Lahore from 1901 to 2015……………………….....……90

4.2.1. Population Growth of Lahore from 1901-1941………......………………90

4.2.2. Population Growth of Lahore from 1951 to 2015…………...…………...92

4.2.3. Population Growth Rate of Lahore from 1951-2015…………………….94

4.2.4. Population Distribution and Density of Lahore……………..…………...95

4.2.5. Urban and Rural Population of Lahore……………...……….…………102

4.3. Urban Expansion of Lahore from 1951 to 2015……………………...…………105

4.3.1. Historical Expansion of Lahore: Pre-1947………………….…………..106

4.3.2. Urban Expansion of Lahore from 1951 to 1972 ( Pre-Satellite Era)……108

4.3.3. Land use Changes of Lahore from 1972-2015…………………..……...109

4.3.3.1. Nature, Rate and Extent of Land use Change…………...….116

4.3.4. Temporal Urban Expansion of Lahore from 1972-2015…………….….118

4.3.5. Annual Rate of Urban Expansion from 1972 to 2015………...………...121

4.3.6. Urban Expansion Intensity Index…………………………………….…122

4.3.7. Urban Change Detection of Lahore from 1973-2015……………….….123

4.4. Classification Accuracy Assessment……………….………………….………..126

CHAPTER 5: LAND SURFACE TEMPERATURE VARIATIONS….……..……130

5.1. Introduction……………………………………………………………..………130

5.2. Factors Increasing Land Surface Temperature ……………….……..……….....131

5.2.1. Rapid Growth of Population………….……...……………….…………132

5.2.2. Land use Changes………………………………………………….……134

5.2.2.1. Urban Built-up Area………………………………..……….135

5.2.2.2. Reduction in Agricultural Land………………………..…....138

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5.2.3. Increase in Registered Factories...……………...……………………….139

5.2.4. Increase in Registered Vehicles…………….………………….……….141

5.2.5. Increase in Greenhouse Gases…………………………..………..……..143

5.3. Comparison of Contributing Factors of Changing Temperature Trends……....146

5.3.1. Multiple Regression Analysis………………………………………………......147

5.4. Atmospheric Temperature Trends of Lahore from 1950 to 2015……..…..…....148

5.5. Land Surface Temperature Variations of Lahore, 1990-2015………………….150

5.6. Town wise Trends of LST of Lahore Between 1990 and 2015….………..........156

5.7. The Correlation between LST and Urban Land use Patterns………...…………158

5.8. Correlation between LST and Indices………………………………...………...161

5.8.1. Relationship of LST to NDVI………………………………...………...161

5.8.2. Relationship of LST to NDBI………………………………...………...166

5.9. Cross Validation of Satellite and Met Station Data…………………………….171

5.10. Urban Heat Island of Lahore………………………………………...….………172

6. SUMMARY, CONCLUSION AND RECOMMENDATIONS..……………….178

6.1. Summary ………………………………………………………………..……...178

6.2. Conclusion…………………………………………………………...…...……..184

6.3. Recommendations………………………………………………………………189

7. REFERENCES………………………………………………………..…………...192

APPENDICES................................................................................................................232

Appendix 1: Mean Annual Recorded Temperature (°C) 1950-2015...............................232

Appendix 2: Annual Trends of Ambient Air Quality of Lahore …………….….……...233

Appendix 3 Air Quality Parameters vs Population density and Temperature………….234

Appendix 4: Urban Population of Lahore, Punjab, Pakistan …………………………..234

Appendix 5: Number of Registered Vehicles of Lahore………...……………………...235

XV

Appendix 5: Number of Registered Factories in Lahore……………………………….235

Appendix 6: Published Research Paper…………………………………………………236

XVI

LIST OF TABLES

Table Title Page

Table 1.1: Urban Development of Lahore...................................................................08

Table 1.2: Mean Temperature (°C) & Precipitation of Lahore 1950-2015………….15

Table 2.1: Radiative Properties of Several Materials…………………………..........39

Table 2.2: Urban-Rural Contrasts………………………………………………........45

Table 3.1: Metadata of Landsat (5, 7 and 8) Satellite Images……………………….56

Table 3.2: Ground Weather Station of Lahore…………………………………...….59

Table 3.3: Data type used for the Study……………………………………………..61

Table 3.4: Description of Landsat Imagery Spectral Resolution…………………….64

Table 3.5: Band Combinations in RGB Comparisons….............................................67

Table 3.6: Description of the Land use Classification Scheme used in the Study…..71

Table 3.7: The Metadata of Landsat 8-TIR………………………………………….79

Table 3.8: Detail of Calibration Constant………………………………………........80

Table 3.9: Procedure of Applied Statistical Test……………………………….........86

Table 4.1: Population Growth rates and Inter-censual increase of Lahore………….91

Table 4.2: Rank of Lahore among the Major Cities of Pakistan since 1951………...92

Table 4.3: Population Increase in Lahore from 1951-2015….....................................92

Table 4.4: Population Growth Inter-censual increase in Lahore from 1951-2015…..95

Table 4.5: Tehsils of Lahore and Population in 1998………………..........................96

Table 4.6: Population distribution and Density of Lahore from1951-2014…………96

Table 4.7: Town wise Urban & Rural Union Councils of Lahore …………….........98

Table 4.8: Population Densities of Nine Towns of Lahore 1998…………………....99

XVII

Table 4.9: Population Densities of Nine Towns of Lahore (2010 Estimates)….…..100

Table 4.10: Population Densities of Nine Towns of Lahore (2015 Estimates)….......101

Table 4.11: Population of Lahore and its Constituent Administrative Units………..102

Table 4.12: Lahore Urban Population and ACGR 1951-1998 & 2015………….......103

Table 4.13: Urbanization (1951-1998) in Lahore and 2015 Estimated……………...104

Table 4.14: Area Statistics and Percentage of Land use of Lahore from 1973-2015..110

Table 4.15: Overall Amount, rate, nature and Extent of Land use change………….117

Table 4.16: Comparison of Built-up and Non Built-up Area of Lahore...…………..118

Table 4.17: The Urban Area, ARU and Increase urban area of Lahore……..............121

Table 4.18: Indices of urban temporal Expansion of Lahore………………..............122

Table 4.19: Overall classification Accuracy and Kappa (k) Statistics………………128

Table 4.20: User’s and Producer’s Accuracy for each Land use Type……………...128

Table 4.21: Conditional Kappa for each Category…………………………………..128

Table 5.1: Pearson Correlation between Population Growth and MAT……….…...134

Table 5.2: Land use Changes Patterns of Lahore since 1973 to 2015….…………..135

Table 5.3: Pearson Correction between Urban Built-up Area and MAT…………..138

Table 5.4: Pearson Correction between Reduction in Vegetation and MAT………139

Table 5.5: Pearson Correction analysis between MAT and Factories of Lahore…..140

Table 5.6: Person Correction between Registered Vehicles and MAT of Lahore....143

Table 5.7: Degree of Correlation of Different Drivers behind the changing

Temperature Trends…………………………………………………….146

Table 5.8: Multiple Regression Analysis…………………………………………..148

Table 5.9: Descriptive Statistics of Land Surface Temperature of Lahore…...……151

XVIII

Table 5.10: Land Surface Temperature Change from 1990 to 2015……………..….151

Table 5.11: Land Surface Temperature Variations with Different land uses…..........159

Table 5.12: Relationship between Vegetation Density and LST…………………….164

Table 5.13: Relationship between Built-up Area and LST………………………….169

Table 5.14: Cross Validation of LST with Lahore Urban MET Station Data……....171

Table 5.15: Cross Validation of LST with Lahore Rural MET Station Data……….171

Table 5.16: dTmin and dTmax over the period of 65 year at Lahore………………..173

Table 5.17: Regression result of Temperature of Lahore Urban station and Lahore

airport rural Station during 1950 to 2015……………………………….173

XIX

LIST OF FIGURES

Figure Title Page

Figure 1.1: Geographical Location of the Study Area (Lahore)….…………….…….12

Figure 1.2: Administrative Towns and Urban sub region of Lahore………...…….…13

Figure 1.3: Mean Monthly Temperature (oC) of Lahore from 1950 to 2015………...15

Figure 1.4: Mean Annual Atmospheric Temperature of Lahore from 1950 to 2015...16

Figure 1.5: Climograph of Lahore based on 65 years Climatic Data….......................17

Figure 1.6: Thesis Organization Flow Chart………………………………………….21

Figure 2.1: Landsat Mission’s Timeline and Their Current Status…………….……..30

Figure 2.2: The Albedo of different Land Surfaces…………………………..………38

Figure 2.3: Temperature profile of the Urban “Heat Island” shows the increase in

Temperature with increase Urbanization…...............................................48

Figure 2.4: Causes of Increase of Urban Temperature and UHI Formation………….49

Figure 2.5: Effect of Urban Heat Island Formation…………………………………..50

Figure 3.1: Imagery used for Urban Analysis ……….………………………...……..57

Figure 3.2: Meteorological Station in Lahore……………………………….………..59

Figure 3.3: Flow Diagram of Research Methodology………………………..………62

Figure 3.4: Spectrally Enhanced Subset Images showing Study Area…………...…..66

Figure 3.5: Flow Chart of Image Classification Process…..........................................69

Figure 3.6: Basic Steps in Supervised Classification…………………………………70

Figure 3.7: An example of Training Samples on an Image…......................................73

Figure 3.8: Flow Chart of Image Classification Accuracy Assessment Process……..74

Figure 3.9: Process of Land Surface Temperature Retrieval…....................................77

XX

Figure 3.10: Regression Analysis Flow Chart…………………………………………84

Figure 3.11: Random sample points for Relationship between LST and NDVI………84

Figure 4.1: Population Growth of Lahore from 1901 to 1941…..................................91

Figure 4.2: Inter-censual Increase and Growth Rates of Lahore 1911-1941………....91

Figure 4.3: Population Growth of Lahore from 1951 to 2015…..................................93

Figure 4.4: Growth rate and Inter-censual Increase from 1951-1998……………..….95

Figure 4.5: Population Density of Lahore from 1951-2015………………………….97

Figure 4.6: Town wise Population Distribution and Density of Lahore in 1998…….99

Figure 4.7: Town wise Population Distribution and Density of Lahore in 2010……100

Figure 4.8: Town wise Population Distribution and Density of Lahore in 2015……101

Figure 4.9: District, Urban and Rural Population of Lahore...……………………...104

Figure 4.10: Urbanization and Built-up area Tends of Lahore……………………….105

Figure 4.11: Urban Expansion of Lahore From 1850 to 2015.……………………….107

Figure 4.12: Urban Expansion of Lahore from 1947-1972…………………………..109

Figure 4.13: Land use Distribution of Lahore 1973……….…………………...…….111

Figure 4.14: Land use Distribution of Lahore 1980………….………………………112

Figure 4.15: Land use Distribution of Lahore 1990………………………………….113

Figure 4.16: Land use Distribution of Lahore 2000…………………………….........114



Figure 4.19: Nature of Relative Land use Changes of Lahore from 1973 to 2015…...118

Figure 4.20: Land use Comparison of Lahore from 1973 to 2015…………………...118

Figure 4.21: Comparison of Built-up and Non Built-up area of Lahore…………….119

XXI

Figure 4.22: Temporal Change in Urban Expansion of Lahore in 1973 and 2015…...119

Figure 4.23: Temporal Urban Expansion of Lahore from 1973 to 2015………..........120

Figure 4.24: Urban Change Detection of Lahore from 1973 to 2015………………...124

Figure 4.25: Spatial Expansion of Lahore from 1973 to 2015……………………….125

Figure 4.26: An Example for Test sample Taken on an Image……………………....127

Figure 5.1: Factors Increasing Land Surface Temperature………………...………..131

Figure 5.2: Population Growth of Lahore from 1951 to 2015………….…...............133

Figure 5.3: Correlation between Population growth and MAT of Lahore………….133

Figure 5.4: Land use Patterns of Lahore from 1973 to 2015………………………..136

Figure 5.5: Population and Urban Built-up area of Lahore from 1973 to 2015…….137

Figure 5.6: Correlation between Urban Built-up area and MAT of Lahore………...137

Figure 5.7: Correlation between reduction in Agricultural land and MAT of

Lahore…………………………………………………………………...139

Figure 5.8: Number of Registered Factories of Lahore from1990-2015……………140

Figure 5.9: Relationship between MAT and Registered Factories of Lahore...…….140

Figure 5.10: Number of Registered Vehicles of Lahore……………………………...141

Figure 5.11: Trends of Vehicles of Lahore from 1990 to 2015....................................141

Figure 5.12: Relationship between Vehicles and MAT of Lahore...…………………143

Figure 5.13: Greenhouse gases from 2008 to 2010 in Lahore……………………......145

Figure 5.14: Comparison showing all Contributing Factors of Temperature change...147

Figure 5.15: Atmospheric Temperature Variations of Lahore from 1950 to 2015.......149

Figure 5.16: Trend line showing future prediction of MAT of Lahore until 2030…...150

Figure 5.17: Land Surface Temperature Variations of Lahore in 1990………………152

XXII




Figure 5.21: Town Wise Comparison of LST of Lahore in 1990 and 2015………….157

Figure 5.22: Town wise Trends of LST of Lahore in 1990 and 2015..........................158

Figure 5.23: Land Surface Temperature Variations with Different Land uses..……...160

Figure 5.24: Spatial Distribution of LST and NDVI of Lahore in 1990……………...162




Figure 5.28: Relationship between NDVI and LST from 1990 to 2015……………...165

Figure 5.29: Spatial Distribution of LST and NDBI of Lahore in 1990……………...167




Figure 5.33: Relationship between NDBI and LST from 1990 to 2015……………...169

Figure 5.34: Comparison between LST with Lahore Urban-Rural MET Data………171

Figure 5.35: The Mean Maximum & Minimum Temperature Variations of Lahore at

Lahore Airport and Shadman Observatories…………………………...174

Figure 5.36: Urban and Rural Temperature Trends to represent long term UHI…….175

Figure 5.37: Presence of Urban Heat Island in 1990……………………....................176

Figure 5.38: Presence of Urban Heat Island in 2015……………...………………….176

1

CHAPTER 1: INTRODUCTION

1.1. Introduction

Urban expansion is highly important geographical phenomenon in today’s world

(Shekhar, 2007; Yesserie, 2009). Urbanized areas are stated to be the most dynamic

places on the earth surface in the world’s urban growth history (Yuan et al., 2005).

Aristotle asserts that the authority of the city state must be subjected to the human beings

in order to attain good life (Zhang, 2009). It was reported in 2008 that human civilization

achieved significant milestone which had no precedent as the half (3.3 billion people) of

the total population was residing in urban areas (UN, 2008). The world population in

2015 was estimated to be 7.3 billion out of which 4.4 billion (60 per cent) people were

inhabitants of Asia. It is estimated that by the end of 2016, 83 million more people would

become the part of global population (UN, 2015). Presently, the annual growth of the

global population is comparatively slower than the growth observed in the past. Current

annual growth of world population is 1.18 per cent while it was determined 1.24 per cent

almost a decade ago. At the present growth rate, 1.18 per cent, of the world population,

83 million people are added annually. It is projected that the global population in 2030

would be 8.5 billion which means the addition of 1 billion people and it would be 9.7

billion in 2050 and it would reach 11.2 billion by the end of 21st century (UN, 2015). All

over the world, most of the people prefer to live in cities as compared to country side, 54

% of the global population was urbanized in 2014. It is estimated that in 2050, 66% of the

global population is projected to be residing in urban areas. Rapid increasing of

urbanization and population trends project that a population of more 2.5 billion people

would be residing in the cities by 2050, with approximately 90% of increase concentrated

in Africa and Asia (UN, 2014).

The rapid expansion of urban areas is a recurring demographic phenomenon

shared by the developing countries in general (Tewlode, 2011), and Pakistan in particular.

The level of urbanization has increased significantly in developing countries owing to

rapid increase in urban population. In Pakistan, the percentage of people residing in cities

has increased from only 17.8 per cent in 1951 to about 32.5 per cent in 1998 and 39.2

per cent in 2015 (GoP, 2015). Within the Asia-Pacific region, based on both the urban

expansion and urbanization level, Pakistan is one of those countries which are undergoing

2

moderate level of urbanization. If compared with other SAARC countries, Pakistan has

the highest number of inhabitants hailing from urban areas as 39.2 percent of its

population lives in cities. The other countries of south Asia are far behind Pakistan in

terms of urban population and urbanization level. It is estimated that by 2030 about 50%

of Pakistan’s population would settle in cities (GoP, 2015). Pakistan is urbanizing at an

annual rate of 3 per cent, the fastest pace in South Asian countries (Kugelman, 2013).

According to UNFPA (2007), most of the urban areas are situated at the heart of the

fertile agricultural lands and the trends of the urban expansion focus on fringe areas.

Agarwal et al., (2002), reports that 6.8 million km2 of forests, grasslands and woodlands

have been transformed into urban land uses in the last three centuries on a global basis.

These land use changes have significant effects on the earth’s surface resources and

climate of the urban areas (Araya, 2009).

Atmospheric and land surface alterations due to urban land expansion change

thermal physical properties of urban areas that become warmer than their adjacent rural

areas (Van and Bao, 2010). The distinguished climatic condition termed ‘Urban Heat

Island’ (Brandsma et al., 2012; Kantzioura et al., 2012) is rapidly developing in

urbanized areas throughout the world (Kumar et al,. 2012). Land use changes due to

urban expansion always play a vital role in regional and local climatic conditions of the

monsoon countries (Coltri et al., 2009). One of the most important effects of land use

change is observed in the form of variation of land surface temperature in urban areas.

The major aspect of urban heat island phenomenon is its effects on the local climate and

resultant inconsistency in temperature in cities. Anthropogenic activities in urban areas

cause emission of the greenhouse gases which further intensify the urban heat and

contribute to enhance the spatial extent of urban heat island in metropolitan cities. The

observations of atmospheric processes in the urban areas are essential for the

comprehension of climatic changes, specifically at the local and regional level. The study

of land surface temperature variation is essential for the assessment of urban micro-

climate.

The Land Surface Temperature (LST) has become more and more significant

during the last five decades due to urban growth and increased consumption of energy in

cities of the world (EPA, 2008). As major human activities are concentrated in urban

areas, about 70 per cent of world energy is consumed in cities. The major anthropogenic

3

influences on urban climate are land use changes, such as development, industrial

activities and the emission of greenhouse gases. Land use changes (Agarwal et al., 2002;

Pielke et al., 2002) and increase in greenhouse gases (Houghton et al., 2001) are stated to

be the primary human impact of urban climate change which also contributes to land

surface warming. These two factors are inseparable as both contribute to rise in daily

mean temperature in urban areas (Gallo and Owen, 1999; Pielke et al., 2002). The

urbanization in Lahore has led to a massive increase of housing units, public places,

industrial activities and development of commercial areas, and deforestation, and it has

resulted in an increase in the overall land surface temperature of Lahore (Qureshi et al.,

2012). The significance of this study is to highlight the usage of satellite remote sensing

and GIS techniques in evaluating urban expansion of Lahore and to examine its impact on

land surface temperature.

1.2. Background of the Study

Urban expansion is ascribed to growth of urban population, increase in the

number of factories, industries and vehicles and they emit greenhouse gases that affect

climate change drastically ranging from land surface to atmosphere (Sun et al., 2010).

Urban expansion is the most significant human activity, producing massive impacts on

urban climate at the global, regional and local levels (Landsberg, 1981, Turner et al.,

1990) and it has become a formidable challenge to retain healthy environment while

attaining sustainable development (Fan et al., 2009). Rapid urban sprawl has changed

landscape with serious impact on urban climate as well as cultural, economic and social

setup of the societies. Spatio-temporal urban expansion patterns are crucial to

comprehend their effects on surface temperature. Urban expansion is determined by

socio-economic development and population growth of urban area (Liu et al., 2002;

Lopez, 2001; Wilson et al., 2003). Urban expansion has also led to environmental and

ecological problems such as increase in land surface temperature and major reduction in

vegetation cover (Ifatimehin and Ufuah, 2006).

Land surface temperature is a key geographic phenomenon to be examined for

thorough understanding of environmental and climatic changes taking place all over the

world. Land surface temperature is defined as temperature detected when the land surface

is touched with the fingers and surface temperature is the skin temperature of the land

4

surface (Rajeshwari and Mani, 2014). It is the temperature released by the surface of land

and measured in kelvin. It can be acquired from satellite images or direct measurement.

LST equips with accurate measurement to indicate the energy exchange balance between

the atmosphere and land surface (Zhengming and Dozier, 1989; Zhengming, 2007). It is

significantly affected by land use changes and increasing greenhouse gases in the air. All

the chemical, physical and biological processes taking place on the earth surface are

controlled by LST (Ruiliang et al., 2006). The study of LST is of pivotal significance to

study human-environment interaction and urban climate because of its spatial and

temporal resolution observed within environment of a city (Stathopoulou and Cartalis,

2009; Weng, 2009).

There is a general consensus that the urban climate can significantly be changed

due to urban expansion and anthropogenic activities. It is observed that the human

activities such as urbanization, agricultural systems, pollution, and deforestation have

accelerated severe climatic changes (Coltri et al., 2009). The most domineering problem

of increasing surface temperature is due to urban expansion alteration and conversion of

vegetated surfaces to impervious surfaces. These changes have great effects on the

absorption of solar radiation, land surface temperature, storage of heat, evaporation rates

and can radically alter the conditions of the near-surface atmosphere over the urban areas

(Mallick et al,. 2008).

One of the key modifications prompted by the urban land expansion is vegetation

cover altered into the asphalted and concrete constructions on a large scale. Subsequently,

the thermal characteristics of the urban area and impervious surface undergo a change.

(Srivanit, 2012). The consumption of heat absorbing materials used in construction (e.g.,

asphalt, stone, concrete and metal) and construction of pavements, roads, footpaths,

terraces and parking lots in cities and the consistent decrease in the area of natural

vegetation, agricultural land and water bodies produce higher temperature in cities which

apparently is a localized phenomenon but it contributes to global heat as well (Joshi and

Bhatt, 2012). The increased urban temperature leaves adverse impacts on climate, like

modification of precipitation patterns, higher pollution level and manifold consumption of

energy for air conditioning. Urban Heat Island (UHI) effects are of prime concern

regarding the studies of urban climatology for magnitude and variation of land surface

temperature in urban areas (Srivanit, 2012).

5

It is stated in the fifth assessment report of The Intergovernmental Panel on

Climate Change (IPCC) that cities are principal contributors in raising the global average

temperature and climatic change. It has also been demonstrated by the estimates in the

related studies by IPCC that the global climatic change in the next decade will be

principally due to human dwellings, use of fossil fuel and its combustion (Houghton et

al., 1996; 2001). Assessment report by IPCC states an average increase in temperature by

0.6°C in the last century. The increase is further projected to be of 1.4ºC to 5.8ºC by the

end of 21st century (Parry et al., 2007). The IPCC report for the year 2007 indicates

increase in methane, carbon dioxide and nitrous oxide, mainly caused by industrial and

anthropogenic activities (Parry et al., 2007). The urban heat island effect is contributed by

the anthropogenic activities, including human demand of goods, increasing industrial

activities, use of vehicles, process of proving heating and cooling systems in domestic use

and activities pertaining to economic gains (EPA, 2003).

The temperature of cities all over the world is gradually rising due to urban

expansion (Jusuf et al., 2007). The drastic reduction in the vegetated areas in the cities is

one of the possible causes (Kumar et al., 2012). Land surface temperatures in cities have

long been area of research with a function of land use change, anthropogenic activities

and urban morphology, as core meteorological parameters (Mohan et al., 2013). The

Spatio-temporal change of land surface temperature is the most prominent existing issue

of apprehension in urban areas of both developing and developed countries across the

globe (Mohan et al., 2012). Cities like, London (Authority, 2006), Tokyo (Ooka, 2007)

and New York (Cox, 2011) have long been subjected to climate change and altered by

phenomenon of urban heat island. However, with increasing industrial activities and

urban development, urban heat island pockets are also being identified in all populated

areas as well as developing cities like Ethiopia (Kifle, 2003), Mexico (Garcia et al.,

2007), Nigeria (Akinbode et al., 2008), Malaysia (Takeuchi et al., 2010), Oman (Charabi

and Bakhit, 2011), Argentina (Camilloni and Barrucand, 2012), and others. In Pakistan,

Spatio-temporal changes of surface temperature in big cities like Karachi, Lahore,

Faisalabad, Rawalpindi, Multan, Gujranwala and Islamabad, are being examined in the

last few years (Qureshi et al., 2012; Afsar et al., 2013).

The utilization of GIS and satellite remote sensing has been demonstrated as an

effective technique of making assessment of urban expansion, its location, and rate of

6

expansion, trend, amount and possible effects on environment such as urban climate

(Weng, 2001). As satellite remote sensing is a quick means for acquisition of data

encompassing a large area, it is a useful technology for gathering observations regarding

urban expansion and mapping distribution patterns of surface temperature (Lopez, 2001).

Remote sensing techniques provided an opportunity to estimate temperature from the

thermal band of infrared Landsat images (Sun et al., 2004). The range of infrared heat can

be detected through sensors equipped in satellites. Estimation of land surface temperature

from satellite infrared radiometers has been beneficial (Prasad et al., 2013). The

techniques of RS are extensively applied for identifying land surface temperature and

observing urban expansion at numerous scales with valuable results (Weng, 2002). This

study is to monitor the urban expansion of Lahore and to examine its impact on land

surface temperature.

1.3. Statement of the Problem

Urban expansion, both in terms of population size and land use changes, areal

extent, and anthropogenic activities, is the most definite outcome of human alteration of

vegetation areas to built-up surfaces in urban areas (Weng, 2001; Xiao and Weng, 2007).

This change contributes significant effects on local climate and urban temperature

(Landsberg, 1981). One of the familiar phenomena of urban heat island exhibited is that

the temperature in cities is a few degrees higher than the nearby country side. The

presence of UHI raises temperature in cities as compared to their surroundings of country

side (Voogt and Oke, 2003). Agricultural lands and forests are converted into urbanized

structures including roads, pavements, parking lots, buildings, and other impervious

structure. They usually have a greater thermal conductivity and higher solar radiation

absorption capacity in urban areas. The heat during day time is stored and released at

night (Weng, 2001). Therefore, urban areas experience a comparatively higher surface

temperature than rural suburbs. This thermal difference, along with the transportation,

industry, heat emitted by the urban communities contributes to the accumulation of heat

island and increasing land surface temperature in urban areas.

Lahore, has experienced remarkable growth, expansion and developmental

activities e.g. buildings, road networks, deforestation, increasing economic activities, the

formation of strong administrative establishment and accumulation of social services i.e.

7

education, health, cultural and recreational, numerous anthropogenic activities and they

have serious repercussions on the urban climate of the city of Lahore. Since independence

(1947), the enlargement of the built-up area of Lahore has been remarkable and is still

gaining momentum, especially in the last two decades, it has been noted to be expanded

to a significant extent as shown in Table1.1 (JICA, 2011). The spatial growth of Lahore

has been mostly contributed by the accretion of a few developed planned areas, but most

of it has been a subject to haphazard developed parcels of urban land. The overall urban

expansion resulted in unprecedented urban growth and enlargement of the city

boundaries. The previous residential areas are renewed in commercial centers while the

suburban population is continuously moving towards outer skirts because of the

expansion of city (Riaz, 2011). The jurisdictional limit of Lahore Development Authority

is extending continuously, resulting in spatial friction, traffic congestion, accidents,

pollution, increasing land surface temperature and many more problems. The urban

climate of Lahore is affected by the rapid growth of urban expansion.

Since the 1950s, urban development pace has been accelerated because of greater

accumulation of urban population and economy. The urban expansion of Lahore has been

steady since 1951 to 1981, but it has expanded enormously without any limits since

1980s. The major growth started around late 1960’s when the population growth rate was

very high (JICA, 2011). This high growth leads to the urban expansion in the south and

south-west corridors of Ferozepur Road and Multan Road, again mostly unplanned

suburbs, with the exception of rich areas like Model Town, Gulberg and Shadman. In the

east of the city, urbanization has been limited due to proximity of India, and was seriously

affected after the 1965 war. Similarly, westward expansion has been restrained due to the

Ravi River. During the period between 1970s and 1980s, urban population exceeded 02

million, forming Lahore into a metropolis (JICA, 2011). Urbanization in Lahore started to

boost up around 1980s.

The massive urbanization in Lahore through housing colonies, commercial and

industrial regions, infrastructure, transportation projects and rapid urban growth of

population has created impact on urban environment and climate, with considerable

effects on land surface temperature (NESPAK, 2010). According to 1998 Census of

district Lahore, population and built-up area of Lahore increased. The administrative

boundary was revised in 2005 with respect to population and built-up areas (GoP, 2000).

8

Several new housing schemes and commercial areas were also approved by Lahore

Development Authority during the period of 2000 to 2015. It is estimated that almost

12.7% of total urban population of the country is residing in the city of Lahore, while the

city of Karachi contributes 21.7% (Jan et al., 2008). In the province of the Punjab, 22% of

the urban population resides in Lahore, and half of the total provincial urban population

lives in five big cities (Arif and Hamid, 2007).

Table 1.1: Urban Development of Lahore

Description Urban Built-up Area (km2

) Average Growth Area Per

Year in km2

Pre-British Rule 1.105 -

British Period 23.8 0.45

1850 – 1900 68.7 0.89

1901 – 1950 71.2 0.48

1951 – 1965 117.2 3.23

1966 – 1980 175.7 3.90

1981 – 1990 245.6 6.98

1991 - 2000 326.0 8.04

2001 - 2006 397.8 11.96

Source: JICA, 2011

According to the PCO, the population of Lahore in 1998 was 6.3 million (GoP

1998) and 80 per cent of it dwelled within a diameter of 72 km. Most of the population is

concentrated around and within the heart of the city and then a gradual diffusion in a Peri

urban areas is observed with overall average density of 120 people per acre (NESPAK,

1997). In 2012, it was estimated to nine million (GoP, 2012). In 2015, it was estimated to

increase to 9.5 million (GoP, 2015). The growth rate of Lahore is alarming as its

population density has multiplied from 640 to 5386 persons residing in one square

kilometer from 1951 to 2015 respectively. 82.2 per cent population is urbanized and 17.8

per cent is rural (Almas et al., 2005; GoP, 2015).

The land use changes, rapid growth of population, lack of well managed public

transport, increasing number of private automobiles and facilities of life adversely affect

the climatic condition of Lahore. The main factors responsible for the increase of the land

surface temperature in Lahore are industrial activities, development of the commercial

areas, construction of road infrastructure, housing schemes deforestation and combustion

9

from the vehicles and increase of greenhouse gases (Qureshi et al., 2012). Owing to

expansion in built-up area and rapid urbanization, the temperature of Lahore has

significantly increased from 1950 to 2015 (Figure 1.3 and 1.4).

In this research, satellite remote sensing techniques are applied to evaluate urban

expansion and its influence on the land surface temperature of Lahore. It is examined to

see the loss of agricultural land and vegetal cover, expansion of urban land and land use

land cover changes along with role of industrial development in the sprawl of Lahore and

its effects on urban temperature. Various studies have demonstrated the effects of land

use land cover changes in different regions in the world on LST (Keramitsoglou et al.,

2011), the present study figures this relationship in the city of Lahore, Pakistan.

Identifying Spatio temporal correlation between changes in land use and estimation of

land surface temperature can be beneficial in predicting future land warming. The present

research is an attempt to provide scientific information to urban planners, geographers,

resource managers and environmental experts to maintain natural landscapes and manage

sustainable and healthy environment (Zhou et al., 2011).

In the present research, various remote sensing techniques such as supervised

images classification and indices such as Normalized Difference Built-up Index (NDBI),

and Normalized Difference Vegetation Index (NDVI) (Chen et al., 2004; Hawkins et al.,

2004; Wang et al., 2004) would be used to extract land use change from satellite images

for the period from 1973 to 2015 and evaluated and the land surface temperature of

Lahore would be analyzed and retrieved from the thermal infrared band of Landsat

satellite images (Yuan and Bauer, 2005) for the period from 1990 to 2015. Landsat data is

used for mapping of land surface temperature variation and impact assessment of urban

expansion using suitable algorithm. To estimate the thermal condition of land surface

through satellite imagery, it is pertinent to work out the relation between the land

surface temperature and the urban expansion (Hawkins et al., 2004). The Normalized

Difference Built-up Index and Normalized Difference Vegetation Index (Myneni et al.,

2001; Chen et al., 2006) have been applied to investigate the correlation between thermal

behavior and impervious structure and amount of vegetation cover. This study is an

attempt to evaluate the expansion of urban land and estimate its impact on land surface

temperature of Lahore with the help of a time series of satellite images by using remote

sensing techniques.

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1.4. Hypothesis of the Research

The hypothesis of this research i.e.; in Lahore, “due to Land use changes and

consequent urban expansion, the land surface temperature has significantly been altered

during the last three decades” which has caused a number of environmental problems in

the study area.

1.5. Aim of the Study

The present study aims at utilizing both readings of temperature from satellite

based LST and meteorological measurements to assess the effects of urban expansion on

land surface temperature changes over Lahore applying GIS and RS techniques. In situ,

observations at fixed stations provide better temporal resolution of data, while satellite

observations can provide better spatial coverage (Mohan et al., 2013). In order to develop

a deeper understanding of urban heat island phenomenon and land surface temperature ,

observations recorded by MET station and satellite imagery have been compared by the

utilization of both fixed station (Urban and Rural) temperature data as well as satellite

thermal infrared data can supplement the weaknesses and strengths of each other. Surface

temperature derived from thermal imagery has been noted to have better accuracy and

precision with lower bias and relatively stronger correlation when compared to in-situ

temperature observations, therefore, thermal imagery has been utilized to retrieve the land

surface temperature (Hung et al., 2006; Fung et al., 2009). The incorporation of two

techniques namely remote sensing and GIS is observed to be efficient and reliable in

monitoring, analyzing and evaluating urban expansion and its impact on land surface


1.6. Objectives of the Study

In order to test and explore the above-mentioned hypothesis, following research

objectives are sought: The explicit objectives of the proposed study are enumerated below

1. To highlight urban expansion process of Lahore in a specific time period and

analysis of spatial and temporal changes taking place through sequential mapping.

o To monitor the urban expansion during the period under study i.e.; 1972 to

2015 using Landsat satellite images.

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o To monitor and analyze the dynamic change of urban land use in Lahore

from 1972-2015

2. To evaluate temporal change in the land surface temperature using satellite

thermal imagery from 1990 to 2015

3. To study the land surface temperature variations of different types of land use

from 1990 to 2015

4. To identify temporal changes in atmospheric temperature trends of Lahore using

meteorological data from 1950 to 2015

5. To explore the relationship between land surface temperature and NDVI, NDBI

using satellite imagery from 1990 to 2015

1.7. Research Questions

The study pays attention to answer the subsequent research questions

i. What urban expansion has occurred in Lahore from 1972 to 2015?

ii. Does urban expansion have any effects on land surface temperature of Lahore?

iii. What are the changes between land use type (e.g. built-up area and vegetation)

and land surface temperature?

1.8. Study Area

Lahore commands geostrategic, geopolitical and administrative role as the Capital

of Punjab province, Pakistan and the 2nd biggest City of Pakistan in terms of population

of about 10 million (JICA, 2011). It is located at the north-eastern part of Pakistan with its

Centre lying with 25kms of the International Border with India (Figure 1.1) (NESPAK,

2004). Lahore boasts a history of nearly 1000 years (GoP, 2000). The city of Lahore,

being one of the ancient cities of Pakistan and provincial capital, operates administrative

functions regarding health, education, culture and transportation along with its urban

hierarchy in trade and industry, stands 2nd in commerce (Riaz, 2011).

Lahore lies to the western side of the River Ravi at level flood plain. Sloping

towards the active course down slop north to south. Lahore lies between 74°-01’ and 74°-

39’ east longitude, and 31°-15’ and 31°-42’ north latitude (Figure 1.1). Lahore is ranked

12

fifth in South Asia and thirtieth in the world. It is also the 2nd largest as well as 2nd most

populous city of Pakistan. It has grown the historic route linking central Asia with sub-

continent (NESPAK, 2004). Lahore is bounded by Sheikupura district in the west and by

Wagha on the east, while on south it is surrounded by the Kasur district and on the North

side it is bordered by Ravi River (JICA, 2011).

Figure 1.1: Geographical Location of the Study Area (Lahore)

Minallah, 2016 (Edited)

The administrative structure has been a subject to change after declaration of

Local Government Ordinance in 2001. Administrative bodies for Districts, City Districts,

Towns/Tehsil and Union Councils have been created. Lahore was declared as City

District and was further divided into six towns. In 2005, six towns were split into nine

towns in City District Govt. Lahore. Now, Lahore City District comprises following nine

towns (Fig. 1.2) which are administrated by Town Municipal Administration (TMA). The

Lahore cantonment is distinctly governed by cantonment board and the provision of core

facilities is the sole responsibility of Lahore Cantonment Board (JICA, 2011). Today, the

area of the Lahore is spread over 1,772 km2.

13

Figure 1.2: Administrative Towns and Urban sub regions of Lahore


1.8.1. Physiography

The city of Lahore can be divided into two parts; low lying alluvial soil and

upland area. The low lying area is found along the River Ravi, while the upland area is

located in the east, away from the River Ravi, occupying the total area touching the

Amristar border at the east of the district. The low lying areas are generally flooded

during the monsoon season by the water of the river Ravi. The flow of the river Ravi is

directed along the boundary of Lahore district with Sheikhupura district. The urban

development and the construction of infrastructure destroyed or changed the

physiographic features of Lahore, including levees and channel remnants etc. The

confinements of the flood plains along the river Ravi have been constructed by

embankments (bunds) and spurs. Moreover, the sewage drains have replaced the

Meandering channels. The area subjected to the sub-recent flood plain is measured to be 4

to 8 meters as compared to the areas subjected to the recent flood plain like places

including Mughalpura, Shalimar Garden and Multan road (IMPL, 2004). The plain slope

of the upland is from north-east to south-east. The low land lying to the south is termed as

Hithar. The soil available in the area of low lying is easy to be filled, rather sandy in

terms of fertility.

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1.8.1.1. Topography

Lahore is located naturally having no mountain or hills between the central

uplands and low lying alluvial land of the river Ravi. The alluvial land of the district

Lahore can further be divided into 1) Uttar and 2) Hithar. Uttar comprises the almost 2/3

of entire land in the north. On the other hand, the low lying area of Hithar is generally

flooded during the monsoon season by the high flow in the river Ravi. The height above

sea level of the area is calculated to be 150 to 200 meters.

The soil of Lahore is analyzed to be different in character and prone to be dry.

However, the soil is fertile enough for the plant nutrients. Irrigation in the area is

dependent on the water supply from canals as rainfall is precarious; water level in the

wells is lower and quality is saline. The level of the water where it is suitable for

irrigating purposes is lower for the tube wells. There is also a variation in the depth wise

chemical composition of the elements in water of different areas of Lahore. The suitable

potable water for drinking purposes is found in the surrounding areas covering a belt of 5

miles to 20 miles in the vicinity of the river Ravi. In the Hithar areas, the supply of water

for irrigation is non-perennial and the deficiency is made up by installation of tube wells.

The soil is alluvial and soft, while in some areas, loam is yielding but also sandy and

fertile.

1.8.2. Climatic Conditions

1.8.2.1. Temperature

Lahore city has extreme climate. According to Koppen Classification System,

Lahore experiences semi-arid climate with hot and rainy summers and mild winters. It

experiences four seasons which include summer (June-Aug) with dust rain storms, heat

wave days, Rainy Monsoon and dry but pleasant Autumn (Sep-November), winters (Dec-

Feb) with few western disturbances causing rains and hot dry spring (March-May)

(Heiden, 2011).

The summer season sets in April and continues till September. The hottest months

are May, June, and July. The mean minimum and maximum temperatures of Lahore

during these months vary between 27.3°C and 40.4°C as shown in Figure 1.3 (NESPAK,

2004). From mid of July to September, Monsoon rains become a cause of comparatively

pleasant weather. The winter season starts from November to March. Extreme cold

15

weather is experienced from December to February with minimum temperature almost

reaching down up to the freezing point. The mean minimum and maximum temperatures

during these months varies between 5.9°C and 22°C respectively (NESPAK, 2004). The

minimum temperature of Lahore falls below zero degrees Celsius. Sometimes, maximum

temperature reaches around 50 Degrees Celsius (JICA, 2011; PMD, 2015).

Table. 1.2: Mean Temperature (°C) and Precipitation of Lahore 1950-2015

Month Mean Temperature (°C) Precipitation

(Millimeters)

Relative

Humidity (%) Minimum Maximum MAT

January 6.5 19.2 12.9 23.0 64.6

February 9.5 22.2 15.9 28.5 57.6

March 14.6 27.4 21 41.2 51.1

April 19.9 34.1 27 19.7 37.9

May 24.4 38.9 31.7 22.4 31.9

June 27.4 40.1 33.8 36.3 39.8

July 27.0 36.8 31.9 202.1 63.3

August 26.6 34.9 30.8 163.9 68.8

September 24.7 34.8 29.8 61.1 59.6

October 18.9 32.7 25.8 12.4 53.2

November 12.1 27.3 19.7 4.2 61.4

December 8.4 21.6 15 18.9 67.8

Annual 18.3 30.8 24.6 628.7 54.7

Source: Pakistan Meteorology Department Lahore, 2015

Figure 1.3: Mean Monthly Temperature (°C) of Lahore from 1950 to 2015


0

5

10

15

20

25

30

35

40

45

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Tem

pera

ture

(°C

)

Month

MXT MNT MAT

16

Figure 1.4: Mean Annual Atmospheric Temperature of Lahore from 1950 to 2015


1.8.2.2. Rainfall

The city has light to moderate rain fall during January and February, which is

succeeded by a spell of pleasant spring weather. In April temperature starts rising and two

succeeding months are quite hot. Dust storms occur occasionally during this season

relieving temporarily the intensity of the heat. Towards the end of June, monsoon starts

and during the following two and a half months, spell of rainy weather alternates with

intervals of sultry weather (GoP, 2004). Lahore reports maximum rain during the month

of July (Figure 1.5). Maximum rainfall in a single day in Lahore was recorded to be 8.7

inch (221 millimeters) on August 13, 2008 (PMD, 2010). A Table 1.2 displays monthly

average precipitation and mean minimum and maximum temperature recorded at Lahore.

1.8.2.3. Humidity

Relative humidity in the winter is higher as compared to the summer season. May

and June are dry and very hot accompanying dust storms. Monsoon season sets in the end

of the June or at the beginning of July, characterized by humid sultry weather and heavy

downpour. July, August and September are practically suffocated in the year. The

variation in the relative humidity throughout the month is shown in the Table 1.2.

y = 0.0128x - 0.821

R² = 0.185923

23.5

24

24.5

25

25.5

26

1950 1960 1970 1980 1990 2000 2010 2020

Tem

para

ture

An

om

aly

(°C

)

Year

Lahore Temperature Linear (Lahore Temperature )

17

Figure 1.5: Climograph of Lahore based on 65 Years Climatic Data

Source: Pakistan Meteorology Department, Lahore, 2016

1.8.2.4. Wind Speed and Direction

Winter season in Lahore has minimum wind storm and is mostly calm. During the

months of April to July, wind storms are common. The maximum occurrence is recorded

in June when high temperature develops low air pressure. In winter, the direction of the

wind flow is from north-west, while in summer, it is directed from the south-east which

18

causes monsoon rains. The predominant direction of the wind in Lahore is north-west,

while it is south-west in the monsoon season.

1.9. Significance of the Study

This research aims to empirically analyze urban development and its consequent

impact on temporal land surface temperature of Lahore since 1951. Land use patterns of

Lahore have changed tremendously due to urban population growth and boost in

industrialization and commercial activities from 1951 to 2015. Industrial development

along with rapid urban expansion has caused increase in built-up area, loss of agricultural

land and deforestation resulting in change in overall environmental conditions and

increase in the land surface temperature of Lahore. Urban areas are the signs of

development and major agglomeration of human resource evolution. It is pertinent to

point out that no geographical study has ever been carried out in the context of evaluation

of urban expansion and its consequent impact on the urban climate of Lahore. Urban

expansion, land use changes and its effects on land surface temperature are examined

through remote sensing procedures.

Scholars such as Weng, (2001), Xiao et al., (2006), Zemba et al., (2010), Xu &

Min, (2013), Stemn, (2014) and Yang et al., (2014) have already focused on the remote

sensing application in urban analysis to find out the spatial relationship between the urban

expansion and LST within the limits of the cities. Therefore, the proposed study is very

significant in this regard as population growth and built-up area of Lahore have increased

manifold since 1951. This study identifies the population pressure on different areas of

Lahore to work out a relationship between population and urban growth and temperature.

The research related to urban expansion will be productive in improving the urban

dwellers’ standard of living, making sure the civic amenities and meeting the needs of

basic services and housing infrastructures for them. The facts of the phenomenal urban

sprawl and the patterns of land use can be beneficial for the decision makers in terms of

policy making and urban planning for the betterment of inhabitants of the city. The facts

revealed in the study are helpful in making settlement plans before the commencement of

the mega projects in Lahore. It will cope with the difficulties of urban developments.

The increase in temperature in the present millennium is inviting researchers to

conduct studies in this regard. LST gives vital information related to surface physical

19

properties and the urban climate that is crucial to environmental processes. It also

supplements to recognize how and to what extent the factors are contributing to the

climate change of an urban area. Furthermore, comparisons can be made after the analysis

of temperature rising rates with the temperature of the other areas to investigate natural

and anthropogenic activities in the region. The fluctuation in the temperature and

information about the changes are required to foresee potential effects of such changes on

water resources, energy, health, agriculture and industrial sector. LST statistics could

facilitate urban planners particularly for better understanding of urban climate along with

seeking solution for the management of urban environment. The results acquired through

present research suggest that land use changes have crucial effects on the indigenous

climate of Lahore, which can be helpful in prediction of land use and regional climate

change patterns in future. Remote sensing data are of a great value in terms of calculating

LST in this research as this method extends the skills to collect data over a vast area at the

one and same time. In urban area comprising hundreds of Km2, it might not be possible to

obtain land surface temperature measurements especially across the whole area in a great

number at the same time in a ground survey. If such a survey would have been regulated,

it would further demand the extensions of it to a large area. Satellite images capacitate to

obtain a large area coverage simultaneously.

The results and findings of this research can be generalized for other cities of

Pakistan. The other benefit of this research will be that it can be used as a reference

research document. All the basic information is presented in tables, charts, figures and

maps. All the maps are prepared in GIS environment with the rapid urban growth and

population explosion in Lahore. The research findings are to be considered by the city

managers to develop the city systematically; only up-to-date land surface temperature

information of town planning can make our research area healthier, more habitable and to

certain extent even a great industrialized city. This study offers a deeper insight of the

issues confronted by urban dwellers because of population growth. This study may be a

valuable addition for the planners and researchers at the same time for forecasting and

choosing between numerous substitutes. At technological, theoretical and application

level, this study is highly beneficial. The significance of GIS, satellite remote sensing and

image processing techniques in detection of land use change and urban studies

emphasizes the technological trait of the research while theoretical level of this study

exhibits the development and growth of urban area and population and detects changes in

20

urban land use. It is facilitating us with an intellectual approach towards the issue of

urban expansion and this approach can be beneficial for the future researchers. At

application level, the transformation of agricultural land into urban built-up area and land

surface temperature variations have been identified. This study examined the land surface

temperature variations and spatial expansion of urban physical structure of Lahore which

is an additional perspective of the theoretical framework of urban studies.

The same issues relating to the urban expansion and necessities are being faced by

the other major and big cities of Pakistan. The similar methodology can be opted to probe

into the problems of urban expansion in the other major cities of Pakistan in order to

detect the intensity of urban expansion and the urban land use changes and its impact on

land surface temperature. The procedure adopted in the present study can be utilized as a

substitute to the traditional and conventional empirical observation in-situ data used for

climatic change and environmental studies. The conclusion arrived through the

application of GIS and remote sensing imageries has long been proved beneficial for

urban expansion monitoring, land use change analysis and mapping land surface

temperatures.

1.10. Thesis Organization

The organization of the research work is divided into six chapters. The first

chapter provides the introduction to research, background and statement of the problem. It

gives the detailed hypothesis, aim and objectives of the study and research questions of

the present study. It also represents the description of the study area dealing with the

physical nature and climatic conditions of the research area. It also provides the

significance of the study and thesis organization. The second chapter deals with the

review of literature including urban expansion and related land use changes, use of

remote sensing for assessing urban expansion and urban climate pattern. It also describes

the utilization of remote sensing data to compute radiant surface temperature, and also

provides literature on the relationship between the LST and NDBI, NDVI.

The third chapter is categorized into data sources and methodology. A brief

description of the sources and data acquisition techniques and quality of data has been

presented in third chapter data source section. The methodology section delineates the

conceptual framework as well as techniques and procedure followed during the present

research. The methodology section of the third chapter also provides detail about the GIS

21

and remote sensing techniques in monitoring and analyzing Spatio-temporal urban

expansion of the city of Lahore and estimate of the LST.

Figure 1.6: Thesis Organization Flow Chart

Minallah, 2016

The chapter four of this thesis includes the analysis of the population growth of

Lahore from 1951 to 2015. It describes the historical expansion of Lahore. It also narrates

the Spatio-temporal urban expansion of Lahore from 1951 to 2015 using a time series of

Landsat imagery from 1973 to 2015. It highlights the land use changes associated with

urban expansion occurred from 1951 to 2015. Chapter five of the thesis deals with the

remote sensing techniques to identify the land surface temperature variations of Lahore

22

and impact of urban expansion. The basic steps to be followed are derived from the

estimation of land surface temperature by using satellite thermal images. The chapter five

also describes the comparison based on the results derived from Landsat images of

surface temperature and Spatio-temporal urban expansion of Lahore. In the end, the last

chapter 6 of the thesis provides a summary and concludes the study with recommendation

drawn from the analysis.

23

CHAPTER 2: URBAN EXPANSION AND ITS IMPACT ON

LAND SURFACE TEMPERATURE: A REVIEW

2.1. Introduction

The present study aims at evaluation of the pragmatic works pertaining to the

urban expansion with reference to land surface temperature using satellite remote sensing

techniques. Review of the previous literature and work done in the field provides a

guideline in designing the scientific studies through the identification of the broken links

and gaps in previous studies and provides basis for innovative research. Gradually but

steadily, industrialization, economic development and rural to urban migration and

contributing factors have shaped urban centers and cities anew by expanding their

original administrative limits across the world. Subsequently, the result of urban

population growth is overcrowded cities and loss of agricultural land adjoining the city.

The shift of rural area into urban built-up ensuing from population increase and economic

development taking place is alarming presently. Lately the urban areas cover only almost

3% surface of the earth and effects of urban expansion on the environment processes are

long-lasting on the global scale along with climate change (Grimm et al., 2000; Griffiths

et al. 2010).

Urban expansion tends to bring surface modification, land use change and the

structure and components of the atmosphere. These modifications yield results and show

diverse micro and meso scale urban climates which are unusually warmer unlike the

original climate of the country side. For instance, the cities have higher temperatures and

less strong wind than that of country side. The concentration of people and anthropogenic

activities in cities produces an “island” of higher temperature surrounded by a “sea” of

cooler country side, the urban and rural areas temperature differences are typically named

as the urban heat island phenomenon (Oke, 1992). LST is an important parameter for

recognizing urban micro-climatic changes and their Spatio-temporal variations related to

the urban environment.

The relationship between urbanized areas and their atmospheric and land surface

temperature has been demonstrated in invoking interest in geographers and environmental

scientists as well as in the government within academia. Geographers are primarily

24

concerned with impacts of anthropogenic activities on urban environment (Viterito,

1991), while many environmental scientists are concerned with how the UHI

Phenomenon exercises effects on the state of environment and how is itself affected by it

(Gartland, 2008). The review of literature in this connection is as follows.

2.2. Urban Expansion and Land use Changes

Urban geography deals with the evolution, location and spatial morphological

arrangement of cities. It emphasizes upon the study of cities and towns in terms of

population concentration, infrastructure and their impact upon economy, environment and

urban climate. Urban geography is a branch of Human geography that is a branch of

geography which shapes spatial distribution of human phenomena and economic

activities with their particular environment. Urban expansion focuses on growth of

population and physical expansion of towns and cities. In the present world, a common

man considers urban growth, expansion and sprawl synonymously but they are dissimilar

as urban growth is an amount of increase in developed and built-up land, infrastructure

increase of cities is expansion and increase in spatial features (typically has a negative

connotation) is urban sprawl which is uncoordinated growth (Bhutta, 2010).

Urban expansion is a major human activity that instigates massive influences on

urban environment at different scales (Jothimain, 1997; Turner et al., 1990) and it has

become a serious managerial issue as for as urban planning and sustainable management

is concerned. Socio-economic development and population growth trigger urban

expansion (Epstein et al., 2002; Wilson et al., 2003). A number of studies focus on urban

expansion in developed countries (Seto et al., 2002; Liu et al., 2002;) as well as

developing countries, like India, Pakistan (Barrens et al. 2001; Lata et al., 2001; Weng,

2001; Sudhira et al., 2003; Ghaffar, 2006; Shirazi, 2011; Anwar and Bhalli 2012; Khan,

Arshad and Mohsin, 2014; Bhalli and Ghaffar, 2015), China (Kaufmann and Seto, 2001;

Seto et al., 2002; Weng, 2002; Seto and Kaufmann, 2003; Cheng and Masser, 2003; Xiao

et al., 2006), and Mexico (Muoz-Villers and Lpez-Blanco, 2008).

As the world continues to become gradually urbanized, the earth’s land surface

continues to alter. Land Use Change (LUC) data is most essential for many fields of

science, urban land use planning, decision making and management. Land use refers to

“what people do to the land surface”. For example, agriculture, residential land uses,

25

commercial and industrial land. While the land cover refers to the “type of material

present on the landscape”. For example, water, forest, cotton fields, asphalt and concrete

highway (Jensen, 2005; Lillesand et al., 2007). Although environmental scientists and

geographers are concerned with the extent of land cover type change happening with the

passage of time on the Earth’s landscape (Mas, 1999); they are also alarmed at the

climatic influences of land use change (Chuvieco, 2008). One of those impacts is the

urban heat island phenomenon and higher overall temperature (Quattrochi et al., 1997;

Harwood, 2008).

Urban expansion is a form of urban growth taking place in various phases,

increasing the residential density, creating new urbanized land and redevelopment,

conversion of non-urban land into built-up areas (Angel et al., 2005; Bhatta, 2010). In

such advancements, the agricultural land, green spaces and vegetal cover, forests and

water bodies are encroached by the urban expansion. Urban expansion reduces the fertile

agricultural land by converting it into built-up areas (Khan et al., 2014). The

consequences of such conversion lead towards deforestation, water scarcity and urban

flooding, affecting the environment ecosystem and resulting in increase of land surface

temperature in cities (Puertasa et al., 2014).

The rapid population growth in urban areas is also one of the major reasons for the

urban expansion, extension of built-up areas for anthropogenic activities, development of

industrial areas along with commercial centers as experienced in the cities of developing

countries, like Lahore, Pakistan. Such kind of urban expansion and development occurred

haphazardly along unplanned new residential colonies and vicinities of the city.

Researchers argue on the changes occurred in patterns defining industrialization,

urbanization and economic activities as demographic and natural change (Khan et al.,

2014). These demographic and natural changes are major reasons initiating urban

expansion and the extension of built-up areas to cater the ever increasing population in

the city (Li et al., 2003). Thus, in the developing countries like Pakistan, the urban

expansion and the massive physical growth of the city can be attributed to the rapid

population growth. According to the UN report 2005, the population of the developing

countries with low income causes population explosion that is almost five time greater as

compared to countries that have developed already (UN, 2005).

26

In Pakistan, like other developing countries, the socio-economic problems of the

complex nature and climatic change are being created by the irregular pattern of the urban

sprawl at a very high rate. It is a fact that urban areas house 52% of the total population of

the world, although the urban areas occupy a small part of world’s land (PRB, 2013),

causing rapid threatening impact on natural environment and climate of urban areas

(Grimm et al., 2000; Haregeweyn et al., 2012; He et al., 2008). Resultantly, problems

including the deficiency of drinking water, squatter settlements, lack of better sewerage

facilities, overburdening of municipal facilities and conversion of fertile agricultural land

into built-up area are intensifying the urban scene, ultimately affecting the urban climate,

increasing the land surface temperature. Since many cities in Pakistan, especially the

bigger cities of the province of the Punjab are located in the heart of the most fertile land

including Lahore, Faisalabad, Rawalpindi and Multan, the expansion of urban land, is

therefore, a constant threat for fertile agricultural land, the supplies of food to cater the

ever increasing population in the future. The pattern of the urban population in Lahore,

Faisalabad and Multan specifically signifies the factors contributing the urban sprawl like

mass exodus from rural areas, natural increase of population, changes in the patterns of

the settlements, changes in socio-economic condition and improvements of the living

standards.

According to the census of 1981 of Pakistan, almost 24 million people were urban

residents which represent 28% of the total population of Pakistan. In Pakistan, 32.5 per

cent people were urban residents in 1998 which further increased 39.2 per cent in 2015

(GoP, 2015). In the Asia Pacific region, Pakistan has the major proportion of urban

population, 39.2 per cent, if compared with other countries of south Asia, which show

moderate urbanization level. Statistics show that by 2030 almost 50 per cent of population

of Pakistan would have be urbanized (GoP, 2015). Pakistan is urbanizing at an annual rate

of 3 per cent, the fastest pace in South Asian countries (Kugelman, 2013). A similar

pattern is discerned in the increase in urban population of the city of Lahore, as

mentioned in the previous studies conducted, indicating the increase in population and the

development of peri-urban vicinities. The increasing demand of housing facilities to

accommodate the ever increasing population and the conversion of the fertile arable land

into built-up structure have become the major concern over the past few decades. During

the last sixty six years, a massive development has taken place in the housing units,

contributing towards the increase in the land surface temperature of the city of Lahore.

27

2.3. Role of Remote Sensing in Assessing Urban Expansion

In geospatial science, satellite remote sensing observes the earth surface with

airborne sensors from space above its land surface. It has highly sensitive cameras which

not only operate through light, but produce images by utilizing other bands of

electromagnetic spectrum including microwave, infrared and ultraviolet. These airborne

and satellite sensors are located high in space, and can easily make images of large areas,

even a whole province. Observations related to earth through satellite images are

conducted from space by utilizing these sensors mounted on satellite (Bhatta, 2010).

Assessment of urban expansion with the help of geospatial techniques with

consistent acquirement of satellite remotely sensed digital data provides a broad visionary

approach to spatially organize urban land use and change detection analysis for the

sustainable urban development. The advent of satellite remote sensing technology

entertained a new horizon for assessing, monitoring and mapping land cover changes

along with empowering the researchers to predict future urban expansion more

technologically and efficiently than the conventional and traditional approaches (Maktav

and Erber, 2005). GIS and satellite remote sensing techniques seem to be a proper and

effective tool to present and recognize the urban growth phenomenon and are also utilized

globally for the analysis of urban expansion (Im et al., 2008).

Urbanization has numerous effects on the environment. With the rapid increase in

urban population, urbanized areas have also extended rapidly within the past several

decades (Goldblum and Wong, 2000). Many studies have examined that urban expansion

has produced localized increases in the land surface temperature as exposed by both long-

term analysis of satellite thermal data (Rizwan et al., 2008; Jiang and Tian, 2010;

Tursilowati et al., 2012) and analysis of ground based measurements (Kataoka et al.,

2009). Satellite remote sensing is a technique or tool that estimates electromagnetic

energy content present in a geographic area or an object from far by using sophisticated

sensors and then gathering important material from the satellite data utilizing algorithms

statistically and mathematically. Its utilities are integrated and synchronized with

mapping science tools, like cartography, GIS and other spatial techniques of data

collection (Bhatta, 2008). Spatio-temporal detailed information about urban morphology,

patterns of land use, distribution of population, infrastructure, and reasons for urban

dynamics are pertinent to be understood and observed. Urban satellite remote sensing

28

technique provides such type of information (Herold et al., 2004). Besides being an object

of challenge by spatial and spectral heterogeneity, satellite remote sensing seems befitting

source for collecting urban data (Jensen, 1999; Goldblum and Wong, 2000; Donnay et al.,

2001)

The fact is undisputable regarding earth observation being a modern science,

related with the studies of changing environment by using satellite remote sensing data

such as aerial photographs and satellite images (Bhatta, 2010). As Compared to similar

applications, satellite remote sensing of urbanized areas, is relatively a new methodology

for geographers and the remote sensing communal, particularly through space-borne

sensors (Maktav et al., 2005; Xu, 2008). Nonetheless, the interest of researchers and their

reliance on using the techniques of satellite remote sensing have shown a significant

intensification. The reasons for opting remote sensing are many; for instance, quick

acquisition of data for a large area, digital processing and respective analysis, integration

of GIS techniques and probability of getting temporal dataset (Bhatta, 2008).

The most valuable source used for monitoring urban expansion and mapping

built-up areas is remote sensing data, exclusively incorporating satellite remote sensing

system for various reasons (Xu, 2008; Bhatti and Tripathi, 2014). It provides a

comprehensive and synoptic view of enormous urban places, formally not imaginable to

get through simple field appraisals (Richards, 2013). Another major feature of utilizing

satellite remote sensing data is the accessibility of historical archives (Guindon et al.,

2004) helpful in understanding and mapping urban expansion over a span of time and for

urban analysis (Maktav et al., 2005; Griffiths et al., 2010).

Satellite images have also been extensively utilized for automated and semi-

automated mapping of land use change, water, vegetation, snow and other such

topographies (Joseph, 2005; Lillesand et al., 2004; Jensen, 2006). The techniques used in

formulation of image digital data can be categorized into two groups. The image

classification encompassing object based and pixel based methods comprises one group,

(Guindon et al., 2004; Cleve et al., 2008; Gao, 2008), whereas direct segmentation of

satellite images through indices comprise the second group (Zha et al., 2003; Zhang et al.,

2005; Knight et al., 2006). Each method imports definite limitations and a set of

advantages. However, indices precede the other classification methods in generating

results in short span of time.

29

Land cover data from satellite images is widely used in natural resource

management, environmental studies, and urban and regional planning. Some applications

are forest type mapping (Kennaway and Helmer, 2007), forest fire assessment (Mitchell

and Fei, 2010), Vegetation Drought Response Index mapping (Brown et al., 2008),

surface water estimation, land resource assessment, and urban green space delineation

(Lwin and Murayama, 2011). Moreover, land cover data serves as a primary form of

input bases in many geospatial models and spatial decision-support systems. In addition,

remote sensing data used in human settlement mapping are important for developing

countries where fine-scale GIS data are difficult to obtain. Human settlement mapping,

especially the detection of sparse urban, dense, industrial, and built-up infrastructures, is

important for population estimation, country resource assessment, urban planning and

monitoring of urban growth, and disaster management.

Numerous studies have utilized remote sensing statistics related to human

settlement such as house value estimation (Jensen et al., 2004; Wu and Murray, 2005),

population assessment (Liu and Herold, 2006; Yuan et al., 2008; Mao et al., 2012), slum

detection (Weeks et al., 2007; Kit et al., 2012; Kohli et al., 2012), urban population

density modeling (Joseph, and Wang, 2012), leaf index and household energy (Jensen et

al., 2003; Jensen et al., 2004), life quality assessment ( Nichol and Wong, 2006; Rashed

et al., 2007), urban growth (Cheng and Masser, 2003), and social vulnerability

assessment (Lu and Weng, 2007; Taubenböck et al., 2008).

Satellite remote sensing multispectral imagery and techniques have been useful

for several environmental analysis and monitoring the modification of land use, the

launch of the Landsat Earth observation satellite program since 1972 (Figure 2.1).

Satellite Remote sensing (SRS) imagery and data can provide essential information on

urban growth (Phinn et al., 2002; Jensen et al., 2004;), associated processes and their

effects on the urban climate and human environment, as well as observing the urban

expansion and spatial-temporal distribution of land use patterns (Ward et al., 2000;

Griffiths et al., 2010).

Satellite remote sensing thermal infrared data was not obtainable before the

launch of the Landsat 5/TM in 1982, due to low spatial resolution of other thermal

infrared sensors available. (Mallick et al., 2008). In 2013, Landsat 5/TM satellite was

decommissioned, and scan line corrector (SLC) failure was observed in 7/ETM+ satellites

30

Landsat imagery due to 22% drop in scanning (Figure 2.1). On 11 February 2013,

Landsat 8 satellite was launched successfully and it continues mission of earth

observation to date (Lulla et al., 2013; Tollefson, 2013).

The Landsat 8 OLI with 09 spectral bands (four visible, one near infrared, three

shortwave infrared) is stretched over a 185 Km swath including one panchromatic band

with a spatial resolution of 30 m and 15 m. (Maimaitiyiming et al., 2014; Yang et al.,

2014). Specifically, Landsat 8 satellite carried by thermal infrared sensor which sets up

two thermal infrared spectral bands 10.60–11.19 μm and 11.50–12.51 μm, respectively,

with a spatial resolution of 100 m by separating the thermal infrared wave band are

created on the proper Landsat 7/ETM+ (Irons et al., 2012). Therefore, to remove the

atmospheric effects, split-window algorithm can be utilized and then retrieve land surface

temperature with a relatively higher accuracy results (Yang et al., 2014).

Figure 2.1: Landsat Missions’ Timeline and their Current Status

Source: USGS, 2016

One of the reasons for high demand of satellite remote sensing data for urban

applications and analysis is the coverage of very large area encompasses. Remote sensing

data allows the researcher to study the large areas which may be very hard to access, and

it also delivers almost appropriate and actual material (Muttitanon et al., 2005). Spectral

and spatial high resolution images are also vital for land use observation as the sensor

may detect more mixtures of land use classes. Satellite remote sensing provides very large

archive of data covering time span of more than 40 years of observations (Reis, 2008).

31

No doubt, the limitations of optical remote sensing has sensitivity to atmospheric

conditions like a pattern of cloud may cover the area of study or so. In addition, the

resolution of the sensor limits the accuracy of results obtained from the satellite images.

The issues of urban landscape are complicated, featuring spectral and spatial

heterogeneity along with several surfaces of anthropogenic and natural origins which

create hurdles in analysis and further image classification creating large segment of mixed

pixels. In the course of time, the researchers studied that the acquisition of the data sets

was constrained and the researches had some limitations. First of all, there is a non-

availability of the cloud free and free of cost high resolution images of the study areas.

Secondly, as the Landsat imagery was introduced in 1973, and the earlier images had low

resolution. Thirdly, the deficiency of Landsat 7, the Scan Line Corrector (SLC) images of

the ETM+ sensor were rendered inadequately for the investigation of the LST and urban

expansion change detection studies.

There are several revisions on the usage of satellite remote sensing to screen urban

expansion and its impression on surface temperature variation (Jensen et al., 1993; Gatrell

and Jensen, 2008). Urban satellite remote sensing is very important and definite for

demonstrating the relations between people and their environment (Li and Yeh, 1998).

Space-borne remote sensing data and satellite images are particularly beneficial for

developing countries as they are cost effective and time saving techniques where as

conventional methods of surveying are expensive and time consuming (Jensen et al.,

1995; Dong et al., 1997), and these procedures and systems of remote sensing have

become more feasible substitutes to traditional survey and methods of ground-based land

use mapping and urban analysis.

2.4. Urban Climate Patterns and Land Surface Temperature

Urban climate refers to the difference between the climatic conditions of a city

and the climate of its neighboring countryside. The most significant features of urban

climate include higher air and land surface temperatures, lower humidity and changes in

radiation balances and constrained atmospheric exchange and their cause increases of

pollutants from a variety of sources (Kuttler, 2008). Urban climate can also be referred to

specific climate conditions for a long time in cities that differ from surrounding country

sides and are attributed to urban development, urban expansion, massive change in urban

32

landscape, and variations in air and surface temperature of urban area (Eum et al., 2011).

Urban land cover changes are intensely altered landscapes aloof from ecosystems and

natural processes (Blake, 2011). Urban areas have weaker wind flow and have higher

temperatures as compared to neighboring non urbanized areas. Cloud cover, air pollution,

orientation of infrastructure and shades of the building decide the sum of sunshine

received by an urban area. Huge and tall structures in the urban areas influence the flow

of radiation (Huang et al., 2011). The absorption of solar radiation is higher in urban

areas with more thermal conductivity and similarly the capacity to store urban heat

throughout the day and release at night-time (Xian et al., 2006). Moreover, the surface

structure of urban areas possesses such features as they influence the micro-climate of

urban area (Carnahan et al., 1990; Aniello et al., 1995; Voogt and Ok, 2003; Huang et al.,

2011).

The weather elements are detected in Met stations in urban areas, at least one

station in every city. In some cases, these observations don’t detail micro climate

conditions and actual climate changeability. Even in some cities, the climate stations are

not found and, therefore, the climatic data is received from the neighboring Met station

(El-Nahryv and Rashash, 2005). So, the remote sensing thermal infrared images are

widely used for estimation of climatic condition because of their capacity to cover larger

areas providing thermal condition of cities and surrounding areas. Satellite remote sensing

imagery with thermal infrared data are distinctive bases of information to describe urban

climate and also utilized for the estimation of land surface temperature (Weng, 2009).

Typically urban surfaces display higher overall surface and air temperatures than

vegetation covers and rural areas because urban land constructions trap more heat in the

urban canopy layer while vegetation cover helps to moderate the surface temperatures of

the adjacent non urbanized areas (EPA, 2003). The relationship of different types of land

use can be associated with land surface temperatures (Pease et al., 1976), Temporal and

spatial change in LULC can be observed with changes in urban heat island intensity and

magnitudes (Lo et al., 1997), and seasonal investigation can also be analyzed through

seasonal LULCC patterns (Liu et al., 2008). Land use changes are an imperative input to

global climatic variation and environmental monitoring (USCCSP, 2003). The population

growth and anthropogenic activities have been renowned as the leading reasons behind

land use land cover change, although similar changes do arise gradually and naturally

33

(Coppin et al., 2004; Nagendra, 2004). Human beings put pressure on the land, altering

its land cover type, in order to maximize their benefits from the land.

Lambin et al., (2001) investigated the factors of the land use and land cover

change which in return modify many environmental development policies. He challenges

the opinions that population growth and poverty were the prime reasons for land use and

land cover change. This study reflects that the increasing economic opportunities caused

by institutional factors are the determining forces for land use. Both local and national

polices and market define constraints and opportunities for new land uses. Global forces

have assumed a decisive role in land use change for they invigorate or mitigate local

factors. They considered that the human influence on land cover included: demographic,

cultural, socio-economic, urbanization, technological, institutional or related to

globalization. The environmental effects of Land use land cover change can be

categorized into: climate change, environmental pollution, biodiversity change, LST

change and other impacts (Ellis and Pontius, 2009). For example: converting forest and

vegetation cover to impervious surfaces and other land uses can lead to deforestation to

biodiversity loss, overgrazing to pollution, climate change and higher temperature in

cities. Environmental and climatic changes affect civilization (Sherbinin, 2002), so

evidence on land use conversion can always support decision and policy making (Adu-

Poko, 2012).

Natural disasters and anthropogenic activities are the main reasons for dramatic

changes in land use profile (Muttitanon and Tripathi, 2005). Land use profile

transformations affect global climatic and environmental sustainability on a local,

regional and global scale, which makes the examination of these LULC changes vital for

future well-being of the mankind (Sun et al., 2012). Some types of LULC changes,

which origin from direct conversion in land use and its impact on micro-climate and air

temperature, meteorologically are very important (Owen et al., 1998). The main threat to

the environment and humanity originates from anthropogenic (i.e., human-induced)

modifications rather than from changes forced by nature. The most prominent

consequences of anthropogenic activities are such changes in land cover as vegetated

cover, forests lands turning to impervious surfaces and urban land uses (Tan et al., 2010).

One of the greatest significant types of Land use transformation is urbanization and urban

expansion itself (Xian et al., 2006; Zhou et al., 2011). In relationships of surface

34

temperature change, conversion from vegetation cover to impervious surface can have

similar consequence as global warming and globalization (Mohan et al., 2011).

There is an overall compromise that urban expansion and development lead to

land surface and atmospheric modification, land use alterations as well as the content and

structure of the atmosphere (Roth et al., 1998). Altogether, these alterations consequence

in appearance of several micro and meso-scale change of urban climate, warmer than the

surrounding of rural areas (Zhou et al., 2011). Urban heat islands phenomena basically

describes the urbanization impact on urban climate at local, regional and global level.

Urban heat island shows the inconsistency in ambient temperature inside an urban area

and its immediate country side (Nonomura et al., 2008) and demonstrations of the result

of cities, storing and generating more heat than the neighboring country sides (Aniello et

al., 1995).

The magnitude of earth surface temperature increase is closely associated with the

extent and type of urban development rather than the actual size of urbanized area or

population (Roth et al., 1998; Xian et al., 2006). It means that the mega-cities of the

developing countries might experience higher temperature effects due to UHI as

compared to the older and bigger cities in the developed countries (Weng, 2009). Rapid

growing urban surfaces display different thermal, radiative, aerodynamic, diverging

thermal and moisture properties than rural surfaces (Xian et al., 2006). Owing to the

reduction in agricultural land and surface moisture, the temperature is dramatically

increasing in urban area with continuing urbanization activities (Owen et al., 1998).

There is also a conflicting urban climate effect known as urban heat sink along

with the phenomenon of UHI. Urban heat sink demonstrates an opposite stance to UHI of

asserting urban places cooler than the rural areas in the surroundings. Although the

phenomenon of UHS is even more time dependent than UHI, yet, it can be easily

influenced by morphological and seasonal factors. For instance, the radiant and spectral

characteristics of pre-emergent state of crops are identical with the bare soil, and they

exercise remarkable impact on overall land surface temperature. Moisture and density are

also major factors attributing to the abnormal surface temperature of land (Carnahan et

al., 1990).

35

Huang et al., (2011) estimated that the increasing distance from the downtown

tends to decrease the urban temperature. Urban heat island is higher in land surface

temperature than the non-urbanized surrounding areas including some hot-spots within

the area with higher temperature. Some of the other researchers conducted studies and

concluded that higher temperature is connected with less vegetation and land use land

cover changes such as industrial and commercial zones with large open pavements,

parking lots and flat roofs and not with the distance dichotomy of downtown. Weng,

(2009); Roth et al., (1998) and Zhou et al., (2011) emphasize that increase in vegetation

cover contributes to decrease UHI influence along with the growth in water bodies.

Urban land use has a substantial influence on global, regional, and local, climate

and environmental alteration, and has momentous social, economic, biophysical,

ecological, and climatic special effects (DeFries et al., 2010). These impacts are

augmented by the Spatio-temporal variation of urban land use alterations and they tend to

last for decades and are frequently irreversible (Seto and Shepherd, 2009). Optical

satellite sensors on board several remote platforms show an important part in the urban

expansion monitoring and its impact on LST. The creation of remote sensing satellite

equipment is through it conceivable to retrieve land surface temperature for local region

and global scales through different airborne sensors and satellite platforms that detect and

capture thermal infrared data from Earth’s surface (Streutker, 2002).

The thermal environmental condition in cities is characterized by the urban heat

island phenomena affecting human health, climate, environmental conditions and energy

balance. Ground-based fix Met station observations reflect only thermal condition around

the Met station. On the other hand, utilizing satellite remote sensing, thermal infrared

bands allow the investigator to get the thermal climatic condition for each and every pixel

in the satellite thermal image. Remote sensing is a convenient tool and technique for

studying the environment through satellites sensor systems recorded digital information

for energy patterns and balance (Weng et al., 2004). Now-a-days, thermal infrared urban

imagery has been extensively utilized to measure land surface temperature in urban areas

and appraise the urban heat island phenomenon (El-Nahry and Rashash, 2005; Raja sekar,

and Weng, 2009).

For the evaluation of the LST accurately, various atmospheric measurements are

required simultaneously over the area of study. The climatological analysis requires a

36

network of in situ measurement to provide historical data of atmospheric temperature

with moist static energy trends. Despite the availability of the in situ data, the spatial

resolution of the network is not efficient enough to identify the areas of increased emitted

surface heat, according to the measurements obtained by the monitoring stations. In

addition, in situ measurements are not efficient enough to provide indication of thermal

properties, discarding surface features because of the height of the air temperature

measurements. However, airborne or satellite sensors can measure the radiant emissions

from the surfaces and such larger observations enable the measurement of thermal

properties of small surface features. It also allows investigation of high spatial resolution

of urban microclimates (Hardegree, 2006).

2.5. Use of Remotely Sensed Data in Land Surface Temperature

Estimation

Earth Surface Temperature (EST), containing land surface temperature and Water

Surface Temperature (WST), mentions the temperature of the highly significant layer

where the surface and atmosphere converge (Maimaitiyiming et al., 2014; Yang et al.,

2014). Earth surface temperature is a significant parameter reflecting the environment of

the earth surface, and is most commonly utilized in numerous fields of studies such as

global warming (Friedel, 2012), agricultural monitoring (Son et al., 2012), the effects of

UHI and climate change.

Land surface temperature, is frequently referred to as the skin surface hotness of

the Earth and as retrieved from Satellite remotely sensed thermal infrared images

(Trenberth, 1992; Anderson et al., 2011). Land surface temperature tends to feel the heat

of the surface at a specific location. From a satellite remote sensing stance, the surface

means everything is detected by sensor over the ground. It might be the grass on a lawn or

the leaves in the canopy of a forest, snow and ice and the roof of a building. Thus, LST is

not the similar as the air temperature that is encompassed in the daily weather report of

fixed station (NASA, 2000; Seba, 2013).

Land surface temperature is an essential element in evaluating and exhibiting the

surface energy and water balance, evapotranspiration (ET) and surface moisture (Gillies,

Carlson, Cui, Kustas, and Humes, 1997; Moran, 2004; Carlson, 2007), and climate

change at local, regional and global scale (Jin, Dickinson, and Zhang, 2005; Weng, 2009;

37

Rozenstein et al., 2014), with principal significance for multi-dimensional applications,

like urban climate, vegetation monitoring and the hydrological cycle (Ramanathan et al.,

2001; Kalnay et al., 2003; Wan et al., 2004; Chapin et al., 2005). Land surface

temperature and its spatial differences have long been emphasized of research on the

phenomenon of Surface Urban Heat Island (SUHI) (Oke, 1982; Streutker, 2003;

Rajasekar, 2009; Imhoff et al., 2010). Land surface temperature variations in time and

space, observed by remote sensing techniques and thermal images are utilized for the

estimation of a magnitude of geophysical variables, such as vegetation water stress, soil

moisture, evapotranspiration, and thermal inertia (Agam et al., 2008; Kustas and

Anderson, 2009; Karnieli et al., 2010).

LST is the key factor determining the energy exchange and surface radiation

(Weng, 2009), monitoring the distribution of heat flow between the temperature of

atmosphere and land surface (Tan et al., 2010). LST, as a significant variable, aids in

governing radiative, sensible and latent heat changes at the boundary of biosphere and

atmosphere (Guillevic et al., 2012). Thus, monitoring and understanding the dynamics of

the LST and relationship with anthropogenic activities are critical for demonstrating

environmental changes (Kerr et al., 2004), forecasting climate and monitoring vegetation

(Meng et al., 2009). For example, models with climate simulations display that a major

reduction in agricultural land and vegetation, has foremost to a rise of land surface

temperature (Shukla and Mintz, 1982), a drop of evapotranspiration (Collatz et al., 2000)

and rainfall over surfaces of land and altering the balances of sensible and latent heat

changes (Moran et al., 2009). Consequently, Land surface temperature is a significant

component of the climatic change that can be retrieved from optical satellite remote

sensing explanations to observer long term ecological and climatic changes (Guillevic et

al., 2002; Guillevic and Koster, 2002).

Land surface temperature works as a vital indicator of biological, chemical, and

physical processes of the ecosystem. Land surface temperature is influenced by such

properties of urban surface roughness, surfaces as color, chemical composition and

humidity (Tan et al., 2010). LST controls the atmosphere especially lower layers. Thus, it

can be critical factor for the urban environment and also called weather variable because

Land surface temperature modifies the balance of energy and surface radiation (Retalis et

38

al., 2010). Land use arrangement is one of the major factors manipulating LST,

particularly the percentage of each land use type occupying the urban land.

Urban built-up land and buildings can have a particularly high impact on LST

(Zhou et al., 2011). LST has a positive relationship with built-up structure and shows

negative association with vegetated grounds and forests cover (Sun et al., 2010). Major

reduction in agricultural land influences the balances of energy and heat exchange,

leading to an intensification of LST, at the same time, evapotranspiration and

precipitation have the reverse trend (Guillevic et al., 2002; Meng et al., 2009; Zhou et al.,

2011). Not only the high density areas, but buildings and their feature structures also

matter and open surfaces, paved areas of complicated shapes tend to increase land surface

temperatures.

Figure 2.2: The Albedo of different Land Surfaces

Source: Oke, 1987

The traditional method of getting land surface temperature was measurement by

navigates with thermometer riding on ground vehicles and observations obtained from

fixed ground based Met station (Voogt and Oke, 2003; Srivant et al., 2012). Due to the

variation of LST, the traditional technique of discrete point observations taken from fixed

station cannot be obtained continuously and large-scale information about the land

surface temperature (Yang et al., 2014). However, the practicability of satellite remote

39

sensing and thermal infrared technology makes it possible to estimate the land surface

temperature spreading over outsized areas with a systematic re-examine competence

(Peng et al., 2014).

Table 2.1. Radiative Properties of Several Materials Material Albedo (α) Emissivity

Asphalt 0.05 – 0.20 0.95

Concrete 0.10 – 0.35 0.71 – 0.91

Urban areas 0.10 – 0.27 0.85 – 0.96

Soils: wet to dry 0.05 – 0.40 0.98 – 0.90

Grass: long to short 0.16 – 0.26 0.90 – 0.95

Source: Oke, 1987

Remote observation of LST became possible by aircraft and satellite platforms

providing new horizons for the observation (Roy et al., 2010), of land surface

temperature, and the critical evaluation of their relationship using the thermal remote

sensing and urban climatology simultaneously (Voogt and Oke, 2003), and to make

digital classification of land use cover as input to study energy exchange between surface

and atmosphere through models (Srivanit et al., 2012). Voogt and Oke, (2003) proposed

03 key uses of satellite remote sensing thermal images to the learning climate of urban

areas. Two of them focused on investigating relationships either between urban spatial

structure, land surface characteristics and thermal designs and or between surface and

atmospheric islands of heat and the third is addressed on reviewing surface energy

exchange stabilities by joining models of urban climate with thermal remote sensing data

(Srivanit et al., 2012).

Since 1980s, many studies have been accompanied to investigate the viability and

precision of thermal image analysis and uses for land surface temperature retrieval (Li et

al., 2013). Since the availability of satellite TIRs data (Vinnikov et al., 2011), many

studies have been utilizing satellite images to monitor land use changes and their impacts

on Land surface temperature (Weng, 2001; Dousset et al., 2003; Xiao et al., 2007). A

range of different algorithms, such as multi-channel, split-window and mono-window

algorithm (Katsiabani et al., 2009; Qin et al., 2001a; Zhou et al., 2010) were

recommended to estimate land surface temperature for actual application situations and

account of the individualities of dissimilar type of data. Thermal infrared data is measured

40

by remote sensing satellite sensors in thermal infrared spectral range. Radiance of the top

of the atmosphere detected by satellites thermal infrared sensors (Kustas and Anderson,

2009; Weng, 2009). However, radiation measured by the satellite sensor is influenced by

atmospheric constituents and in order to get accurate values, this original thermal data

must be modified for atmospheric and emissivity effects (Weng et al., 2004). This

radiometrically corrected thermal infrared imagery can be utilized to estimate LST in

Kelvin or degree Celsius (Weng, 2009; Vinnikov et al., 2011).

Historical observations of urban climate through ground based station

measurements were carried out by using regular meteorological networks. In-situ

instruments measure the chemical or physical properties of the adjacent air. Platforms

utilized for atmospheric and surface observations involve ships, ground-based stations,

weather balloons, and aircraft and satellite sensors. Satellite remote sensing systems

indirectly retrieve these properties from perturbations of electromagnetic signals passing

through the air (Jensen, 2007). In recent decades, airborne and satellite sensors, such as

Landsat TM, ETM+. OLI_TIRs, MODIS, ASTER and HCMM (Sobrino et al., 2004;

Hung et al. 2006; Pu et al., 2006; Nichol et al., 2009) and AVHRR (Advanced Very High

Resolution Radiometer) (Kato and Yamaguchi, 2007) images have been utilized in

studies correlated to the land surface temperature features of urban places (Dousset and

Gourmelon, 2003; Ji and Peters, 2004; Pongracz et al., 2006; Sahin et al., 2012).

With the growing recognition of the significance of LST, procedures and

techniques for its estimation from satellite images have continuously been developed (Li.,

2013). In addition to land surface temperature quantities, this thermal infrared imagery

might also be used to get emissivity data of dissimilar land surfaces with various temporal

and spatial resolutions and precisions. Land surface temperature and emissivity data have

been used in environmental and urban climate studies (Quattrochi and Luvall, 1999),

mostly for examining patterns of LST and their association with land surface

physiognomies, evaluating UHI, and relating land surface temperatures with energy

fluxes of land surface for depicting landscape processes, patterns and properties (Weng et

al., 2004).

Satellite remotely sensed thermal Infrared imagery data are an exclusive basis of

info to describe heat islands, which are interrelated to the heat islands of canopy layer.

Atmospheric temperature data particularly, everlasting MET station temperature data

41

offer long term temporal coverage but there is lack of spatial coverage. Measurements

and observations by ground vehicles rectify inadequacy to certain degree but fail to

provide a synchronized image of the whole urban area (Weng, 2009; Weng and Fu,

2014). Only thermal infrared imagery data can offer a simultaneous and continuous

observation of an entire city, which is of major significance for comprehensive study of

urban land surface temperature (Schmugge et al., 1998; Schwarz et al., 2011).

Thermal infrared satellites data has lots of advantages, due to this most of the

urban climate studies preferably utilized thermal data. For instance, satellite remotely

sensed data permits for the acquisition of thermal data over a region and very large areas

and also provides a lot of information; on the other hand direct measurements only deliver

points measurements. Another significant advantage of satellite remotely sensed data is

that it is very cheap and generally easy in acquiring thermal data, while direct

measurement method is tremendously expensive and time consuming for the assessment

of whole of the region and area of interest. It is also essentially, that the thermal data from

the satellite sensor cover is of the whole of the region and area of interest at one time.

On the other hand, the direct measurements of the temperature were acquired from

the MET stations at different times, at different conditions, which affect the analysis and

inferences. Such measurements are also coupled with some advantages including ability

to take the vertical surface into account, which is particularly important for highly dense

urban built-up areas. Satellite remotely sensed data has some flaws like its sensitivity to

atmospheric conditions, which depends upon surface roughness and land use type, lack of

information about vertical magnitude of land surface temperature. Despite these

weaknesses, satellite remotely sensed data remains one of the most reliable and important

sources of thermal data for urban heat island and climatic change studies.

2.6. Relationship between NDVI, NDBI and LST

In order to examine the relationship between Land surface temperatures and land

use changes (e.g. vegetation and built-up area), many researchers employed a quantitative

method in the discovery of the correlation between temperature and numerous remote

sensing indices used. For the extraction of various land surface features from satellite

remote sensing images, a variety of indices has been developed (Chen et al., 2006).

Qualitative approach on the association between the land use pattern and land surface

42

temperature helped us in urban planning and decision making (Tian & Xiangjun, 1998). It

is acknowledged that several vegetation indices acquired from satellite remote sensing

imagery can be utilized in the extraction of vegetation quantitatively and qualitatively.

Several remote sensing indices have been quantitatively utilized to characterize

land use land cover categorized for land surface temperatures studies (Liu and Zhang,

2011). On the other Qualitative researches on the correlation between LST and LULC,

patterns were utilized for sustainable development and planning of urban land use (Xu,

2010). Several indices of vegetation got from satellites thermal imagery can be utilized in

the assessment and amount of vegetation both quantitatively and qualitatively. The NDVI

(normalized difference vegetation index) is the most frequently utilized method for the

assessment of vegetation cover (Tian and Xiangjun, 1998).

Other several indices comprise the normalized difference built-up index, the

normalized difference Bareness Index (NDBaI), normalized difference water index

(NDWI), Vegetation Water Content (VWC), (Gao, 1996), Normalized Difference Snow

Index (NDSI), used for built-up land, vacant land, water bodies and snow cover

extraction, respectively (Bannari et al., 1995; Hall et al., 1995; Mcfeeters, 1996; Zha et

al., 2003). The calculations observed through these indices are founded on several stuffs

like multispectral satellite image bands reflection or in terms of strong absorption (Jensen,

2006).

The estimate of vegetation productivity Soil Adjusted Vegetation Index has been

utilized (Yuan and Bauer, 2007) in heterogeneous urban areas (Weng et al., 2004). Zha et

al., (2003) recommended the usage of a Normalized Difference Built-Up Index reliant on

the spectral reflectance attribute of artificial exteriors. Xu, (2007) suggested the Index

Based Built-up Index (IBI) for the quick documentation of built-up structures from

satellite imagery. The Normalized Difference Water Index is applied to illustrate water

content quantitatively (Huete, 1988). It is imaginable that the application of NDWI, IBI,

NDVI, and SAVI could characterize land use categories through quantitative methods for

recognition of correlation between dissimilar indices like NDVI, SAVI, IBI, NDWI and

LST in urban heat island and climate studies (Xu, 2007; Zha et al., 2003).

The investigation of the spectral signature of the area under study supplements the

development of index for its extraction of information from the satellite images (Gitelson

43

and Merzlyak, 1996; Huete and Jackson, 1987; Dozier, 1989). Satellite remote sensing

images are accomplished of measuring, estimating and detecting a range of components

concerning the morphology of town and cities (Webster, 1995), such as the, shape,

amount, textural form, density and expansion of built-up land and decreasing vegetation

cover in urban areas (Mesev et al., 1995). Satellite remote sensing images are particularly

significant in urban regions of prompt land use land cover changes someplace the updates

in terms of information are tiresome and laborious through conventional surveying (Fung

and LeDrew, 1987; Martin, 1989). The observing of urban expansion and development is

mostly to govern the amount, variety and site of land use conversion (Howarth, 1986;

Eastman and Fulk, 1993).

Several research projects have addressed the appropriate use of satellite remote

sensing images in a comprehensive range of analysis and urban applications for

supporting decision and policy making environment (Gatrell and Jensen, 2008; Zeilhofer

and Topanotti, 2008;). In the areas of urban land use planning, several studies have been

investigated through satellite images, mainly Spatio-temporal modelling of urban growth

(Jensen and Cowen, 1999; Hathout, 2002; Jat et al. 2008) and the urban change detection

analysis (Liu and Lathrop, 2002; Alphan, 2003; Herold et al., 2003a; Bahr, 2004; Jensen

and Im, 2007), land use changes appraisal (Weng, 2001; Yuan et al., 2005; Yuan, 2008)

estimation of LST and observing urban heat island Phenomena (Kato and Yamaguchi,

2005; Xiao et al., 2006).

2.7. Urban Expansion and its impact on Land Surface Temperature

The world is experiencing rapid urban expansion, where cities are accommodating

more than half of the population of the world, and furthermore, 70% of the population

over the globe will reside in the urban areas by 2050 (UNO, 2007). In the recent era, the

association between the land use type and the environmental quality has intensively

received attention for urban planning (Stone et al., 2001). Patterns of Land use reflect the

fundamental social and natural processes, and proved key information to understand and

model several phenomena on the surface of the Earth (Howarth, 1986; Liang, 2008).

More prominently, land use/land cover change data are significant for

environmental and climate change studies and developing considerate the multifaceted

relations between anthropogenic actions and global temperature change (Jung et al.,

44

2006; Gong et al., 2013). Precise land use identification is also an important aspect for

enlightening the presentation of hydrologic, atmospheric and ecosystem models and

changing land surface temperature patterns (Miller et al., 2007; Running, 2008). LULC

information is critical and helps as the foundation for worldwide change of surface

temperature and climatic studies (Bounoua et al., 2002; Jia et al., 2014).

The climate in metropolises and supplementary built-up places is transformed due

to the commercial and industrials activities of urbanization, urban expansion and changes

in Land use type. The most authoritative problem in cities is increasing land surface

temperature due to conversion and alteration of vegetated cover to impervious surfaces.

These land use changes affect the surface temperature, absorption of solar radiation,

storage of heat, evaporation rates, wind turbulence and can extremely alter the

environments of the land surface to atmosphere over the whole cities. The temperature

variance between cities and countryside areas is typically named as urban heat island

Phenomenon (Mallick et al., 2008).

When changes occur due to urban expansion over a period of time as a

consequence of conversion of vegetated surface to impervious surface and its replacement

with land surface such as commercial buildings, highways, parking lots and residential

areas and consequently modify the surface temperature and characteristics of the moisture

and albedo (Betts, 1999); Thus, changes in land use result in a consistent modification in

the land surface temperature of that area. These changes in land surface temperature tend

to increase the temperature differences between rural and urban areas. Table 2.2 shows

urban-rural constraints amplifying the urban heat island effect (Robinson et al., 1986;

Harwood, 2008).

45

Table 2.2: Urban-Rural Contrasts

Elements Parameter Urban Compared with Rural

(- less; + more)

Incoming

Radiation

On Horizontal Surface

Ultraviolet

-15%

-30% (winter)

-5% (Summer)

Temperature Annual Mean

Winter Maximum

Length of freeze free season

+0.7 °C

+1.5 °C

+ 2 to 3 weeks

Wind Speed Annual Mean

Extreme Gusts

Frequency of Calms

-20 to -30%

-10 to -20%

+5 to +20%

Relative Humidity Annual Mean

Seasonal Mean

-6%

-2% (Winter)

-8% (Summer)

Cloudiness Cloud Frequency and Amount

Fogs

+5 to +10%

+100% (Winter)

+30% (Summer)

Precipitation Amounts

Days (with <5mm)

Snow Days

+5 to +10%

+10%

-14%

Source: Harwood, 2008

Voogt and Oke, (1998) explored the variations in surface temperature rise owing

to the patterns of shaded and irradiated surfaces. A strong anisotropy is also exhibited in

light industrial and residential land uses. Similarly, Ao and Ngo, (2000) investigated land

surface temperature by means of GIS and field measurement in Vancouver, Canada. The

study aims at finding out the association between the land use type and surface

temperature within the urban areas of Vancouver. The research quantifies the influence of

vegetation, density of the built environment and infrastructure and impervious surfaces,

on the release of heat. The research propagates that the zones with higher population

density such as center core, have higher temperature interconnected to the thermal

material goods of existing infrastructure and street physiognomy. The coolest temperature

was noted near the dense vegetation. Ifatimehin and Ufuah, (2006) also incorporated the

46

study by utilizing GIS and RS methods combined with field checks and survey to map

land use changes and measured the rate of urban expansion along with the loss of

vegetation in Lokoja between 1987 and 2005.

The built-up area, vacant land, cultivated land and other land use types increase at

the expense of vegetation cover. Urban expansion has also led to environmental and

ecological problems such as increase in surface temperature, erosion and major reduction

in vegetation cover. He et al., (2006) demonstrated a newly found urban expansion

scenario patterns through integration of two different models namely System Dynamics

(SD)-based model and Cellular Automata (CA)-based model. One model was applied in

Beijing to study the urban growth from 1991 to 2004 and on its basis future urban growth

was predicted for 2020. Several studies such as Kalnay and Cai, (2003); Trenberth,

(2004); Feddema et al., (2005); Christy et al., (2006); Mahmood et al., (2006) and Ezber

et al., (2007) examined and proved that if land use changed, there was a corresponding

change in land surface temperature in urban areas (Chase et al., 2000).

Yue and Xu, (2008) observed that major factors affecting urban environment of

Shanghai were population density, vertical development of buildings, types of underlying

surfaces and allocation of industries. Satellite remotely sensed data have been utilized to

investigate the relationship between land surface temperature and different land use land

cover categories such as vegetation and artificial surfaces (Xian and Crane, 2005; Van

Thi and Xuan Bao, 2010; Xu et al., 2010). Remotely sensed thermal images that retrieve

land surface temperature have been efficiently utilized to determine the effects of land use

changes on thermal urban environment (Sun et al., 2010). Several classification

approaches such as the maximum likelihood (Weng et al., 2004; Basar, 2008), spectral

un-mixing and iterative self-organizing method (ISODATA) have been utilized to derive

land use types and to describe the statistical correlation between the urban features and

urban thermal environment (Weng et al., 2014).

Thermal remote sensing, however, has some deficiencies. It often overestimates

the intensity of the urban heat islands, owing to the urban surfaces, heterogeneous in

nature, as detected by the thermal sensors of satellite (Roth et al., 1989a). Voogt and Oke

(1997) investigated this issue by making comparison between in situ measurements of the

temperature with the surface temperature measured by thermal images. As anticipated,

the measured air temperature can considerably differ from land surface temperature as

47

estimated by the satellite thermal images. The precision of the measurements is

determined by the resolution of the sensors.

2.8. Urban Heat Island

Temperature in urban areas is typically warmer than surrounding countryside; this

unique phenomenon is recognized as the Urban Heat Island (UHI) (Landsberg, 1981;

Moreno-Garcia, 1994). There is a measureable pocket of warm air produced by

metropolitan cities as they represent areas where human activity is concentrated and large

density of population live and work. Urban heat island effects encompass some former

observations in climatology, starting from the works of meteorologist Sir Luke Howard in

early nineteenth century (Howard, 1818). The phenomenon of UHI was first observed in

London in 1820, Sir Luke Howard documented that temperatures in London was

remarkably warmer (night was 2.1°C) than the rural areas (Harwood, 2008). The USA

started its research on urban climate in the mid-1950s, but serious urban climate

monitoring did not begin till the early 1970s (Gabler et al., 2009)

A number of factors mandate that the temperatures of urban area are normally 1-

6°C (2-10°F) warmer and varied from those of the surrounding countryside. Several

factors include; energy use, industry, automobiles, human population, materials and

buildings, pollution levels and water on the surface. As urban development increases and

global population grows, the above mentioned factors will intensify too, and UHI will

directly affect more people than the climatology of the region in general (Gabler et al.,

2009). The intensity and formation of UHI depend on climatic conditions and impervious

surfaces in cities. The difference between urban and rural temperature exists, especially

during clear, calm conditions and is the smallest with windy conditions. It is generally the

largest spatial extent of UHI in evening time (Kidder and Essenwanger, 1995).

UHI is one of the major effects of urban expansion that eventually leads to certain

changes in surface temperature, water vapors, solar radiation absorption, air temperature,

and evapotranspiration and air pollutant. All these changes directly affect human health in

general. The phenomenon of urban heat island is also referred to as higher radiation heat

budget and thermal conductivity (Tan et al., 2010) in urban areas because of impervious

surfaces when compared with the countryside (Landsberg 1981). Anthropogenic activities

contributing to the urban heat island effect include installation of new units for industrial

48

activities, increasing number of vehicles, cooling and heating systems in residential areas

and commercial centers and economic activities for material gains in the urban centers

(EPA, 2003). The IPCC, (2007) reports that a considerable increase is observed in

quantity of methane, carbon dioxide and nitrous oxide in the air, caused by anthropogenic

activities. Cities are prime contributors of greenhouse gases in this regard (Parry et al.,

2007). The effect urban heat island is observed in the spatial distribution of higher land

surface temperature affected by urban expansion which is maintained by surface heat

fluxes. Land surface temperatures are measured as primary source for conducting urban

heat island studies.

The most discussed impact of urbanization is an increase in local and regional

temperatures. Specifically, on a micro-scale, urban heat island is the most important

phenomenon in climatic change (Stohlgren et al., 1998). There is thermal anomaly in the

heat islands, differentiating the observation of temperature from one location with the

other; for instance, the temperatures of the urban areas are higher than those of the rural

areas (Weng, 2002; Weng et al., 2004). UHIs vary in their severity among seasons and

scale (Pinho et al., 2000). Several factors including, population and city area, the size of

green spaces, geographical location and climatic conditions can affect the intensity of

urban heat islands (Kalnay et al., 2003; Kim et al., 2005).

Figure 2.3: Temperature profile of the urban “heat island” shows the increase in

temperature with increase in urbanization

Source: Gabler et al., 2009

49

Under proper conditions, as Oke (1982) puts in, urban heat island can range up to

10-15°C. As a consequence of micro climatic change, as created by the urban heat island,

the necessity increases in energy consumed for cooling systems in the buildings (Adina et

al., 2009). Akbari et al., (2001) asserts that increase in 1°C temperature raises demand for

electricity from 2 to 4 % every year. Vegetation in the typical urban built up areas is

lesser than the surroundings and the surfaces are usually in dark contrasts as compared to

the rural areas. The difference in the temperature of an urban center with its surroundings

comprising of rural areas reaches up to 2.5°C, at daytime in warm summer, causing

additional demand for about 5-10% electricity (Akbari et al., 2001).

According to Oke (1987), Akbari et al., (2001) and Santamouris et al., (2007), the

causes of urban heat island include hindrance in the flow of air because of high

architectural structures of the buildings, release of heat generated by anthropogenic

activities, absorption of solar radiation because of low albedo and low amount of

evapotranspiration as there is less vegetal cover. Figure 2.4 summarizes the causes of

UHI:

Figure 2.4: Causes of Increase of Urban Temperature and UHI Formation

Source: Nuruzzaman, 2015

Urban heat island is created due to a number of factors which contribute to

increase in land surface temperature, including low albedo materials (Bouyer et al.,

2009), human gathering, increased use of air conditioner (Okwen, 2011), destruction of

trees (Akbari et al., 2001), urban canopy (Masson, 2006), wind blocking (Priyadarsini,

50

2008) and air pollutants (Bousse, 2009). The effects cause discomfort to the residents of

the city center as they are devastating, specifically in arid and tropical regions in summer

time (Akbari et al., 2001). Improper planning of the cities is one of the reasons

contributing to intensity of urban heat island effect (Li, K. et al., 2012). Taha, (1997)

emphasizes that the intensity of urban heat island is added by the air pollutants realized

from power plants, industrial processes and exhaust gases omitted from vehicles along

with the anthropogenic activities generating heat. Urbanization is accelerating more than

half of total population on the planet, living in urban territories in 2008. This number is

projected to be increased up to 66% by 2050 (Zhou et al., 2016).

Figure 2.5: Effect of Urban Heat Island formation

Source: Nuruzzaman, 2015

The characteristics of urban heat island effect have been extensively studied in the

present research. Deosthali, (2000) claims that both heat and moisture are observed in the

heart of the city at night, whereas the day time is dry and hot island as the sun rises. The

maximum urban heat island is observed to be average in summer, while in winter, it is

strong (Kim and Baik, 2002; Zhong, 1996). The intensity of urban heat island was observed

to be correlated with the temperature of the country side; whereas the spatial extent was

identified to be independent of both temperature of the country side and heat island

magnitude (Streutker, 2002). Giridharan et al., (2004) conducted study in Hong Kong,

and observed daytime urban heat island effect in the high-density and high-rise residential

developments. He indicated that the architectural designs of the impervious structures can

consume less energy by maximizing cross ventilation, manipulating surface albedo, total

height of the building with sky view factor. Saaroni et al., (2000) materialized a new

method for monitoring urban heat island effect on different scales and from different

51

levels. It also enabled diverse thermal coverage characteristics and spatial assessment of

surface urban heat island effect in the cities.

Nieuwolt, in 1966, investigated LST and UHI of Singapore comparing airport area

(represented as rural area) with city area. The difference recorded was reported to be

3.5°C. It was investigated that the temperature difference was an offshoot of greater solar

radiation absorption and reduction in evapotranspiration in the urban areas (Nieuwolt,

1966). Chandler, (1960; 1965) observed that the mosaic of land surfaces influence

indirectly effects on urban heat island as the local airflow patterns are changed along with

a reduced diffusion of heat from courtyards, paved surfaces and waste heat omitted from

the industrial and anthropogenic activities. The similar complexity and heterogeneity of

pattern, composition and spatial extent of mosaic of land surfaces in the cities have also

been indicated to be contributing towards the local urban heat island effects in London

(Clarke and Peterson, 1972), Germany (Blankenstein and Kuttler, 2004), Hokkaido, Japan

(Shudo et al., 1997) and Hong Kong (Giridharan, 2004).

Rao, (1972) observed surface urban heat island through satellite-based sensors for

the first time. Since his usage, a variety of satellite sensor based combinations including

aircraft and ground based have been in use to observe surface urban heat island effect

over a range of different scales. Roth et al., (1989) observed the spatial distribution of

LST along the western coast of North America in several cities. It was observed that the

patterns of daytime intra urban thermal were significantly correlated with land use. On the

other hand, at night time, urban heat island effect was not much correlated with land use.

Akbari et al., (1992) considering the previous researches conducted, concluded

that the temperature is reduced by 0.5–5°C through urban parks and large number of

trees. Nichol, (1994) utilized satellite remote sensing techniques to investigate the LST in

Singapore and thermal images analysis showed that there was a difference of

approximately 4°C between the urban and rural areas temperatures. UHI observation in

Singapore demonstrated that the vertical building structures in the locality had changed

the urban climate of Singapore. Roth et al., (2000) mentioned in their research that UHI

was a result of land surface and atmospheric modification of climate due to urban

expansion, which could lead to severe social, economic and environmental consequences.

Likewise, the impact of urbanization on land surface temperature has been observed in

many cities of the world, and temperature is increasing day by day, as well as significant

52

variation between city and countryside temperatures has been noted (Nonomura et al.,

2009).

Progress in the field of Urban Heat Island detection still has issues to be resolved.

First of all, there is lack of latest technology in satellite imagery to obtain imagery at

night time (Nichol, 2005). After the sunset, most of the heat islands have highest

intensities, but owing to the inability of obtaining satellite imagery at the night time, it

becomes difficult to obtain data of study areas for complete urban heat island analysis.

Nichol, (2005) claims that urban heat island phenomenon is chiefly associated with night

time, but the analysis remains incomplete due to the lack of adequate temporal coverage

of study areas at nighttime. Secondly, the satellite sensors with high spatial resolution are

unable to record high temporal repeat times, necessary for cloud-free images from time

period of the study area (Harwood, 2008). Thirdly, some of the satellite sensors like

MODIS or NOAA-AVHRR, with high repeat times, lack the ability of spatial resolution

and fail in microclimate analysis. ASTER data was also acquired, for the point at which

urban heat island is magnified, close to the thermal crossover time, but again the

nighttime imagery did not support the predicted magnified UHI effect (Nichol, 2005).

Chen et al., (2006) explored the correlation between several indices and

temperature in the Pearl River Delta, situated in Guandong Province of south China. They

explored that the correlation is negative when NDVI is lower, while it is positive between

the LST and NDBI. They attributed the change in the temperature trends to the increasing

built up areas and loss in vegetal cover in the study area. Jusuf et al., (2007) investigated

urban heat island in Singapore and explored the impact of land use land cover types. It

was observed that the land surface temperature is decreased at day time in the following

order; Industrial areas, commercial centers, airports, residential areas and parks.

However, the order is reversed at the nighttime, it follows as; commercial centers,

residential areas, parks, industrial areas and airports.

Kottmeier et al., (2007) studied the impact of small-scale land use on LST. They

used block-related data and airborne surface thermometry of Berlin in 1998, during Berlin

Ozone Experiment. There was a positive correlation between the LST and the sealed

surfaces within blocks. Katpatal et al., (2008) investigated the LST patterns of Nagpur,

India. They observed the impact of Land use of urban areas on atmospheric temperature

and concluded the presence of impervious surfaces in the city that formulates the heat

53

surfaces and create a canopy layer heat island. Liang and Weng, (2008) conducted a

multiple scale analysis of urban heat island in Indianapolis USA. They observed a

positive correlation between the urban landscape parameters and land surface

temperatures. Yue and Xu, (2008) observed the population density, industrial areas, urban

buildings and impervious surfaces, diversity of urban landscape and types of underlying

surfaces contributed towards the urban heat island effect in Shanghai, China and changed

the urban thermal environment. Fujibe, (2010) observed the impact of population increase

on LST in Japan on various days of the week. He concluded that diurnal variations in

LST existed on the days when people commuted in and out of the city for job. A decrease

in atmospheric temperature was observed on holidays and weekend nights. These

variations in the atmospheric temperatures are observed at larger scales in big cities.

Viterito, (1991) concludes that these variations exist at smaller scales in small cities as

same impact factors do exist there.

Rapid expansion of urban centers in Japan initiates rising awareness for urban

warming. Urban warming has been found to have contributed significantly to feel

temperature changes (Fujibe, 2011). The US EPA, differentiates surface UHIs and

atmospheric UHIs. Sun oriented energy is retained and transmitted back to the

atmosphere by the physical land structures. This is supposed to be a principal cause of

warming surface and air temperatures, particularly in the canopy layer, which is nearest to

the surface (Rinner and Hussain, 2011). Shanghai is one of the greatest and fastest

growing urban areas in China. The endless growth of resistant manufactured surfaces in

urban areas has altogether impacted the urban thermal environment; unusual urban

modifications in the city are the main supporter of the Shanghai's city and countryside

zones temperature (Chen et al., 2015).

Satellite remote sensing is a modernized, efficient and helpful tool for researchers

to monitor and conduct research on actual problems faced by modern world. The data

collected through satellite imagery are utilized for the precise and reliable estimation and

assessment of land surface temperature and global climatic changes taking place and

predictions regarding changes to come in future. Therefore, estimation of Land surface

temperature and assessing its Spatio-temporal variations are not only supportive to

comprehend ecological and environmental processes, but are also concerned with the

well-being of people.

54

CHAPTER 3: MATERIAL AND METHODS

3.1. Introduction

The title of the chapter affirms the attempt to explain data sources, methods and

techniques used while acquisition of data, image pre-processing (layer’s staking, geo-

reference, geometric and radiometric correction), image processing, Spatio-temporal

analysis, impact assessment, mapping and presentation of the data. The methods used in

this study helped in acquiring the designed objectives and in responding to the research

questions of the study. This chapter gives us Spatio-temporal analysis of change detection

and helps us to draw conclusion on the expansion of the city and its influence on surface

temperature of Lahore. The data types used for the analysis have been categorized into

two groups, satellite remote sensing data and reference data.

The present study has utilized time series of Landsat satellite imagery, Landsat 5/

Thematic Mapper (TM), Landsat 7/Enhanced Thematic Mapper plus (ETM+) and

Landsat 8/Optical Land Imager (OLI) for the years from 1973 to 2015. Satellite remote

sensing images are used increasingly for the analysis of change detection of urban

expansion and estimation of land surface temperature because it is technologically

efficient and cost effective (Epstein et al., 2002). Reference data comprises aerial

photographs, topographical maps, and land use of the study area, shape files of

administrative boundaries, in situ temperature and census data. Geographic data (GPS

points) are also collected for all the land use type. These GPS points are utilized as

training sample during the image classification and accuracy assessment.

3.2. Data and its Sources

Data used for Spatio-temporal analysis in the present study include Landsat

satellite imagery, in situ atmospheric temperature measurements, and census data. In this

research, spatial data of sequential nature of land use type and urban expansion has been

accumulated from Landsat satellite imagery, topographic sheets and along with aerial

photographs of the study area. Monitoring of urban expansion and evaluating its impact

on LST via multi-temporal, multi-source remote sensing imagery have claimed great

interest in the recent decade (Gallego, 2004; Mayunga et al., 2007). Remote sensing is,

certainly, the most effective tool in monitoring urban expansion and detecting land use

55

changes with estimation of temporal variation of land surface temperature (Foody, 2002;

Alrababah and Alhamad, 2006).

3.2.1. Primary Data

Landsat satellite imagery is the primary source of data because of its temporal

resolution and free availability. In total, six Landsat satellite images were collected for

this research. Primary data is acquired through multi-source and multi-date satellite

images from 1973 to 2015 (Figure 3.1) provided the data source for the Spatio-temporal

analysis of the study. Urban expansion and consequent change in temperature are

assessed through Landsat 5/TM, 7/ETM+ and Landsat 8/OLI_TIRs images for the year

1973, 1980 1990, 2000, 2010 and 2015 respectively. These Landsat images are acquired

and downloaded from U.S. Geological Survey database and from the site of Global Land

Cover Facility (GLCF) (glcf.umiacs.umd.edu), according to the suitability and

availability due to cloud cover (the acceptable cloud cover should not be more than 10%).

GLCF helps in understanding the environmental system appropriately providing earth

science data and its products.

3.2.1.1. Satellite Images

Urban areas are ideally analyzed by high resolution satellite images but the

availability of such images is not easy and it costs very high. The selection of accurate

satellite system and right image for urban change detection is an art we can learn with

passage of time and experience. Nevertheless, resolution, time, availability, and cost are

some of the significant constituents for its data acquisition. The present study has utilized

the times series of Landsat (5, 7 and 8) imagery at different intervals, ranging from five to

ten years since 1973 to 2015. Images of moderate resolution like satellite Landsat

imagery are accessible free of cost from the USGS in Earth Explorer website, so the

images required for different time spans were acquired and downloaded from their

official site (http://earthexplorer.usgs.gov). These images were used to determine the

urban expansion and land use changes and also estimating land surface temperature of

Lahore. Satellite images of 1973, 1980, 1990, 2000, 2010 and 2015 were required for the

urban analysis (Figure 3.1). The satellite images were opted following the criteria defined

below (Sun et al., 2008):

1. 10% cloud coverage or cloud-free over the study area satellite images should be

acquired.

http://earthexplorer.usgs.gov/

56

2. The satellite images for the analysis should cover a long time span to maximize

the separability and distinguish the different land use changes and detecting

surface temperature (Tan et al., 2010).

Landsat 5, 7 and 8 data were used to carry out this research work. Landsat 5/TM

and 7/ETM+ sensor have 6 spectral bands with 1 thermal band and Landsat 7/ETM+ has

one panchromatic band with resolution 15m (Table 3.1). Landsat (5 and 7) images of

bands 1–5 and 7 have a spatial resolution of 30m (Chen et al., 2006). Landsat 5/TM,

7/ETM + imagery precisely the thermal infrared (band 6) with a spatial resolution of

120m and 60m respectively (Table 3.1) has been used for local-scale urban studies of

retrieving land surface temperature (Weng, 2002; Chen et al., 2002). Bands 1-5 and 7

were utilized for Land use type supervised image classification, while band 6 for land

surface temperature extraction in both cases Landsat 5TM and Landsat 7/ETM+ images.

Table 3.1: Metadata of Landsat (5, 7 and 8) Satellite Images

Year Sensor Bands Spatial

Resolution

Thermal

Resolution Path/Row

Date of

Acquisition

1973 MMS 1-4 60m - 160/38 23-03-1973

1980 MMS 1-4 60m - 160/38 04-03-1980

1990 TM 1-5 & 7 30m -

149/38 16-03-1990 6 - 120m

2000 ETM+

1-5 & 7 30m

149/38 19-03-2000 6.1 & 6.2 - 60m

Pan (8) 15m -

2010 TM 1-5 & 7 30 m -

149/38 07-03-2010 6 - 60m

2015 OLI

1-8 30m -

149/38 21-03-2015 Pan (9) 15m -

TIRs 10 &11 - 100m

Source: http://landsat.usgs.gov/

In the present research, Landsat 8/OLI_TIRs bands 10 and 11 are utilized to

evaluate land surface temperature with thermal resolution 100m and Landsat 8/OLI_TIRs

and spectral bands of 2, 3,4,5,6 and 7 with spatial resolution 30m and one panchromatic

band with spatial resolution of 15m (Table 3.1) are utilized to identify land use changes,

demarcating urban expansion. These spectral bands (2-7) are also utilized to generate

Normalize Difference Vegetation Index and Normalize Difference Built-up Index of the

study area (Rajeshwari and Mani, 2014). A detailed description of Landsat satellite

imagery and characters has been presented in Table 3.1. Lahore is covered in one tile

http://landsat.usgs.gov/

57

(Figure 3.1). Landsat (8) provides metadata of the thermal bands such as rescaling factor

value and thermal constant, which can be used for computing numerous algorithms like

LST (Rajeshwari and Mani, 2014).

The following satellite remote sensing imagery (Figure 3.1) has been used to

assess the urban expansion and estimating land surface temperature. They provide

beneficial input for mapping urban environment, monitoring urban expansion and city

planning because of their, temporal, spatial and spectral resolutions (Sadidy et al., 2009).

Figure 3.1: Imagery used for Urban Analysis


3.2.2. Secondary Data

Dependency on secondary data has also played a vital role in the present research

work. Books, articles, annual reports and latest research papers are used as a source for

secondary data. Demographic data from the PCO also contributed to validate the results

derived from the image analysis. Secondary data are required in order to investigate the

possible relationship between the indicators of urban expansion and its impact on

temperature respectively like population growth and areal extent, increased number of

factories and increased number of vehicles per year. The source for the secondary data

required includes;


58

1. For Population Growth:

a. Census of Pakistan:

i. City Reports of Lahore,

ii. District Census reports

iii. Punjab Development Statistics

iv. Economic Survey of Pakistan.

v. Pakistan Statistical Year Book

vi. Agricultural Census of Pakistan

vii. Excise and Taxation

viii. Board of Revenue Lahore

2. For Urban Expansion;

a. Survey of Pakistan Toposheets

b. Published Maps/ Reports of Lahore Development Authority (LDA)

c. Urban Unit Lahore.

d. NESPAK Lahore

3. For Atmospheric Temperature Trends

a. Pakistan Metrological Department (PMD)

b. Thermal Landsat Imagery

4. For Vehicles

a. Excise and Taxation Department Lahore

5. For Green houses Gases

a. Environmental Protection Agency (EPA)

3.2.2.1. In-Situ Atmospheric Temperature Data

For this study ground weather stations temperature data were obtained from

Pakistan Metrological Department (PMD) for the period from 1950 to 2015. Metrological

mean monthly data of minimum and maximum temperature data was obtained from two

observatories positioned in district Lahore. One observatory is located at Shadman (PBO)

Jail Road Lahore, which is massively urbanized area having impervious surface

structures, and the second is sited as Lahore Airport (APT), which is considered to be

59

rural vicinity having rural structures (Tables 3.2 and Figure 3.2). The distance between

the two met observatories is about 10 kilometers. In order to assess the temporal trend of

air temperature of Lahore, metrological data for mean monthly average with maximum

and minimum temperature data of these two observatories were obtained for the period

1950 to 2015, and examined through simple liner regression method in which time period

is utilized as independent variable while air temperature data is utilized as dependent

variable. The data covering the time period of nearly six decades were acquired in

centigrade scale. Temperature data on monthly basis were available from these two

ground stations for the period from 1990 to 2015 and the same date satellite thermal

infrared images acquired for the purpose of retrieval land surface temperature analysis.

Table 3.2: Ground Weather Station of Lahore Weather Station Weather Data Latitude Longitude

Shadman Lahore (PBO) 1950 to 2015 31°32'34.08"N 74°19'29.16"E

Lahore Airport (APT) 1953 to 2015 31°31'13.75"N 74°24'37.93"E

Source: PMD Lahore

Figure 3.2: Meteorological Station in Lahore


60

3.2.2.2. Census Data

Demographic data is utilized for examining the correlation between the

temperature and population density of the study area. Densely populated area and

industrial areas within the study area may have higher temperature owing to the

anthropogenic activities and built-up environment in Lahore. For the correlation analysis

between LST and population, data are obtained from population census organization,

Pakistan from 1951 to 1998 and Punjab Development Statistics from 2000 to 2015. The

data analyzed are temporally closest to the study’s time period.

Mostly statistical data are derived from multiple sources including all the censuses

held so for after the establishment of Pakistan, from the first conducted in 1951 and the

latest held in 1998 but as far as reliability and authenticity of demographic data is

concerned, the census of 1998 is considered the only reliable source. No doubt, this data

is not recent with reference to time scale (almost two decades old) but this is the only

latest data available in addition to population data gathered by different governmental

organizations from time to time. Likewise, socio-demographic data related to Lahore at

Union Council level is obtained from census report published by City District

Government Lahore. These secondary sources of data provided the demographic data and

information regarding to different administrating units.

3.2.2.3. Land use Data

Non-image data is one of the secondary sources of information utilized in this

research. These data have been derived from different topographical maps printed by

survey of Pakistan. A topographical map was required for the study to perform geometric

correction of satellite images from the Survey of Pakistan. Moreover, many land use maps

of Lahore i.e. 1947, 1966, 1974, 1980 and 1987 and projected land use map of 2021 have

been obtained from LDA and these maps have been utilized extensively in this research.

The most important data were acquired from Integrated Master plan of Lahore 2004

which encompasses LDA union council maps and details of Lahore land use type. In

addition, the present research is carried on GIS software which proved very helpful in the

preparation of the maps. The researcher used ArcGIS 10.1 to draw the maps. Local

boundaries of Lahore, administrative and provincial limits shape files were acquired from Urban

Unit Lahore, Lahore Development Authority and NESPAK office. Table 3.3 illustrates the

details of these data sets used for analysis.

61

Table 3.3: Data types used for the Study

Reference Data Date of Acquisition Scale Source

Topographic Maps

i. 35 L/1 & 35 L/13

Published 1957, surveyed

1925-1926 1:50,000

Survey of

Pakistan

Lahore Guide Map Published 2000,

Surveyed 1994-95 1:65,000

Survey of

Pakistan

Map of Greater Lahore 2004 - LDA

Land use Map 1966, 1980, 2004 - LDA

Analysis Zones Map 2006 - CDGL

The land use map of Lahore provides basic information regarding the numerous

aspects of urban expansion. Land use maps from Lahore Development Authority (LDA),

urban unit and NESPAK were utilized to update the data of land use modifications in

Lahore with the integration of Landsat Satellite images. Theoretical material, both

published and unpublished, from the housing and physical planning department Lahore is

utilized. It was pertinent to edit data on the basis of differences and similarities after the

collection of relevant data required. After the process of editing, different statistical

techniques were utilized to produce different maps.

3.3. Methodology

Research methodology is based on such scientific methods as are manipulated by

the researcher in a given situation for the purpose of data collection and deduction of

results from acquired facts and figures. Then researcher is able to draw some conclusions

and present recommendations for further research in that particular field. Assessing the

impact of urban expansion on land surface temperature is a complicated phenomenon

involving various activities. Likewise research involves the process of satellite images to

acquire precise, essential and exact information on the land use variation that the earth’s

surface environment is undergoing (Stemn, 2013).

The research method can be categorized into three main portions; (1) data

collection and pre-processing, (2) data processing (3) and data analysis. For the present

study the following methodology (Figure 3.3) is adopted which involves several satellite

remote sensing data processing procedures used in order to carry out this research

including; image processing, classification of the imagery, urban expansion change

62

detection analysis, retrieval of land surface temperature and calculating NDVI and NDBI

indices. The Figure 3.3 summarizes the whole methodologies that were exercised during

the execution of the present work involved in the research.

Figure 3.3: Flow Diagram of Research Methodology

Minallah, 2016

All these portions and their subparts are elaborated in the Figure 3.3 above.

Satellite imagery of different dates was processed separately, at the first step image

classification has been completed to retrieve LST and calculate NDVI and NDBI. The

results of land use classification were also utilized in order to produce land surface

63

temperature map (Figure 3.3). These steps belong to the data processing portion which

results into the making of land use type and maps of variation of land surface temperature

maps. The analysis portion comprises LST-NDVI linear Regression and Land use-LST

composite.

For the purpose of correlation study, three different data time series of MMxT,

MMiT, MAT, as well as selected urban expansion parameters i.e. number of vehicles,

population growth, number of factories and greenhouse gases for the specific area under

study are analyzed by statistical methods. A statistical test gives scholar fundamental

insight for formulating decisions quantitatively for a process or processes subsequently.

To observe the significant change in temperature trend with respect to passage of time

and for finding out its causes, linear regression and Pearson correlation were applied

respectively (Seber, 2012).

3.3.1. Image Pre-Processing

Image pre-processing is the most important part of satellite remote sensing

imagery processing and analysis, having impact on final product quality and further

processing. The aim of digital image pre-processing is to reinstate suitable image from the

distorted raw image. Image pre-processing typically consists of a series of processes

including; geometric, radiometric correction and registration of image (Lillesand et al.,

2007). To attain precise urban expansion, retrieving land surface temperature and

calculating NDVI, multi-temporal, Landsat images must be pre-processed both

radiometrically and geometrically to correct errors arising from the earth’s curvature,

imaging sensors and due to atmospheric effects (Schroeder et al., 2006). Image Pre-

processing techniques, which are sometimes termed as image rectification and restoration,

are proposed to correct sensor and platform specific radiometric and geometric distortions

found in satellite imagery

Landsat satellite images were acquired and downloaded in “GeoTIFF” image

format along with each spectral band layer in separate file. Before further images

processing, all band layers of each Landsat image were stacked in one image and the

image was thus converted into “.img” format, excluding unnecessary bands by using

ERDAS imagine 9.2. Layer stacking of different bands of each image is important

procedure for the production of a false color composite satellite image (Horning, 2009).

64

Table 3.4: Description of Landsat Imagery Spectral Resolution

Satellite Sensor Spectral Bands Spectral Range Scene

Size

Pixel

Resolution

L 5 TM multi-spectral 1,2,3,4,5,7 0.45 - 2.35 µm

185 X

185

km

30 meter

TM thermal 6 10.40 - 12.50 µm 120 meter

L 7

ETM+ multi-spectral 1,2,3,4,5,7 0.450 - 2.35 µm 30 meter

ETM+ thermal 6.1, 6.2 10.40 - 12.50 µm 60 meter

Panchromatic 8 0.50 - 0.90 µm 15 meter

L8

OLI (Coastal aerosol) 1 0.43 - 0.45 30 meter

OLI multi-spectral 2,3,4,5,6,7 0.45- 2.29 30 meter

OLI (Panchromatic) 8 0.50 - 0.68 15 meter

OLI (Cirrus) 9 1.36 - 1.38 30 meter

TIRS 10 10.60 - 11.19 100 meter

TIRS 11 11.50 - 12.51 100 meter


The Landsat satellite images were attained as standard products, i.e.

radiometrically and geometrically rectified. With the comparison of classified Landsat

images of the study area were used to detect the urban expansion and land surface

temperature variation. For this purpose, images classification and LST retrieved were

requisite (Jensen, 2004; Bhandari, 2010). Landsat 5/TM, 7/ETM+ and 8/OLI 1-5, 7

spectral bands were utilized for LULC image classification while band 6 of TM image or

in case of Landsat 7/ETM+ 6.1 and 6.2 band and in case of land 8/TIRS band 10 and 11

band containing thermal infrared data were utilized to retrieve LST (Landsat 7 Science

Data Users Handbook, 2007).

3.3.1.1. Geometric and Radiometric Correction

Geometric correction helps to remove the geometric distortions of the raw image

data due to the perspective of the optical scheme, the motion of the sensor and terrain

factors. Geometric correction in general, re-projects the satellite image to the suitable

projection and geographic coordinate system from the sensor’s projection. Many

researchers utilize the correction level (1T & 1G) provided by United State of Geological

Survey (USGS), while other researchers accomplish their own geometric correction on

Landsat satellite images (Yang et al., 2003; Li, et al., 2011). By utilizing the United State

of Geological Survey (USGS) standardized geometric correction level (1T & 1G) offered


65

a systematic accuracy across space and time which is tough for individual researchers to

match this level (1T).

For the present research work downloaded Landsat image scenes were geo-

rectified and offered by USGS to the Standard Terrain Correction Level (1T) and no

additional geometric correction execution on Landsat imagery (Hestir, 2011) was

required. Data taken was normalized by radiometric correction at different time and

location in order to receive same pixels of spectral values. These steps are responsibility

of the company handing over the data for the user (Lillesand et al., 2007). Radiometric

correction techniques are used to remove the inconsistencies between spectral reflectance,

spectral radiation brightness of the object and pixels recorded by sensors (Jianya et al.,

2008).

In the present research, no geometric and radiometric correction was done,

because the datasets acquired from USGS were correct to some extent. Radiometric

corrections sometimes become needless with regards to change detection based on object

or feature comparison (Jianya et al., 2008). In case of Landsat 5/TM, 7/ETM+ and 8/OLI

imagery, data originates in Level 1T and Level 1G processing levels were available. This

means that Landsat image data is already with geometric and radiometric correction and

geo-referenced to UTM map projection. Digital Elevation Model (DEM) is also employed

by level 1T, to improve topographic correction (Landsat 7 Science Data Users Handbook,

2007).

3.3.1.2. Generating Subset Images

Landsat image scene had much larger extent than the study area of interest (AOI).

In order to illuminate any possible errors or to reduce file size in image classification,

original images were clipped and subset from the complete scene by utilizing the vector

layer of Lahore. Lahore is covered in only one scene (Figure 3.1). All composite false

color images were subset (Figure 3.4) into the study area of interest by using ERDAS

Imagine 9.2 (Jensen, 2004). ERDAS Image 9.2 basically provides two method of subset

image; that is by specifying the rectangular area extent and utilizing either an area of

interest tool (Bhandari, 2010). The present research work used AOI tool in order to

generate the subset images.

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3.3.1.3. Image Enhancement

In order to increase the visual interpretation of the satellite images, which is

pertinent, all the subset images of the study area were enhanced. The image cells are

highlighted and become prominent for easy recognition and better comprehension of the

image features by using image enhancement techniques. Such techniques of image

enhancement help in identifying land use classes and selecting area of interest practically.

Figure 3.4: Spectrally Enhanced Subset Images Showing Study Area


The histogram equalized stretch was selected to enhance the satellite images

among all the available image enhancement techniques (Shalaby and Tateishi, 2007).

Every subset image used in the research was enhanced utilizing histogram equalization

technique with the aid of ERDAS Imagine’s 9.2, histogram computation tool is used in

order to enhance the volume of visual information. This method is extremely vital in

identifying ground control points and rectification (Zemba et al., 2010). The five

spectrally enhanced satellite images were utilized for the image classification and re-

classification of the built-up area (Kaiser et al., 2008), as shown in Figure 3.4.

3.3.1.4. Bands Combination for Visual Interpretation

Source Landsat 5/TM, 7/ETM+ and 8/OLI_TIRs imagery used for this study

enclosed 7 and 9 spectral bands respectively. For the purpose of demonstration, 3 bands


67

of Landsat imagery were preferred which were very supportive in the collection of

training areas. Different false color band combinations were used to classify different

land uses.

Table 3.5: Band combinations in RGB comparisons

Image RBG Landsat 7 & 5 Landsat 8

Color Infrared 4, 3, 2 5,4,3

Natural Color 3, 2, 1 4,3,2

False Color 5,4,3 6,5,4



Source: http://landsat.usgs.gov/L8_band_combos.php

For example, 742 and 743 as RGB were used for identification of built-up

structure and water bodies respectively and Similarly 543 as RGB was utilized for better

recognition of vegetation cover and water. Some other band combinations of satellite

image such as 741, 321 as RGB were also considered for precise decisions about different

land uses variations.

http://landsat.usgs.gov/L8_band_combos.php

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3.3.2. Image Classification

Image classification is a procedure whereby all the pixels of satellite imagery are

characterized into land use land cover classes and further thematically classify each pixel

of the satellite image, based on its spectral response (Lillesand et al., 2004). Digital image

classification technique is among the most helpful procedure of satellite remote sensing

image analysis, which can be utilized for land use (LU) mapping as well as socio-

economic and other environmental applications (Lillesand et al., 2007).

Digital image classification procedure is very complicated as various factors have

to be taken into account. Main steps of images classification include; selection of the

most suitable image classification technique and its process, finding training area,

classification theme and selection of a class scheme suitable for the study area, image

classification itself and post-classification method with classification accuracy

assessment.

Image classification scheme is typically designed and based on the desired result

and the input satellite data. In case of land use mapping, the spectral pattern recognition

scheme is most appropriate. Numerous types of land use retain different blends of

radiance values forming distinctive spectral patterns (Matinfar, 2007). This system

utilizes recognition of such a pattern pixel-by pixel. Such technique helps in acquiring the

significant information about the real world. This kind of information determines to make

the thematic maps with availability of detail about the entity such as land use type;

vegetation cover, bare land, built up land and water (Bhandari, 2010).

In the broader perspective, spectrally oriented two main image classification

techniques are recognized: supervised classification and unsupervised image

classification. The information about the entities attained through the spectral reflectance

has been used by the supervised image classification in order to explain the guidance data

for designing the categories of image classification (Ratanopad and Kainz, 2006).

Supervised classification investigates spectral inconsistency which is defined

productive information according to the category. Whereas the later approach is

unsupervised image classification, an automatic classification system, where training

samples are not required at all. Unsupervised classification technique is mainly helpful in

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making classification of the unknown areas and to evaluate the number of classes for

further supervised image classification (Kaiser et al., 2008). In remote sensing

application, supervised image classification approach has been widely used (Yuksel et al.,

2008).

Figure 3.5: Flow Chart of Image Classification Process

Minallah, 2016

3.3.2.1. Supervised Classification

The present study has adopted supervised classification technique for the time

series of Landsat images of 1973, 1980, 1990, 2000, 2010 and 2015 and set it forth as a

base data for the Spatio-temporal exploration of land use variations and determining

expansion of urban area of Lahore. In Supervised classification method, the researcher

defines the category and the numerical description in terms of computer algorithm, to

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determine the class to which each pixel belongs. The classes were defined for the area of

interest, to have a universal classification scheme for the study.

Figure 3.6: Basic Steps in Supervised Classification

Source: Lillesand et al., 2007

Keeping in view the demography of the study area, based on prior knowledge and

literature review, four classes were defined as follows: built-up area, vacant land,

vegetation, and water bodies for the purpose of Spatio-temporal analysis. After the

supervised classification procedure, Universal color scheme was also employed and

conventional colors were assigned to each and every class. Blue color was allotted to

water bodies, dark green for vegetation, light golden for vacant land and red color was

allotted to built-up area. Table 3.6 is a description of the various land use classes.

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Table 3.6: Description of the Land use Classification Scheme used in the Study Level I Level II

Description Main land use Sub land use Class

URBAN Urban/Built-up Area

All infrastructure: commercial, residential,

industrial areas, mixed use, and man-made

structures. It comprises areas of intensive use

with much of the land covered by structures.

Included in this category are cities, towns,

villages, settlements, pavements, road network,

highways and transportation, power, and

communications facilities.

WATER BODY Water and Wetland

This consists of areas persistently covered with

water; provided that if linear they are at least

200m wide. This category includes; River,

lakes, permanent open water, ponds, streams

and canals, lakes, reservoirs, bays and

Estuaries.

NON-URBAN

Vacant Land

Construction sites, excavation sites, developed

land, Fallow land, earth and sand land

infillings, open space, bare soils, and the

remaining land cover types. Barren Land is

land of limited ability to support life and in

which less than one-third of the area has

vegetation or other cover. Land area that is

non-cultivable and has no specific land use and

any other radiating land surface.

Vegetation

Natural vegetation, Trees, mixed forest,

gardens, playgrounds and Parks, vegetated

lands, grassland, crop fields and agricultural

lands. Agricultural Land may be defined

broadly as land used primarily for production

of food and fiber. This category includes;

Cropland and Pasture, Ornamental

Horticultural Areas.

Source: Anderson Classification System, 1980

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3.3.2.2. Training Stage

In order to complete the supervised image classification procedure, the researcher

selected some training samples for the description of spectral characteristics of each land

use class. Selection of appropriate training samples is most important phase of supervised

classification, as each pixel of the satellite image is numerically associated to the training

set. Each pixel is allotted to a land class after considering the results of the comparison,

therefore, the training areas must be selected as accurate as possible to avoid confusion

between misclassification and the classification scheme. It must be attempted to get

training areas selected from the high resolution of satellite images or from the collected

fieldwork.

The training areas in the present research were selected with maximum accuracy

from the high resolution satellite images and also from the fieldwork. Google Earth high

resolution images were used to check each training area belonging to a specific class

(Lilesand and Kiefer, 2002). A difference is noted in the training sites from class to class

and on average, 100 pixels of training areas were nominated for each type of class. The

types of classes selected for classification of urban land use are as built-up area, vacant

land, vegetation and water bodies (Yuksel et al., 2008). Enhanced images are used for the

selection of training sites for each year, 1973, 1980, 1990, 2000, 2010 and 2015.

Region growing tool of ERDAS imagine was employed to enhance the coverage

of training area and to intensify the number of pixels for each training site. There were

several types of softwares such as Online Google Earth, Arc GIS and ENVI used to

integrate the digital topographic map with high resolution satellites images of study area

for obtaining training sites and reference to identify urban land cover changes from

images.

In the present research, the training sites obtained from the satellite images are

shown in Figure 3.7 used for classification. If the main emphasis of the study is to

investigate the urban expansion, detailed land use land cover maps are not required and

“simple binary classification” from satellite data meets the needs of the analysis by

focusing on classes of urban and non-urban land use. To ensure accuracy assessment, the

classified images are matched with land use and topographical maps of Lahore with

ground checks and relevant time period.

73

Figure 3.7: An Example of Training Samples on an Image

Minallah, 2016

3.3.2.3. Classification Stage

There are various parametric rules involved in decision designing processes of

supervised image classification, such as Mahalanobis distance, Maximum likelihood and

Minimum distance algorithms and these are available in the ERDAS Imagine 9.2 (Lu and

Weng, 2007). For the purpose of Land use mapping, good classification results can be

achieved by using the algorithm of Maximum Likelihood. In the present study, among the

three parametric rules the algorithm of Maximum likelihood was ideal and useful in this

research work because of the provision of the good classification results, comparatively to

the rest of two algorithms namely Minimum distance and Mahalanobis distance

algorithms. Maximum likelihood algorithm is simpler and provides reliable results. This

method was also useful for reclassification of classified images for built-up area

extraction in the existing research exertion. Supervised classification by using algorithm

of Maximum likelihood assumes that the probability of a given pixel falls in a specific

74

class in which stats for every class in every band are normally spread. It means that a

pixel is allocated to a specific class with the highest probability (Richards, 1999).

3.3.3. Classification Accuracy Assessment

Image classification is not valid without its accuracy assessment. There were

several reasons responsible for occurrence of errors and they come not only from the

image classification itself, but poorly selected training areas and also from image

registration etc. Therefore, it is very important to conduct an assessment of classified

result. Accuracy assessment assumes all differences between image classification

consequences and reference data derivation from the image classification errors

(Congalton and Green, 1999). The most common procedure of assessing the image

classification accurateness is confusion/error matrix (Fig. 3.8). The confusion/error matrix

encompasses a category comparison of relationship between known, ground-truth and

result of classification.

Figure 3.8: Flow Chart of Image Classification Accuracy Assessment Process

Minallah, 2016

The overall accuracy shows the accuracy process of image classification. Overall

accuracy is evaluated in percent and denotes the number of pixels properly categorized

75

and divided by the total number of pixels. Producer’s and User accuracy can be well

defined in relation to the error of omission and error of commission. Producer’s accuracy

is confined to the percentage demonstration of the specified class which can exactly be

recognized on the map. Whereas User’s accuracy is the presentation of the probability

that the specified pixel executes on the ground as it has been classified.

Kappa coefficient is a statistical quantity of agreement and measures the overall

results of image classification. The kappa measure of statistical agreement integrated off-

diagonal elements of the confusion/error matrices that were image classification error. It

also supplements in representing acquired agreement after excluding number of classes

which cause to occur error if there is strain with confusion matrix and kappa coefficient

(Foody, 2002).

Several procedures have been adopted for the evaluation of the classified images

through accuracy assessment. In the present study, some of these were practically applied

and by using the stratified random sampling techniques 270 points were collected for

comparative analysis between the each classified image and comparative assessment with

images acquired from the Google Earth and land use maps of Lahore. All these selected

points indices set forth the fundamentals to evaluate the precision assessment of the land

use classification. All classified images were vectorised into polygons after the

measurement of accuracy.

3.3.4. Post-classification Change Detection

The most effective approaches to analyze change detection processes in urban

areas include change detection algorithm and Post-classification comparison (Nadoushan

et al., 2012). After the processes of classification of images, it is often observed that the

pixels are misclassified. In order to reduce the effects of “salt and pepper” appearance of

such misclassified pixels of the images, post-classification results smooth the dominant

areas under land use. To further eliminate the salt-and-pepper effects in classification,

Median filtering by 3*3 pixels has been utilized. One of the other post-classification

techniques used in the present research is post classification change detection. The

purpose of the current research is to identify the land use changes in the study area over

approximately 42 years. There are many techniques of change detection available. In the

present study, post-classification change detection was selected because, unlike other

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techniques of change detection, it is noteworthy that, the accuracy of this technique is

extremely dependent on the overall accuracy of classification of images used for

comparison. The consequences of post-classification change detection step include:

statistics report of change detection, and a map of change, with recognized classes to

which pixels have changed in the final date image.

3.3.5. Urban Expansion Change Detection

Urban expansion change detection for 1973 to 2015 is recorded by the usage of

Landsat TM, ETM + and OLI_TIRs data. In order to scrutinize the rate, amount, nature

and site of land expansion and land use conversion, an image of built-up structure is

extracted from satellite images. The excerpted images are overlaid to document land use

change (enlargement) image. It is further overlapped with numerous geographic reference

maps to analyze the patterns recurrent in urban expansion, including shape file of Lahore

limits and main roads. Qualitative analysis is utilized to show the measure of land use

changes which reason the intensification of urban surface temperature and its likely

reasons. For this purpose, the thermal satellite images and land use map of Lahore are

overlaid and zoomed into the target area, by Google Earth especially the hot spots

(Google, 2007).

Thermal Landsat satellite images display different temperatures in different colors

using red (hot) for high temperature and blue (cool) for low temperature. The area

congested with high density of building was displayed through more reddish color (Jusuf

et al., 2007); on the other hand, greenish will signify areas consisting of dense vegetation.

The influence of urban expansion and various land use on the urban land surface

temperature in Lahore is investigated through quantitative analysis. The urban land

surface temperature spreading through different types of land use is investigated through

making comparisons of land practice with Lahore thermal imagery taken by Landsat 5, 7

and 8 respectively from 1973 to 2015.

3.3.6. Methods of Retrieving Land Surface Temperature

LST is the radiative skin surface temperature of the land, which always plays an

imperative role in the physics of the land surface through the process of water exchanges

and energy with land to the atmosphere and determining surface radiation (Xiao et al.,

77

2008; Srivanit, 2012; Mallick, 2014). Numerous researches have been completed on the

relative hotness or “UHI effects” of metropolises by assessing the air temperature, by

metrological data.

Traditional techniques of obtaining data on temperature include; direct

observations using local meteorological weather stations. Though these weather stations

measurements have a high temporal resolution, they are expensive, time consuming and

have problems in spatial interpolation as they have local and point coverage. Satellite

sensors are capable of providing quantitative physical data at high temporal and spatial

resolutions (Fig. 3.9).

Figure 3.9: Process of Land Surface Temperature Retrieval

Minallah, 2016

78

This repetitive coverage is proficient in measuring earth surface condition

overtime (Gangulya & Shankar, 2014). At the same time, satellite remote sensing

imagery covers large areas and satellite data is much cheaper and easy to get. Nowadays

thermal infrared data is most commonly used to retrieve LST. This technique is most

convenient and suitable for the studies of urban climatology (Weng, 2009). Estimation of

land surface temperature from satellite images has been extensively utilized for urban

climate studies and adopted by many researches for studying climatology and satellite

remote sensing data available on a regular basis and free of cast. A range of algorithms

has been established to estimate LST from Landsat 5/TM, 7/ETM+ and 8/TIRS imagery,

such as single-channel method, mono-window algorithm (Munoz and Sobrino, 2003) and

radiative transfer method (Qin et al., 2001). In the present research, the radiative transfer

method is utilized to estimate the land surface temperature of Lahore.

3.3.6.1. Brightness Temperature Retrieval

To measure the land surface temperature and temporal change of temperature

from 1990 to 2015, the thermal infrared images of Landsat 5/TM, 7/ETM+ and 8/TIRS

were utilized in order to acquire surface temperature map and identify the thermal urban

environment of Lahore during 1990 to 2015. The thermal band 6 of Landsat 5/TM, 6L &

6H thermal band of 7/ETM+ and thermal bands 10 & 11 of Landsat 8/TIRs images, with

a spatial resolution of 120m, 60m and 100m respectively were used (Yuan et al., 2005;

Ma et al., 2010). They are considered suitable for taking the complex urban temperature

differences which make it possible to identify for an effective analysis of the urban

climate of the study area. The thermal Infrared bands of Landsat images were utilized to

transform the raw value into the black body temperature in Celsius Degree by using

ERDAS and ArcGIS (Joshi and Bhatt, 2012). In order to retrieve brightness surface

temperature (Fig. 3.9) three stages are given by Hashim et al., (2007).

i) Conversion of the Digital Number (DN) to Spectral Radiance (L)

L = LMIN + (LMAX - LMIN) × DN / 255 Equation No. 3.1

Where

L = Spectral radiance

79

For Landsat 5/TM

LMIN = 1.238 (Spectral radiance of DN value 1)

LMAX = 15.600 (Spectral radiance of DN value 255)

DN = Digital Number

For Landsat 7/ETM+

Band 6L = LMAX= 17.04 and LMIN= 0.0

Band 6H = LMAX= 12.65 and LMIN= 3.2

The values are obtained from the metadata header files.

For Landsat 8 can be expressed by

Lλ = ML × QCAL + AL Equation No. 3.2

Where ML stands for band multiplicative rescaling factor

(RADIANCE_MULT_BAND_DN) from the metadata (Table 3.7). On the other hand,

QCAL calibrated and quantized standard pixel values (DN), AL stand for band specific

additive rescaling factor (RADIANCE_ADD_BAND_DN) from the metadata as shown

in Table 3.7 (USGS Landsat 8 product, 2013).

Table 3.7: The Metadata of Landsat 8-TIR Rescaling Factor Band 10 Band 11

Radiance Multiplier (ML) 0.0003342 0.0003342

Radiance Add(AL) 0.1 0.1

Source: Sameen and Al Kubaisy, 2014

At the second stage, the radiance was converted to Brightness surface temperature

by using the Planck curve (Eq. 3.3) specific estimation from Landsat infrared images is

given by Chander and Markham, (2007).

ii) Conversion of Spectral Radiance to Brightness Temperature in Kelvin

Tk = K2 / 1n (K1/L+ 1) Equation No. 3.3

Where Tk stands for temperature measurement in Kelvin, K1 refers to prelaunch

calibration of constant 1 in unit of W/(m2 sr·µm). K 2 is the prelaunch calibration of

constant 2 in Kelvin as shown in Table 3.8.

80

Table 3.8: Detail of Calibration Constant Calibration

Constant

Landsat 5/TM Landsat 7/ETM+ Landsat 8/TIRs

Band 6 Band 6/1 Band 6/2 Band 10 Band 11

K1 607.76 666.09 mWcm^2 775.89 480.89

K2 1260.55 1282.71 1321.08 1201.14

Source: Sameen and Al Kubaisy, 2014

The final phase brightness temperature on Celsius (°C) can be calculated by using

following equation:

iii) Conversion of Kelvin to Celsius

TB = TK - 272.15 Equation No. 3.4

Where TB is the Brightness surface temperature in Celsius (°C), Tk is the surface

temperature in Kelvin (K) (Joshi and Bhatt, 2012).

3.3.6.2. Method of Derivation of Normalized Difference Vegetation Index (NDVI)

The NDVI is used by various researchers (Gao, 1996; Myneni et al., 2001) in

order to distinguish the land use type in the study area and it enables the analyst to

identify the correlation between land use changes and measure LST quantitatively (Zha et

al., 2003). The index value is closely associated with climatic variables, such as

precipitation and sensitive to the existence of vegetation cover on the Earth’s land surface

(Schmidt and Karnieli, 2000). In the present study, NDVI is used to observe the

relationship between Land surface temperature and vegetation cover. NDVI in Eq. (3.5)

given by Purevdorj et al., (1998), generally exploited to determine the thickness of

vegetation cover.

NDVI = (NIR - RED) / (NIR + RED) Equation No. 3.5

Calculations of the Normalized Difference Vegetation Index for a given pixel

always result in ranges from -1 to +1; indicating a value close to zero reflects absence of

greenery and a value touching +1 shows maximum density of greenery. The Normalized

Difference Vegetation Index is utilized to categorize different types of land use for

example green spaces, built-up and water, Index value ranges for all these land use

categories are inconsistent and they show variations in diverse environments and

different regions. The resulting raster file holds values ranging from -1 to +1, where

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values around 0 relate to barren land, -1 means water, and those approaching +1 relate to

healthy and thick vegetation cover (Weier and Herring, 2008). In other words, the

Normalized Difference Vegetation Index (NDVI) value is associated to Land use classes.

Linear regression between LST and NDVI has been designed.

3.3.6.3. Land Surface Emissivity (LSE)

In this present study, the procedure of land surface emissivity assessment from the

NDVI given by Sobrino et al., (2004) and Sobrino et al., (2008) has been applied. LSE

(ε) can be extracted by using NDVI considering three different cases such as

1. Bare surface

2. Abundantly vegetated and

3. Mixture of bare soil and vegetation

0.979-0.035PR NDVI < 0.2

ε = 0.986+0.004PV 0.2 < NDVI < 0.5

.99 NDVI > 0.5

The pixels were divided, under this method, into three categories according to the

values of NDVI. If NDVI values exceed 0.5, the pixels are supposed to be covered

entirely by vegetal cover. Under such cases, the ε equal, 0.99 were assigned to them. If

NDVI values range from 0.2 to 0.5 in pixels, the Proportional Vegetation Cover (PV) was

estimated using the following equation 3.6.

Pv = [(NDVI - NDVImin) / (NDVImax - NDVImin)]2 Equation No. 3.6

Finally the emissive (ε) was acquired from simple linear regression, using the PV

values of equation 3.6 and estimation of emissive using equation 3.7.

LSE (ε) = 0.004 × PV + 0.986 Equation No. 3.7

3.3.6.4. Land Surface Temperature Retrieval

If the values of emissive are known, land surface temperature can be determined

by using simple formula 3.8.

82

LST= (TB/1+ λ*(TB / ρ)*ln (ε)) Equation No. 3.8

Where

TB= Satellite brightness temperature

λ = Wavelength of emitted radiance (11.5 µm)

ρ = h*c/ σ ______equation (1),

where

h= planck’s constant having value (6.626*10^-23 js)

σ = Blotzmann constant (1.38*10^8 m/s)

C= velocity of light (2.998*10^8 m/s)

Put the value of h, c, and σ in equation (1) we will get the value of P which is

“14380”, and then put this value in main equation.

3.3.6.5. Thermal Map Generation

The map for land surface temperature has been arranged by using appropriate

colour ramp in symbology to estimate the difference of land surface temperature. Thermal

image differencing procedure is employed to detect the degree of the land surface

temperature change (Joshi and Bhatt, 2012) during the time phase between 1990-2015.

3.3.7. Relationship between LST and Land use

In order to relate land use classes with LST, the land surface temperature maps

were filtered using Mode filter and vectorized. After the process of vectorization, both

Land use map and Land surface temperature vector map are imported to ArcGIS 10.1.

There, the Land use map was overlapped with the corresponding land surface temperature

vector. ArcGIS has been used due to its better competency for the visualization of data.

This process allowed analyzing whether the Land use classes match Land surface

temperature classes. However, the results of such overlapping are very difficult to

interpret.

Land surface temperature and land use maps of the study areas evidently explain

the correspondence of LST slices and their change in temperature from one date to

another. The operation mentioned above in methodology flow chart is specific in

83

analyzing LUC-LST relationship visually. The other alternative option is to collect

statistical data of LST and land use. For each land use class, maximum, minimum, mean

LST and its standard deviation have been calculated. Due to the nature of Land use map,

it was not possible to estimate linear regression between LST map and LUC map. An

answer to that substance is to calculate NDVI instead of land use classification result.

Normalized Difference Vegetation Index provides information about thickness of

vegetation cover and its distribution. NDVI has been calculated from Landsat imagery.

3.3.8. Regression Analysis Determining the Relationship between NDVI,

NDBI and LST

The present research utilizes linear regression analysis to estimate correlation

between land surface temperature and land use classes on NDVI and NDBI (Fig. 3.10).

NDVI and NDBI are independent variables and land surface temperature is dependent

variable. The equation of linear regression is given by Seber (2012), as under:

Y = α + βx Equation No. 3.9

Where

Y stands for the value of the dependent variable estimated from the linear

regression model.

α stands for the coefficient freedom reflecting y dependent on x.

β stands for the angel coefficient (slope) of regression line, also reflecting the

change of y variable and x variable increase one unit.

x stands for the independent variable ( NDBI, NDVI)

R2 is the coefficient of determination of the variable y, with respect to change of

the variable X. The range for R2 is 0 to 1. The more the value of R2 is, the more

dependent variable Y on variable X is.

To observe the relationship and Correlation among LST and NDVI, NDBI, about

50 random sample point sites were chosen by using Feature Class “Create Random Point

Tool” for each NDVI, NDBI and LST image (Fig. 3.11) and then by using ‘Extract Multi

values to point’ tool in ArcGIS 10.1 to get values of LST and NDVI, NDBI.

84

Figure 3.10: Regression Analysis Flow Chart

Minallah, 2016

Figure 3.11: Random Sample Points for Relationship between LST & NDVI

Minallah, 2016

Landsat 5/TM, 7/ETM+ and 8/OLI Images

Image Correction, Preprocessing

Retrieving NDVI and NDBI (Red band, near infrared and mid infrared)

Composite and subset image of the

study area

Retrieving LST Thermal band (6, 6.1., 6.2 and10, 11)

Supervised Classification Maximum Likelihood

Accuracy Assessment

Land use Map

Regression Analysis

Extracting LST for Each Land

use type

Relationship between LST and

NDBI and NDVI

85

3.3.9. Atmospheric Temperature Trends from 1951 to 2015

The time series Ground observatory data of Mean Annual Temperature (MAT),

Mean Maximum Temperature (MMxT) and Mean Minimum Temperature (MMiT), as

well as selected urban expansion parameters i.e. growth of population, change of land

use, increase number of vehicles, increased number of factories and increased greenhouse

gases since 1950-2015 are analyzed in this study by statistical methods (Table 3.9).

Linear regression and the Pearson correlation were applied (Table 3.9) for analyzing

significant changes in temperature trend in particular span of time and to find out the

causes of such changes.

The present research is aimed at finding out the factors and reasons for

temperature change and locating the contributing factors existing between natural and

anthropogenic activities. The determined trends exist in a certain magnitude. First of all,

the data are compiled and MAT, MMxT, MMiT were computed on mean monthly and

annual basis. The data are compiled afterwards and scatter plots are used to analyze

structures of temperature trends by using SPSS 20 in order to select the test type, i.e.

Kendal tau, spearman of linear data to examine time series of temperature trends.

The variables in graph of the data after plotting are observed from the lower left-

hand corner to the upper-right edge. Simple Linear regression test was utilized to analyze

the temperature trend during the study span. When the data is parametric, the Simple

linear regression analysis provides the statistical test for the inquiry of relationship

between different variables. Mostly, the researchers investigate the causal effects of one

such variable upon another. It also helps in determining the relationship that exists

between the variables in a lucid manner. It also estimates the value of intensification

between both the variables, independent and dependent.

On the other hand, employing another statistical test, Pearson correlation is used

to investigate the causal relationship between different variables to find out contributing

factors. The statistical values in Pearson correlation, value ranges between ±1 and +1

show positive correlation, indicating an increase in both the variables. Similarly, a

negative correlation in the variables indicates that increase in one variable causes

decrease in the value of the other variable. In this study, the hypothesis is; “Multifarious

factors cause change in temperature trends in Lahore”. To conduct linear regression along

86

with Pearson correlation (Table 3.9) on MAT, MMxT and MMiT data time series is as

under;

Table 3.9: Procedure of Applied Statistical Test

Datasets Analysis Statistical Test Procedure

A MAT, MMxT and MMiT

Trends for

6.5

decades

Simple Linear

Regression

Analyze

Regression

Linear

B

Indicators of Urban Expansion

Population Growth

Built-up area Increase

Reduction in Agricultural

land

Increase no. of Vehicles

Increase no. of Factories

Increase Greenhouse Gases

Causes Pearson’s

Correlation

Analyze

Correlation

Bivariate

Minallah, 2016

A. MAT Trend analysis for whole phase (1950-2015):

I. Dependent: (a) MAT; (b) MMxT; (c) MMiT

II. Independent(s): Year (Time Unit)

In the section of analysis, quantitative measurement of annual temperature, along

with its various parameters, are utilized as dependent variable and time period on annual

basis is used as independent variable. The analysis is done using the formula given by

Seber, (2012) as under

Y = + x + e Equation No. 3.10

Where,

Y-Outcome of atmospheric temperature of Lahore (A) a) MAT b); MMxT; and c) MMiT

87

X - Time unit: (A) year

+ x - Linear (systematic) relation between Y and X

- Mean of Y when X=0 (Y-intercept)

- Variation in mean of Y when X increases (slope)

e - Random error term

In the formula, the datasets of coefficients, R square and significance tests are

interpreted for each data time series of temperature. In addition, the R2 statistics tabulated

in model summary, which can be used to calculate the regression model for outcome

predictions. It shows intensity of variable and degree of correlation between them. It can

explain the variations in the dependent variable with predictive efficiency of the model

and changing trends (Seber, 2012).

3.3.10. Software used in Analysis

Software programs run as from ESRI and Leica Geosystems are utilized to get the

analysis required for the results by processing the satellite images and to store, analyze

and manifest information. The present research work utilized ERDAS Imagine 9.2,

ARCGIS 10.2 and ENVI 4.7 for the image analysis including image pre-processing,

image processing and image classification, accuracy assessment, making of change

detection map and retrieving land surface temperature and driving NDVI, NDBI. The GIS

software, ARCGIS 10.2 is utilized to generate the thermal maps, urban expansion and

land use change maps. The other client software, an open source gySIG-GIS was

explored for visualization of satellite images. Microsoft office, including MS Word, MS

Excel and MS Access and SPSS 20, Mini tab were also used for the description, statistical

analysis and tabulation of land use data and analyzing the urban expansion and change

occurred during the study period and also used for graphical representation of analysis.

88

CHAPTER 4: URBAN EXPANSION OF LAHORE, 1951-2015

4.1. Introduction

The world is witnessing an unprecedented urban growth in the recent times. For

the first time in the history of urban growth, world has achieved momentous milestone in

2008 when more than half of the population of the world lived in towns and cities (UN,

2009). Population growth and urbanization go together and economic development is

closely correlated with urban expansion. An increase is noted in the urban areas

expanding rapidly throughout the world, therefore, the present century is termed as

"urban century" (Shirazi, 2011; GoP, 2013). Towns and Cities are growing in population

as well as in their geographic footprint and anthropogenic activities at an accelerating

pace (Blair, 2012).

An unprecedented urban population growth is recorded in Pakistan as well as in

Lahore along with unplanned developmental activities leading to the process of

urbanization and urban expansion. This massive growth in urban population in towns and

cities needs adequate management in terms of urban development planning, urban

population problems and land use change using efficient monitoring techniques and tools.

The current situation can be quantified by accurate information and reliable prediction for

the future expansion trends and to make comprehensive plans for the valuation of the land

use changes in the specific area. Data regarding population (including growth rate and

spatial distribution) and information about the change of land use patterns are required.

Both of these areas coordinate with each other as the population growth influences the

urban expansion and development exercising pressure on land use in respect of increase

in built-up land, demand for more housing in new sectors, the provision of transportation,

appropriate infrastructure, reduction in agricultural land and vegetation and degradation

of environment in general.

Monitoring and assessment of urban expansion and identification of Spatio-

temporal land use changes and respective disparities in urban settlements have now

become significant fields of study as the proportion and number of the urban population

continues to increase day by day throughout the world as well as in Pakistan. Since

Lahore is the 2nd largest city of Pakistan and provincial capital of the province of Punjab,

it has displayed significant areal expansion leading to developmental activities including

89

construction of buildings, roads and other anthropogenic activities since the day of

partition 1947 onwards. It is also one of the ancient and densely populated cities of

Pakistan as well as South Asia.

It is the economic, political, educational hub and major cultural centre for

Pakistan. Lahore is among the thirty largest cities of the world as it shares 22% urban

population in the province of Punjab, Pakistan. Lahore can be compared with the other

cities of the world having Spatio-demographic nature in terms of urban reality. In 2015,

Lahore had more than 9.5 million population and can be termed as a metropolitan city.

The spatial and physical expansion of the city is not only on linear pattern but also

sectoral and marginal because it has occupied the vacant areas and consumed most the

agricultural area of Lahore. The urban structure of Lahore is dynamic in nature as shown

by the behavior of urban expansion.

The land use changes and urban expansion can be monitored efficiently through

multi-spectral satellite remote sensing data. Urban expansion and morphological patterns

have been a vital issue in geographic researches. The significance of remote sensing for

the assessment of urban expansion and observing land use changes and their dynamics are

highly acknowledged. A number of researches have been conducted on analyzing the

significance of mapping in urban areas for monitoring land use and their changes utilizing

satellite data sets during the last two decades (Dewan and Yamaguchi, 2008). The

combination of GIS and RS is comprehensively applied and renowned as powerful and

precise tool in identifying urban land use changes providing reliable accuracy and results

along with clear images of urban expansion. This study, explores the urban expansion and

detects land use changes of Lahore, Pakistan during the periods from 1972 to 2015

through RS techniques.

In this chapter, the land use changes in the city of Lahore and dynamic process of

urban expansion are presented. This chapter is divided into two sections; first comprising

section highlighting temporal population growth of the city of Lahore for the period of

1951-2015. The results are shown in the bar graph, line graph and population distribution

maps of Lahore with the help of statistical analysis. Second phase of the chapter will

describe the urban expansion of Lahore from 1951 to 1972 (Pre-satellite Era) as the

growth of the city started mounting from 1951 onwards. The data for the aforesaid period

(1951 to 1972) was collected by using publications and maps as satellite images were not

90

in practice. The Spatio-temporal urban expansion and land use changes in Lahore from

1972–2015 (Satellite Era) were assessed through satellite images using remote sensing

and GIS techniques.

4.2. Population Growth of Lahore from 1901 to 2015

Population is major focus of all the related studies in geography as relying on the

population and the available resources are intimately connected, displaying the prospects

of urban expansion, land use change, development, land surface temperature and

environment in the specific region. Man, as a habitant, is consumer of all the biotic and a -

biotic resources besides being an important resource himself (Shirazi, 2011). It is,

therefore, the study of growth of population and its distribution is rationale preliminary

point for executing any type of research of the present nature. The focus of the present

research is to investigate the growth of population in the previous decades and to observe

the spatial distribution and temporal growth of population in the present decade for the

benefit of future planning and projection of future population growth rate. These factors

directly or indirectly affect the land resources of the study area. The dichotomy has ended

between the rural and urban areas under the devolution plan. In 2001, the devolution plan

was introduced in Pakistan ensuing local body election with the endorsement of local

government ordinance. Officially it was labeled as Local Government Plan whereas it

was named as Devolution Plan 2000 publically. A district includes a Tehsil in the

jurisdiction (both in urban and rural areas), while a City District comprises administrative

towns. The details of the population growth in the Lahore city (including Metropolitan

Corporation and Cantonment) have also been included in the present research as new set-

up by LDA in 2004 (LDA, 2004).

4.2.1. Population Growth of Lahore from 1901-1941

The population of Lahore, as reported in 1941 census was 671659. The population

of Lahore has increased for about 25 times since the start of the 20 th century as shown in

Table 4.1. In 1901, the population of Lahore was 202964 and 671659 in 1941, while the

population of the country (Pakistan) was recorded to be 1,657,700 to 28,282,000 for the

same span of time. The yearly population growth rate from 1901 to 1921 was rather slow

and can be attributed to under enumeration of population and difficulty in areal coverage.

Since then, the rate of population growth sustained till independence (1947) and then

91

accelerated at the rate of 4.31 and 4.57 % during the period of inter-censual from 1921-31

and 1931-41 respectively.

Table 4.1: Population, Growth Rates and Intercensual Increase of Lahore 1901-1941

Census

Year

Population Inter-censual Increase (%) Avg. Annual Growth

Rate (%)

Lahore Pakistan Lahore Pakistan Lahore Pakistan

1901 202964 16577000 - - - -

1911 228687 19382000 12.67 16.9 1.20 1.6

1921 281781 21109000 23.22 8.90 2.11 0.9

1931 429747 23552000 52.51 11.50 4.31 1.1

1941 671659 28282000 56.29 20.10 4.57 1.9

Source: GoP, 1951

Figure 4.1: Population Growth of Lahore from 1901 to 1941

Source: GoP, 1951

Figure 4.2: Inter-censual Increase and Growth Rates of Lahore from 1911 to 1941

Source: GoP, 1951

0

100

200

300

400

500

600

700

800

1901 1911 1921 1931 1941

Pop

ula

tion

(0

00

)

Year

0%

10%

20%

30%

40%

50%

60%

1911 1921 1931 1941

Perc

en

tag

e

Year

Inter-censual Increase (%) Avg. Annual Growth Rate (%)

92

4.2.2. Population Growth of Lahore from 1951 to 2015

In the urban hierarchy of Pakistan, the city of Lahore ranks second among the

major cities. It is the 2nd largest metropolitan city of Pakistan in terms of population after

Karachi as well as the 2nd largest urban center in the country for economic activities.

Besides, it is also the largest metropolitan city of the Punjab and provincial capital (Table

4.2). About 54.6 % of urban population of Pakistan resides in big cities: Karachi. Lahore,

Faisalabad, Multan, Rawalpindi, Gujranwala, Hyderabad, Peshawar and Quetta (Jan et

al., 2008). A growth of 3% is reported per year, from 2000 to 2005 and similar growth

has been predicted for the next decade (GoP, 2010). The population growth of Lahore

mounted higher than the rate of increase in the country. The detail regarding the

population growth is given below in census periods (Table 4.3).

Table 4.2: Rank of Lahore among the Major Cities of Pakistan Since 1951

S. No. Rank

1951 1961 1972 1981 1998

1 Karachi Karachi Karachi Karachi Karachi

2 Lahore Lahore Lahore Lahore Lahore

3 Hyderabad Hyderabad Faisalabad Faisalabad Faisalabad

4 Rawalpindi Faisalabad Hyderabad Rawalpindi Rawalpindi

5 Multan Multan Rawalpindi Hyderabad Multan

6 Faisalabad Rawalpindi Multan Multan Hyderabad

7 Sialkot Peshawar Gujranwala Gujranwala Gujranwala

8 Peshawar Gujranwala Peshawar Peshawar Peshawar

9 Gujranwala Sialkot Sialkot Sialkot Quetta

10 Quetta Sargodha Sargodha Sargodha Islamabad

Source: GoP, 2009

Table 4.3: Population Increase in Lahore from 1951 to 2015

Census

year

Inter-

censual

Period

(year)

Lahore District Lahore Urban

Population

Inter-censual

Increase (%) ACGR** Population ACGR**

1951 10 1,134,757 - - 861,279 -

1961 10 1,625,810 43.3 3.66 1,312,495 4.30

1972 11 2,587,621 59.2 4.06 2,189,530 4.48

1981 9 3,544,942 37.0 3.79 2,988,486 3.75

1998 17 6,318,745 78.3 3.46 5,209,088 3.32

2010* 12 8,650,000 26 2.69 7,097,000 2.65

2015* 15 9,545,000 - 7,846,000 -

*Estimated **Annual Compound Growth Rate

Source: GoP, 2000; GoP, 2015

93


Source: GoP, 2015

In 1951, the population of city of Lahore was 1.135 million which increased to

1.626 million in a decade, 1961. The average annual growth for the decade was about

3.7%, marked with an increase of 0.491 million people in inter censual period. The total

increase in population in the decade, 1951-1961 was reported to be 43.3% (GoP, 1961).

During the next period of 1961-1972, the increase in population was 1.626 million to

2.588 million. The average growth rate was 4.1 % while 1.326 million people were added

in the overall population of the Lahore. The increase in inter-censual span was 59.2 %

(GoP, 1972).

The massive increase in the growth owes to different factors contributing towards

urban growth in terms of population after independence, 1947. One of the major factors

of population shift towards urban areas was political. West Pakistan was declared as ‘one

unit’, and the culture center was Lahore, inviting people from far off areas to migrate

towards Lahore for better prospects of living. In 1972, the increase was reported to be

2.588 million which led to 3.545 million in 1981. The average annual rate of increase was

3.8% while 0.957 million people were added to the population of Lahore (GoP, 1984).

This decade, as compared to the last one noted a decrease in the population annual growth

rate. In fact, it was for the first time that the government of Pakistan realized the need for

the necessary measurement to be taken in terms of coping with the massive increase in

population. After 1972, a decrease in the rate of population was recorded.

0

2,000

4,000

6,000

8,000

10,000

12,000

1951 1961 1972 1981 1998 2015

Pop

ula

tion

(0

00

)

Census Year

94

After the census of 1981, the next census was held in 1998. The increase was

reported to be 2.774 million in Lahore. The increase was 3.545 million in the year 1981,

6.319 million in 1998 with an average growth of 3.5% per annum. The inter censual

period, ranging from 1981 to 1998 demonstrated 78.3% increase in the respective

population growth. The last census held in 1998, the population of the Lahore city was

officially reported to be 6.319 million, for about 4.77% of the total population of the

country (GoP, 2000). After the last census held in 1998, no preceding censual data could

be collected. The next national census was scheduled to be conducted in 2008. But it

could not be conducted due to deterioration of peace and non-availability of military and

civilian personnel required for the collection of the population data. Then, sixth national

population census was rescheduled to be held in March 2016 but again it was postponed.

In short, population census could not be materialized for one reason or the other. The

current population of Lahore can be estimated through projection equation (4.1) for 2015

given by Riaz, (2011).

Pt+17 = P1 + rP1 Equation No. 4.1

Pt+17 in the equation stands for the time of population Projection, P1 stands for the

population as recorded in the last census and r stands for the growth rate for the specific

period.

It is also estimated by applying the above mentioned equation that the population

of Lahore has increased to 9.5 million in 2015 as compared to 6.319 million in 1998. The

estimate of the government was 9.545 million people for the year 2015, whereas the same

figure is calculated by the researcher. During the period, almost 3.226 million people

were declared to be added in the total population of Lahore.

4.2.3. Population Growth Rate of Lahore from 1951-2015

Since the day of independence, Lahore has witnessed an enormous increase in the

population that amounted to be 1.135 million in the year 1951, leading to an increase in

1998 figuring the population calculation to 6.319 million. Although, the rate of overall

population growth rate in Pakistan had declined to 2.61% in 1998, whereas the rate of

growth in 1972 was 3.66% (GoP, 2000).

95

The growth rate sustained in Lahore for two main factors. First of all, Lahore is

the largest urban center of Punjab, and one of the most advance cities of the country. It

provides better medical facilities and maintains superior standards of living. Its increase is

bound to be natural, contributing mass exodus from the rural areas and occupying spaces

in the metropolitan city of the nearest access. Secondly, Lahore is a hub of socio-

economic, cultural and political activities for the people across the country, especially the

province of Punjab, inviting the people to migrate for better prospects.

Table 4.4: Population Growth and Inter-Censual Increase in Lahore from 1951-998 Description 1951 1961 1972 1981 1998

Population (000) 1135 1626 2588 3545 6319

Inter Censual Increase (%) - 43.3 59.2 37.0 78.3

Average Annual Growth (%) - 3.7 4.1. 3.8 3.5


Figure 4.4: Average Annual Growth rate and inter-censual increase from 1951-1998

Source: GoP, 2000

4.2.4. Population Distribution and Density of Lahore

Lahore has dispersed population. Its metropolitan limits are condensed while 61

settlements in the district house more than 5000 inhabitants. Before the introduction of

PLGO in 2001, Lahore had two tehsils in terms of administration, 1) Lahore Cant 2)

Lahore city tehsil. The census in 1998 reported the population to be 3.78 million (59.8 %)

in Cantonment tehsil while occupying 51.75 % of the total area of Lahore and 2.54

0

10

20

30

40

50

60

70

80

90

1951 1961 1972 1981 1998

Perc

en

tag

e

Census Year

Inter-censual Increase (%) Average Annual Growth Rate (%)

96

million (40.2%) people in Lahore city tehsil while occupying 48.25 % of the total area of

Lahore (GoP, 2000).

Table 4.5: Tehsils of Lahore and population in 1998

Sr.

No. Name of Tehsil

Area

(Km2)

Population

(million)

Population

Density Per km2

Urban

Population

1 Lahore Cantonment Tehsil 917 3.78 4,120 82.8

2 Lahore City Tehsil 855 2.54 2,971 81.9

Total Area & population of Lahore 1772 6.32 3,565 82.4

Source: GoP, 2000

By knowing the density of an area, one can depict the living conditions of that

particular area. For instance, areas with higher density are known to be problematic areas

in terms of necessary facilities and other utilities. Lack of open spaces in congested areas

is likely to spread epidemics and other viral diseases. Even the social values related to the

population residing in those areas are quite different to as compared with the values of

those who reside in low density areas. The total population of Lahore was 6.319 million

while the total area was 1772 Sq. Km in 1998. The population density is 3566 person per

sq. km as compared to the density recorded in 1981, i.e. 2001 persons per Sq. km. In

1972, the density was recorded to be 1460 persons per Sq. km. earlier, in 1961, it was 918

persons per Sq. km while in 1951, the density was 641 persons per Sq. km. (GoP, 2000).

Table 4.6: Population Distribution and Density of Lahore from 1951-2015

Year Area (Sq. Km) Population (000) Population Density per Sq. Km.

1951 1772 1,135 641

1961 1772 1,626 918

1972 1772 2,588 1,460

1981 1772 3,545 2,001

1998 1772 6,319 3,566

2010* 1772 8,650 4,881

2015* 1772 9,545 5,386

* Estimated Population

Source: GoP, 2015

Lahore population density is designed with the aid of following formula:

Gross Density = Total population / Total area (km2) = Persons/km2 4.2

According to the calculations, the estimated density of Lahore increased to 5276

persons per Sq. km. in 2014. Lahore, being a city district, can be termed as ‘City Proper’.

97

The term City Proper refers to the locality within the fixed boundaries, along with the

recognition of being an urban area by the respective government. According to these

standards, Lahore can be titled as the 30th amongst the most densely populated cities of

the world (UN, 2004). If the population growth maintains the same pace, it would be the

20th most densely inhabited metropolitan across the globe by 2025. The structure of

administrative units in Pakistan has been changing over the past years. These

administrative units have been used for the censuses conducted so far, making temporal

analysis complicated for the development practitioners and researchers in developing

countries. In 1998, the situation changed after the promulgation of Local Government

Ordinance (LGO, 2001) in Pakistan.

The administrative bodies were changed and the district was partitioned into city

districts, towns/tehsils and union councils. The administrative structure of Lahore, the

largest metropolitan city of the Punjab province and the 2nd largest city of the country was

announced as City District Government Lahore (CDGL) and was divided into six towns.

It was re-examined and three more towns were further added to CDGL. At present, the

Lahore City District governs nine (09) towns which are administrated under the

supervision of Town Municipal Administration (TMA), besides Lahore Cantonment.

Figure 4.5: Population Density of Lahore from 1951-2015

Source: GoP, 2015

The six (06) sub towns with respect to areas i.e. Shalimar Town (15km2), Gulberg

Town (32km2), Data Ganj Bukhsh Town (33km2), Samanabad Town (35km2), Ravi

0

1000

2000

3000

4000

5000

6000

1951 1961 1972 1981 1998 2005 2010 2015

Pop

ula

tion

Den

sity

Year

Population Density per Sq. Km.

98

Town (62km2) and Aziz Bhatti Town (89km2), are clustered in the north-western side of

Lahore, while the area wise three (03) larger towns i.e. Wagha Town (435km2), Iqbal

Town (464km2), Nishtar Town (520km2) are mainly located towards east and southern

side of Lahore as shown in Figure 4.6.

Lahore is spread over 1772 Sq. Km. and 80 per cent area of Lahore is covered by

three larger towns including; Wagha Town (435km2), Iqbal Town (464km2), and Nishtar

Town (520km2) while Shalimar Town has the least area of about 15 Km2 i.e. less than 1% of

total area of Lahore. The areas of the remaining five (05) towns i.e. Gulberg Town, Data Ganj

Bukhsh Town, Samanabad Town, Ravi Town and Aziz Bhatti Town occupy between 2 to 5% of

the total area of Lahore. These administrative towns are further sub divided into 150 union

council level, 128 urban while the rest are rural and one cantonment as stated in the Table

4.7.

Table 4.7: Town wise Urban and Rural Union Councils of Lahore Name of Town Area Km

2 Total UC’s Urban UC’s Rural UC’s

Aziz Bhatti Town 89 11 7 4

Data Ganj Bukhsh Town 33 18 18 -

Gulberg Town 32 15 15 -

Iqbal Town 464 15 6 9

Nishtar Town 520 19 11 8

Ravi Town 62 30 30 -

Samanabad Town 35 19 19 -

Shalimar Town 15 11 11 -

Wagha Town 435 12 5 7

Cantonment 87 - - -

Total 1772 150 122 28

Source: GoP, 2015

The average gross density in terms of persons per Sq. Km was the highest in

Shalimar Town (25,933) and the lowest in Wahga Town (1,106) in 1998. As per 1998

census data, gross population density of each town has been calculated using its area and

total population. The density is calculated on basis of number of persons per Sq. Km.

The overall density of Lahore was 3566 persons per Sq. Km in 1998. In the year

1998, the density of Shalimar Town was the highest (25,933), while that of Wahga Town

and Allama Iqbal Town was lowest (1,106 and 1,222 respectively). The population

density of the remaining towns varies from 1,411 to 21,576 persons per Sq. Km as shown

in Table 4.8 and Figure 4.6.

99

Table 4.8: Population Densities of Nine Towns of Lahore (1998)

S. No. Town Population 1998 (000,s) Area

Km2

Density

1998 Km2 Urban Rural Total

1 Aziz Bhatti Town 264 150 414 89 4,652

2 Data Ganj Bakhsh Town 712 - 712 33 21,576

3 Gulberg Town 571 - 571 32 17,844

4 Allama Iqbal Town 218 349 567 464 1,222

5 Nishtar Town 403 331 734 520 1,411

6 Ravi Town 1163 - 1163 62 18,758

7 Samanabad Town 722 - 722 35 20,629

8 Shalimar Town 389 - 389 15 25,933

9 Wahga Town 201 280 481 435 1,106

10 Cantonment 566 - 566 87 6,506

TOTAL 6319 1772 3566

Source: GoP, 2010

Figure 4.6: Town wise Population Distribution and Density of Lahore in 1998

Minallah, 2016

The population estimation of 2010 indicates that the overall population density of

Lahore is 4881 persons per Sq. Km. In TMAs, the densest town is Shalimar Town, 35,333

persons per Sq. Km. Data Gunj Baksh Town demonstrates 29,393 persons per Sq. Km

and Samanabad Town shows about 28,114 persons per Sq. Km. The least density is noted

to be in Wahga Town, 1508 persons per Sq. Km., Nishtar Town 1923 persons per Sq.

100

Km. and Allama Iqbal Town 1665 persons per Sq. Km. The density is shown in Table 4.9

and Figure 4.7.

Table 4.9: Population Densities of Nine Towns of Lahore (2010 Estimates) S.

No. Town

Population 2010 (000,s ) Area

(Km2)

Density

2010 Km2 Urban Rural Total

1 Aziz Bhatti Town 355 210 565 89 6,348

2 Data Ganj Bakhsh Town 970 - 970 33 29,393

3 Gulberg Town 778 - 778 32 24,312

4 Allama Iqbal Town 285 488 773 464 1,665

5 Nishtar Town 538 462 1000 520 1,923

6 Ravi Town 1585 - 1585 62 25,564

7 Samanabad Town 984 - 984 35 28,114

8 Shalimar Town 530 - 530 15 35,333

9 Wahga Town 263 393 656 435 1,508

10 Cantonment 809 - 809 87 9,298

TOTAL 8,650 1,772 4,881

Source: GoP, 2010


Minallah, 2016

The population estimation of 2015 shows that the overall density of Lahore

increased to 5,386 persons per Sq. Km. Similar increase is noted to be in the density of

Shalimar Town, 39,000 persons per Sq. Km. and remains the most thickly populated area

101

so far. It is followed by Data Gunj Baksh Town, 32,424 persons per Sq. Km., and remains

secondly densely populated town in TMAs.

Table 4.10: Population Densities of Nine Towns of Lahore (2015 Estimates)

S.

No. Town

Population 2015 (000,s) Area (Km

2)

Density

2015 Urban Rural Total

1 Aziz Bhatti Town 395 228 623,000 89 7244.18

2 Data Ganj Bakhsh Town 1070 - 1,070,000 33 32424.24

3 Gulberg Town 859 - 859,000 32 26843.57

4 Allama Iqbal Town 318 535 853,000 464 1838.362

5 Nishtar Town 599 505 1,104,000 520 2123.076

6 Ravi Town 1749 - 1,749,000 62 28209.67

7 Samanabad Town 1086 - 1,086,000 35 31028.57

8 Shalimar Town 585 585,000 15 39,000

9 Wahga Town 293 431 724,000 435 1705.74

10 Cantonment 892 892,000 87 10252.87

TOTAL 9,545,000 1,772 5386.568

Source: GoP, 2015

The town in the third place is Samabad Town, 31028 person per Sq. Km and the

least densely populated town are Wagha Town, 1705 persons per Sq. Km., Nishtar Town

2123 persons per Sq. Km. and Allama Iqbal Town 1838 persons per Sq. km. the figures

are shown in Table 4.10 and Figure 4.8.


Minallah, 2016

102

The density map of Lahore as shown in Figures 4.6 to 4.8 reveals that high density

zones are in the walled city and its adjacent eastern and southern localities where density

goes even beyond 800 persons per hectare. Like other urban centers, density is high in the

central zone and gradually lower towards the peri-urban areas. Some intermediate zones

like; Ichhra, Babu Sabu, Sanda also display higher density due to decentralization of

activities.

4.2.5. Urban and Rural Population of Lahore

Since 1951, the total population of Lahore has tremendously increased. This

population explosion indicates that Lahore has the potential of growing into a metropolis.

Owing to this massive growth, Lahore was given the status of city district in 2002.

Besides the districts considered to be the city, it also has many rural vicinities. The

population of various administrative towns (as shown in Table 4.11) in Lahore has

increased with fluctuating Annual Compound Rates (ACGRs) as recorded in inter-censual

periods. The rate of urban population growth of Lahore has continuously been declining

since 1972. During the period of 1961-72, it was 4.48%, further reduced to 3.75 % during

1972-81 and declined again in 1981-98 at 3.32 %. The overall growth rate of Lahore has

also been subject to decline but the pace is comparatively slow. The rate of growth of

whole district and urban area was almost the same during 1972-81. The rate of urban

growth declined a bit as compared to the overall growth rate (3.75%, earlier 3.79%).

Table 4.11: Population of Lahore & its Constituent Administrative Units

Name of Towns Total Population (000,s) Urban Population (000,s)

Urban

Population

1998 2010 * 2015 * 1998 2010 * 2015 * 1998 (%)

Aziz Bhatti Town 414 565 623 264 355 393 63.8

Data Ganj Bukhsh 712 970 1070 712 970 1070 100

Gulberg Town 571 778 859 571 778 859 100

Iqbal Town 567 773 853 218 285 318 38.4

Nishtar Town 734 1000 1104 403 538 599 54.9

Ravi Town 1163 1585 1749 1163 1585 1749 100

Samanabad Town 722 984 1086 722 984 1086 100

Shalimar Town 389 530 585 389 530 585 100

Wagha Town 481 656 724 201 263 293 41.8

Cantonment 566 809 892 566 892 892 100

Total 6,319 8,650 9,545 5209 7,097 7,846 82.4

*Estimated

Source: GoP, 2000; 2010; 2015

103

During the period of 1981-98, the difference was further declined. The rate of

urban growth was reduced to 3.32% as compared to 3.46% for the whole area of Lahore

as shown in Table 4.12. During the period of 1972-98, the growth of urban population

grew from 2.19 million to 5.21 million, however the proportionate Lahore urban

population declined from 84.62 percent to 82.44 percent during 1981 to 1998 (Table

4.13).

Table 4.12: Lahore Urban Population & ACGR (1951-1998) & 2015 Estimated

Census

Year

Inter-censual

Period (Years)

Lahore District Lahore (Urban)

Population ACGR (%) Population ACGR (%)

1951 10.00 1,134,757 - 861,279 -

1961 10.00 1,625,810 3.66 1,312,495 4.30

1972 11.67 2,587,621 4.06 2,189,530 4.48

1981 8.46 3,544,942 3.79 2,988,486 3.75

1998 17.00 6,318,745 3.46 5,209,088 3.32

2010* 12.00 8,650,000 2.69 7,097,000 2.65

2015* 14.00 9,545,000 7,846,000

*Estimated

Source: GoP, 2000; 2004; 2010; 2015

Since 1951, the rate of urban growth of Lahore has increased almost nine times.

The rural population in contrast has increased just five times the formal figure. Table 4.13

indicates that the urban population has always been subject to massive increase with a

rapid pace than that of rate of growth or rural population. In 1951, the urban population of

Lahore was 0.861 million that increased in 1962 to 1.312 million. During the inter-

censual period between 1961-72, 0.877 million of people were added to the urban total

and further 2.190 million people in 1972.

The urban population of Lahore in 1981 increased to 2.988 million and further

increased to 5.209 million in 1998 (GoP, 2000). According to an estimate, the projected

urban population of Lahore has grew up to 7.846 million in 2015 (Table 4.13). The

comparison of urban and rural population growth of Lahore as shown in Table 4.13 and

Figure 4.9 indicates that urban population is growing much more rapidly than the rural

population. This massive increase can be attributed to the sprawling limits of the city

encroaching over the rural vicinity. Villages in the vicinity of Lahore were merged into

104

the urban area. All these villages have merged into the urban territory of Lahore and the

respective population is considered to be a part of urban population.

Many rural localities of the Lahore vicinity have been merged into the city district

of Lahore. This rapid urban growth owing to the transformation of rural areas into urban

areas paced the areal expansion. New housing schemes have been established to house the

masses in the Lahore settlement. All such housing societies sprang at the cost of

agricultural land. The rural settlements comprising more than five thousand inhabitants.

Besides these, there are also a large number of smaller villages which have been merged

into urban areas.

Table 4.13: Urbanization (1951-1998) in Lahore and 2015 Estimates

Census

Year

Population Inter

censual

change

(%)

Proportion of

Urban

Population

(%)

Lahore

Total pop.

Lahore

Urban

Inter

censual

increase (%)

Lahore

Rural

1951 1,134,757 861,279 53.31 276,000 12 75.90

1961 1,625,810 1,312,495 66.2 309,000 28.8 80.73

1972 2,587,621 2,189,530 36.4 398,000 40 84.62

1981 3,544,942 2,988,486 74.33 557,000 99.2 84.30

1998 6,318,745 5,209,088 51.1 1,110,000 31.3 82.44

2010* 8,462,000 6,944,000 - 1,518,000 - 82.06

2015* 9,545,000 7,846,000 - 1,699,000 - 82.20

*Estimated

Source: GOP, 2000; 2010; 2015

Figure 4.9: District, Urban and Rural Population of Lahore

Source: GoP, 2015

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

1951 1961 1972 1981 1998 2010* 2015*

Pop

ula

tion

(0

00

)

District population Urban Population Rural Population

105

4.3. Urban Expansion of Lahore from 1951 to 2015

Urban expansion which is regarded as a form of urban growth (Bhatta, 2009)

takes place in multi-dimensional ways, such as increased residential clusters, revival of

built-up areas, introducing new urban areas, which were non-urban areas before. Urban

expansion resulted in encroachment in natural setup such as green belts, agricultural land,

and forest, vacant land and water bodies etc. and consequently natural setup turned into

built-up areas (Angel et al., 2005). Urban expansion has not only affected population but

it has also disturbed environmental ecosystem such as water scarcity, deforestation,

floods and rise in land surface temperature. Rapid population growth, industrial

expansion and growth in commercial activities enhanced the urban expansion (Puertasa et

al., 2014). This situation has been experienced in many cities of developing countries

particularly Pakistan such as Lahore. Such kind of development is seen along major road

networks and new residential colonies in unplanned and haphazard manner. In obvious

changes which are seen in economic activities, industrial extension and rapid

urbanization, researchers are of the view that urban expansion is triggered by

demographic change. Increasing population needed more residential land. It is not wrong

to say that developing countries like Pakistan, rapid increase in population is one of the

major driving forces behind urban expansion and city growth. In order to examine the

nature, amount, rate, location and trends of urban land use change of Lahore, an image of

built-up area is take out from Landsat classified images. During the study span from 1972

to 2015, classified images of built-up area are overlaid to get spatial and temporal urban

expansion map and land use changes of Lahore are observed.

Figure 4.10: Urbanization and Built-up area Trends of Lahore

Source: GoP, 2015; Minallah, 2016

0

2

4

6

8

10

12

1973 1981 1990 2000 2010 2015

0

100

200

300

400

500

600

700

Pop

ula

tion

(m

illi

n)

Urb

an

Bu

ilt-

up

lan

d (

Km

2)

Urbanized Land Population

106

4.3.1. Historical Expansion of Lahore: Pre-1947

Lahore is historical cultural hub of Punjab province, dated back to 1000 years.

Historians have declared Lahore as most attractive, most fascinating and majestic city of

Pakistan as well as South Asia. Loh, son of Rama Chandra, laid down foundation of

Lahore 2000 years ago. The earliest metropolitan city remained prominent through

various reigns, either Hindu or Mughal, Sikhs and British (IMPL-2004). Lahore invites

the inhabitants who are desirous to have urban character. Over centuries, Lahore has

distinctive geostrategic location in subcontinent because of its rich and unique socio-

cultural heritage, combining with urban infrastructure and development including a vast

number of gardens. That is why, Lahore was named “city of gardens” during Mughal

epoch. This Mughal legacy was enhanced through construction of building, parks and

green spaces during the British rule particularly the constructions concerned with Lahore

Cantonment and civil lines. During past decades, rapid urbanization changed the land use

profile of Lahore.

Lahore has always tended to be metropolis in terms of physical infrastructure

since 1947, creation of Pakistan. Extensive road network was laid. Community water

supply system was improved. Educational institutes, hospitals, bus service and radio

broadcasting were there. The municipal corporation had been working as separate public

body for decades. Lahore Improvement Trust (LIT) was established for city planning and

development. Foundation of modern infrastructure for Lahore was laid (Qadeer, 1983). A

large mass of population migrated to Lahore from India at the time of independence. As a

result, population of Lahore city increased. Besides the massive increase in urban

population, the areal expansion has become apparent now-a-days as compared to the past.

It may be ascribed to the division of sub-continent that affected the urban population

distribution in the country. After that Lahore experienced steady and consistent spatial

growth. Lahore Improvement Trust started new residential projects in fifties and sixties e.

g. Gulberg, Samanabad and Upper Mall scheme etc. LIT has succeeded by LDA, which

also made great contribution in development projects, housing schemes and planning

sector including industrial areas, airport and university campuses and commercial areas. It

is necessary to express here that these development projects and planned schemes have a

great contribution in Lahore development. Urban expansion map of Lahore depicts clear

picture regarding Lahore urban development in south and southeast directions (Fig. 4.11).

107

The River Ravi act as a barrier in urban expansion in north and west direction. Similarly

Indian border checked in eastward expansion of the city.

Figure 4.11: Urban Expansion of Lahore from 1850 to 2015


108

Economic progress in the country leads to physical and infrastructural

development and expansion. This is done because of petrodollar in seventies. Internal

migration (from countryside to city) triggered development and expansion of the city. The

physical growth in last six decades is marked by major road networks such as Ferozepur

road, GT road and Multan road. In addition, it has shaped into rectangular developed area

in north south direction. After 70’s, speedy development is seen in city along the Canal

bank, the Mall, Mayo and Lytton road. Many planned and unplanned residential schemes

were launched and they have obvious impact on current setup.

4.3.2. Urban Expansion of Lahore from 1951 to 1972 (Pre-Satellite Era)

Lahore is a historic city of Pakistan. At the time of independence of Pakistan, in

1947, the urban area of Lahore, comprised Walled city, Misri Shah, Mughalpura,

Baghbanpura, Chah Miran, Naulakha, Gari Shahu, Qila Gujjar Sing, Ichhra, Qila

Lachman Sing, Mozaang, Kirshan Nagar, Sant Nagar, Nawan Kot, Raj Garh, Ram Nagar,

Model Town and Cantonment Area. Figure 4.12 (a & b) shows the urban expansion of

Lahore since 1947 to 1972, the period also called as pre-satellite era. The total urban

built-up area was 66 km2 in 1951 as shown in the Figure 4.12a. After the partition of

India and Pakistan, the massive mass of people migrated across the border through

Lahore to reach Pakistan, as it was one of the gateways to enter the territory of Pakistan.

Moreover, Lahore was the major urban and industrial center at that time, providing

employment to the immigrants.

Since 1951 Lahore has grown at unprecedented rate. This unrestrained urban

expansion shifted transportation terminals from the suburbs of railway station to the other

areas of the city. In spite of this settlement, railway station still exists as the junction of

intra city transport. There is no sole CBD in Lahore. There are several business districts

in the city along major roads and highways. The congestions and overpopulated CBD

forced people to migrate to the outer periphery from the inner district of the city. These

peripheries are less populated and congested where there is less air and noise pollution.

At present, CBDs along Ferozpur road and GT road are present. The ring road circling

around the city is also a major reason for the establishment of industrial and residential

suburbs.

109

Figure 4.12: Urban Expansion of Lahore from 1947 to 1972

Source: Ghaffar, 2006

In 1960s new housing settlements appeared such as Samanabad, Wahdat Colony,

Gulberg, Shadbagh, Bahawalpur House, Shadman, Poonch House, Shah Jamal, Muslim

Town and New Garden Town. During the next period of 1951-1965, total urban built-up

area was noted to be 170 km2 in 1965 as shown in Figure 4.12b. Since 1961, the

population growth of Lahore began to increase at a higher rate. During the period

between 1961 to 1972, the population of Lahore increased steadily from 1.63 million in

1961 to 2.59 million in 1972 with average annual population growth rate of 4.06%. With

the passage of time, urban built-up area of Lahore also expanded. A several number of

new housing settlements were established with the passage of time including Joher town,

Faisal Town, New Garden Town, and Allama Iqbal Town and were further added to the

urban built up area of Lahore in order to cope the ever increasing population growth of

the city during the period 1961-1972.

4.3.3. Land use changes of Lahore from 1972 to 2015

In order to observe the patterns of urban expansion and magnitude, extent, trends

and rate of land use changes of Lahore, supervised image classification for the year 1973,

1980, 1990, 2000, 2010 and 2015 are executed and surface land were characterized into

level I classes scheme (Anderson, 1976). For identifying land use changes, based on prior

knowledge and review of literature, Maximum likelihood Algorithm (MLA) is used in the

present research in view of the demography of the study area.

Lahore is divided into four main classes; which includes 1) Urban/Built-up area,

2) Vacant Land, 3) Agriculture Land and 4) Water Bodies. The visual demonstration and

110

quantification can be very effective for the interpretation of the study area statistics. The

urban land use map of Lahore for the year 1973, 1980, 1990, 2000, 2010 and 2015 is

presented in Figure 4.13 to 4.18, and the area change statistics, percentage and rate of

change for each urban land use class during the study span are individually presented in

Table 4.14 and 4.15.

There are numerous ways to measure and monitor the land use changes and their

results. Among others, one of the basic techniques is to tabulate the data and quantify the

total land use changes for each land use type and observe the trends of land use change

between the different years. During the study span, distinct changes have happened on the

key land surface. The major changes of urban land use were evaluated as given below in

Table 4.14.

Table 4.14: Area Statistics and Percentage of Land use of Lahore from 1973-2015

Year

Land use Type Total

Area

(Km2)

Built-up

Area

Vacant

Land

Agricultural

Land

Water

Bodies

1973 Area Km2 223.96 320.02 1213.23 14.79 1772

% 12.64 18.06 68.47 0.83 100

1980 Area Km2 273.29 305.44 1170.57 22.70 1772

% 15.42 17.24 66.06 1.28 100

1990 Area Km2 352.75 277.74 1117.82 24.17 1772

% 19.91 15.67 63.08 1.36 100

2000 Area Km2 445.12 242.23 1062.25 22.40 1772

% 25.12 13.67 59.95 1.26 100

2010 Area Km2 517.43 230.69 1004.99 18.89 1772

% 29.20 13.02 56.72 1.07 100

2015 Area Km2 643.51 196.34 915.71 16.44 1772

% 36.32 11.08 51.68 0.93 100

Source: Computed from Landsat Imagery

111

Figure 4.13: Land use Distribution of Lahore in 1973

Minallah, 2016

Urban land use profile of Lahore changed extensively from 1973 to 1980. In 1973,

the urban/built-up land of Lahore was 223.96 Km2 which increased to 273.29 Km2 in

1980. The portion of built-up area increased from 12.64% in 1973 to 18.06% in 1980. It is

also revealed that the agricultural land decreased from 1213.23 Km2 in 1973 to 1170.57

Km2 in 1980, while area under vacant land decreased from 320.02 Km2 in 1973 to 305.44

Km2 in 1980 (Table 4.14).

During the period from 1973 to 1980, the built-up area increased about 49.33 Km2

(22%) while agricultural land reduced 43 Km2 (4%), vacant land decreased 14 Km2 (5%)

(Table 4.14). The key factor of this rapid urban expansion was the growth of population

of Lahore. Population of Lahore increasing rapidly, reached 3.54 million in 1981 which

was about 2.58 million in 1972. About 1 million people were increased in just one

decade. This increasing population needed more land for the purpose of new settlements

and commercial activities. So the agricultural land turned into built-up areas for new

housing schemes. Figure 4.13 and 4.14 depicts land use patterns of Lahore in 1973 and

1980 respectively. The expansion of the city started in south ward direction.

112


Minallah, 2016

During the second period from 1980 to 1990, the land use change of Lahore

became slightly rapid than the preceding period (1973-1980). The urban built-up area of

Lahore increased from 273.29 km2 in 1980 to 352.75 km2 in 1990. After this expansion

the share of urban/built-up land augmented from 15.42% in 1980 to 19.91% in 1990

(Table 4.14). During this phase, the urban/built-up land of Lahore increased 79.46 Km2

(29%) while agricultural land constantly transformed and decreased 53 Km2 (5%) while

vacant land decreased 27.70 Km2 (9%) was altered into urban/built-up area during 1980-

1990 (Table 4.15). Land use changes of Lahore in 1980 and 1990 are shown in Figures

4.14 and 4.15. The maps show the main urban expansion of Lahore on the southern side

of city. It is noted that agricultural and vacant land was altered into urban land. It can be

analyzed through maps that urban territory increased while there has been decline in

vegetation and agricultural land.

The years from 1990 to 2000 beheld a phenomenal increase in the urban

expansion of Lahore. The urban built-up area of Lahore increased from 352.75 km2 in

1990 and 445.12 km2 in 2000. The net accumulation of more than 92.37 km2 in

urban/built-up land during the period 1990 to 2000 has transformed from vacant and

113

agricultural land use to urban built-up environment and changed the land use profile of

Lahore. The land used for agriculture reduced from 1117.82 km2 in 1990 to 1062.25 km2

in 2000. The agricultural area reduced and its share in the total land decreased from 63%

in 1990 to 59.95% in 2000 (Table 4.14).

During this period (1990-2000), the urban/built-up land increased considerably 92

Km2 (33%) while vegetal cover constantly altered and decreased 56 Km2 (5%), vacant

land decreased 35 Km2 (13%) and was transformed into urban/built-up area during 1992-

2000 (Table 4.15). That is the time when government decided to deliberate the whole of

Lahore as the city district. The settlements adjacent to the limits of Lahore are also

included in the urban area. New housing schemes round about 110 and commercial areas

were approved to meet the residential need of increasing population, at the expense of

agricultural land as well as vacant land.


Minallah, 2016

It is demonstrated from the Figures 4.15 & 4.16 that the expansion in the urban

area during the period mostly expanded towards south, south western and western part of

the district. During the period from 1990 to 2000, the urban growth of Lahore was

tremendous. Lahore had never experienced such a rapid expansion in the history, owing

114

to the massive population in the period. The population in the period increased from 4.83

million in 1990 to 6.76 in 2000 (GoP, 2000). The major expansion took place along

Ferozpur, GT and Riawind road. The fact lying behind the urban expansion in these

directions is the availability of large area of open spaces, pollution free environment, and

availability of good water quality and establishment of many public and private

initiatives.


Minallah, 2016

Digital image classification shows that the growth of urban built-up area was at a

slower rate during the period between 2000-2010. The main emphasis was not on the

decrease in the urban population, rather it focused on the development aligning the

approved housing schemes. The urban/built-up land of Lahore increased from 445.12 km2

in 2000 to 517.43 km2 in 2010. The areas calculated by the author and calculated by the

SUPRRCO Pakistan are same in the year 2010. The share of built-up area in Lahore

increased from 25.12% in 2000 to 29.20% in 2010. From 2000 to 2010 the urban/built-up

land increased 72.31 km2 (16%) while agricultural land reduced 57.26 km2 (5%) and

11.54 km2 (5%) vacant land reduced from the land use profile of Lahore (Table 4.15).

115


Minallah, 2016


Minallah, 2016

116

The share of vacant and agricultural land decreased from 13.67% in 2000 to

13.02% in 2010 and 59.95% in 2000 to 56.72% in 2010 respectively. The land use map of

Lahore (2000, 2010) is shown in Figures 4.16 & 4.17. In 2010 the outcomes indicated

that the total built-up area in Lahore was 517.43 km2. It increased to 643.51.48 km2 in

2015, thus recording a massive growth of urban built-up land 126.08 km2 (24%) during

this time period 2010 to 2015 as shown in Figures 4.17 and 4.18, while agricultural land

was continuously converted and reduced 89 (9%). The share of total urban/built-up area

of Lahore enlarged from 29.20% in 2010 to 36.32% in 2015.

The area under use for agricultural purpose was reduced from 1004.99 km2 in

2010 to 915.25 km2 in 2015, while the vacant land reduced from 230.69 km2 in 2010 to

196.34 km2 in 2015. The total share of agricultural land reduced from 56.72% in 2010 to

51.68% in 2015 (Table 4.14) as a large part of it was converted into built-up land. The

result shows a rapid expansion of built-up land in general increased from 1973 to 2015,

by 187% with the greatest increase occurring from 1980 to 1990, 29% in 10 years.

Agricultural land, vacant land extent decreased by 38%, 24% respectively and mostly

they were converted to built-up area and urban land uses from 1973 to 2015 (Table 4.15).

All these relative trends are further demonstrated in Figures 4.19 and 4.20.

4.3.3.1. Nature, Rate and Extent of Land use Change

In order to determine the magnitude, extent and rate of change of land use change

in the area under study, following variables are measured.

Ta = Total area

Ca = Changed area

Ce = Change extent

Cr = Annual rate of change

These variables can be illustrated by the following formula:

I. Ca= Ta (t2)-Ta (t1);

II. Ce=[Ca/Ta (t1)]x100;

III. Cr=Ce/(t2-t1); Where t1 is the beginning and t2 is ending time of the urban land

used for the research conducted and the outcome was illustrated in Table 4.15

(Yesserie, 2009; Beza, 2011).

117

The average annual rate of change in urban/built-up land determined from the

image analysis land use change area statistics was 3% from 1973 to 1980, 2.9% from

1980 to 1990, 2.6% from 1990 to 2000, 1.62% from 2000 to 2010, 4.9% from 2010 to

2015 and 4.5 % for the whole study period of 1973 to 2015. This indicates a vivid change

in urban expansion and morphology of Lahore according to its extent and size.

Furthermore, agriculture land and vacant land showed an average reduction of change

approximately -0.5 to 2.98% annually. In general, the change values in the Table 4.15

showed that increase in urban built-up areas mostly originated from transformation of

other land uses in particular agriculture land to urban land uses (i.e. Built-up area) during

the past 42 years (1973-2015) following increasing development pressure within the

Lahore. Figures 4.19 and 20 portrayed the nature of relative land use change trends from

1973 to 2015 in Lahore. The areal extent of urban built–up area observed positive

increasing trends while all the study period agricultural area and vacant spaces showed

continuous decreasing trends.

Table 4.15: Overall amount, rate, nature and extent of land use change 1973-2015 Land Use 1973-1980 1980-1990 1990-2000

change

(∆/km2

)

Extent

(%)

Rate

of ∆

(%/yr)

change

(∆/km2)

Extent

(%)

Rate

of ∆

(%/yr)

change

(∆/km2)

Extent

(%)

Rate

of ∆

(%/yr)

Built-up 49.33 22.03 3.15 79.46 29.08 2.91 92.37 26.19 2.62

Vacant land -14.58 -4.56 -0.57 -27.70 -9.07 -0.91 -35.51 -12.79 -1.28

Agriculture -42.66 -3.52 -0.50 -52.75 -4.51 -0.45 -55.57 -4.97 -0.50

Water 7.91 53.48 7.64 1.47 6.48 0.65 -1.77 -7.32 -0.73

Land Use 2000-2010 2010-2015 1973-2015

change

(∆/km2

)

Extent

(%)

Rate

of ∆

(%/yr)

change

(∆/km2)

Extent

(%)

Rate

of ∆

(%/yr)

change

(∆/km2)

Extent

(%)

Rate

of ∆

(%/yr)

Built-up 72.31 16.25 1.62 126.08 24.37 4.87 419.55 187.33 4.46

Vacant land -11.54 -4.76 -0.48 -34.35 -14.89 -2.98 -123.68 -38.65 -0.92

Agriculture -57.26 -5.39 -0.54 -89.28 -8.88 -1.78 -297.52 -24.52 -0.58

Water -3.51 -15.67 -1.57 -2.45 -12.97 -2.59 1.65 11.16 0.27

Source: Computed from Table 4.14

118

Figure 4.19: Nature of Relative Land use Change of Lahore from 1973 to 2015

Minallah, 2016

Figure 4.20: Land use Changing Trends of Lahore from 1973 to 2015

Minallah, 2016

4.3.4. Temporal Urban Expansion of Lahore from 1972-2015

Urban expansion of Lahore denotes the growth of built-up area. First of all, built-

up area was extracted based on four land use type data by using GIS techniques with the

help of Arc/GIS 10.2 and got respectively temporal urban expansion maps of Lahore for

the year 1973, 1980, 1990, 2000, 2010, 2015 (Fig. 4.22 & 4.23) and calculated area of

expansion during the study period from 1973-2015 (Table 4.16).

Table 4.16: Comparison of Built-up and Non Built-up Area of Lahore

Year 1973 1980 1990 2000 2010 2015

Urban/Built-up Area (Km2) 223.96 273.29 352.75 445.12 517.43 643.51

Non Built-up Area (Km2) 1533 1476 1395 1304 1236 1112

Water 14.8 22.7 24.17 22.4 18.89 16.44

Total Area (Km2) 1772 1772 1772 1772 1772 1772

Source: Computed from Landsat Imagery

0

200

400

600

800

1000

1200

1400

Built-up Area Vacant Land Agriculture Land Water Bodies

Are

a (

Km

2)

Land use Profile of Lahore

1973 1980 19902000 2010 2015

0

200

400

600

800

1000

1200

1400

1973 1980 1990 2000 2010 2015

Are

a (

km

2)

Years

Built-up Area Vacant Land Agriculture Land Water Bodies

119

The key concern of the present research was to assess temporal urban expansion

from 1973-2015 with prior emphasis on the major land use type of urban built-up area

and non-built-up area. The comprehensive land use type maps were no longer required

and a simple binary class like urban built-up area from satellite remotely sensed data is

sufficient (Bhatta, 2009). The reclassification maps of Lahore land use type with two

main classes includes built-up are and non-built area comparison are shown in Figure

4.21. Table 4.16 depicts the results evaluated from satellite imagery after reclassification.

Figure 4. 21: Comparison of Built-up and non-built-up Area of Lahore

Minallah, 2016

Figure 4.22: Temporal Change in Urban Expansion of Lahore in 1973 and 2015

Minalla, 2016

223.96 273.29352.75

445.12 517.43643.51

1533 14761395

1304 12361112

0

500

1000

1500

2000

1973 1980 1990 2000 2010 2015

Are

a (

Km

2)

Years

Urban/Built-up Area Non- Built-up Area

120

Figure 4.23: Temporal Urban Expansion of Lahore from 1973 to 2015

Minallah, 2016

121

The land use map got after reclassification of Landsat imagery into two category

provides visual images of temporal expansion of the study area (Figures 4.22 & 4.23).

These temporal urban expansion maps of Lahore provide the proof of the confirmation

about the temporal land use dynamics of Lahore during the study period (1973-2015).

These temporal maps (Figures 4.22 and 4.23) deliver particulars about the nature, rate

extent and overall amount of urban built-up land change during the period (1973-2015) in

Lahore.

4.3.5. Annual Rate of Urban Expansion from 1972 to 2015

Temporal expansion and change of urban area are computed by annual growth

rate. In order to discuss annual growth rate of urban expansion, an estimation index

(ARU) is designed given by Wang and Bao, (1999); Zhu and Li, (2003); Xiao et al.,

(2006); Li et al., (2009); Haregeweyn, et al., (2012); Xu & Min, (2013) and it is defined

as follows:

𝐀𝐑𝐔 =𝐔𝐢−𝐔𝐣

𝐔𝐢 ×

𝟏

𝐓 × 𝟏𝟎𝟎% E Equation No. 4.2

Where ARU is Annual Rate of Urban Expansion; Ui and Uj represent the

urban/built-up areas at the beginning time and ending time for a certain study span (1972-

2015), respectively. T signifies the time that study period covers.

Table 4.17: The Urban area, ARU and increasing urban area of Lahore Year 1973 1980 1990 2000 2010 2015

Urban Area (Km2) 223.96 273.29 352.75 445.12 517.43 643.51

ARU (%) - 3.14 2.80 2.61 1.62 4.87

Increase Urban area (km2) - 49.33 79.46 92.37 72.31 126.08

Minallah, 2016

During the last 42 years, the urban/built-up area of Lahore has increased 419.55

km2 from 223.96 km2 in 1973 to 643.51 km2 in 2015 and the Annual rate of urban

expansion has increased. The result indicated that during the study period (1972 to 2015)

Lahore as a whole, retained a very high growth of increasing urban/built-up area in past

42 years (Table 4.17). The fastest growing period is in 2010–2015, when the ARU

touches 4.87%, and the second faster one is in 1973–1980, when the ARU is about

3.14%, and the third faster one is 1980-1990, when the ARU is about 2.8%. It can be

122

established that urban expansion of Lahore also slowed down between 1990-2000 and

2000-2010 at the rate of 2.6 % and 1.6 % respectively.

4.3.6. Urban Expansion Intensity Index

Intensity of urban expansion is a spatial unit that can define the rate of urban

expansion in different study periods, so it can be utilized to quantitatively link the

intensity of urban expansion at different study periods. The formula given by Liu et al.,

(2000); Hu et al., (2007); Zhou et al., (2009) and Gao et al., (2011) can be stated as

follows:

Ei = ΔA ⋅ 100 / A . Δt Equation No. 4.3

Where: Ei signifies intensity index of urban expansion, ΔA denotes the

transformation area of non-urban built-up area to urban built-up area. A signifies the total

area of study area, Δ t characterizes interval of computing period (in year).

Table 4.18. Indices of urban temporal expansion of Lahore during different periods

Indices 1973-1980 1980-1990 1990-2000 2000-2010 2010-2015 1973-2015

Expansion

area (km2)

49.33 79.46 92.37 72.31 126.08 419.55

Expansion

rate (km2)

7.04 7.94 9.24 7.23 25.22 9.98

Expansion

intensity (%) 0.39 0.45 0.52 0.41 1.42 0.56

Minallah, 2016

Table 4.18 shows that indices of temporal urban expansion and intensity of

expansion during different periods have been calculated by using the equation number

4.3. The urban/built-up area of Lahore was respectively 223.29 km2 in 1973, 273.29 km2

in 1980, 352.75 km2 in 1990, 445.12 km2 in 2000, 517.43 km2 in 2010 and 643.51 km2 in

2015 (Table 4.14). The annual average expansion rate was 9.98 km2 in the past 42 years,

the period extending from 1973 to 2015, and the urban built-up area was expanded to

419.55 km2 with an expansion intensity of 0.56% as shown in Figure 4.22. During the

period from 1973 to 1980, the temporal patterns of urban expansion indicated that the

urban area of Lahore grew at annual average expansion of 7.04 km2, with an intensity of

0.39%, an expansion area of 49.33 km2. Urban area expanded towards the southward

direction, along with the major roads. During the period of 1980 to 1990, an increase in

the urban expansion rate and intensity was noted. The urban/built-up area expanded by

79.46 km2 with an intensity of 0.45%, along with an annual average expansion rate of

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7.94 km2. The Figure 4.23 presents the temporal urban expansion from 1980 to 1990. It is

indicated in the maps that the urban expansion of Lahore majorly occurred towards the

southern side of Lahore. Moreover, the land under agricultural use and forest area was

altered to urban land use. During the period of 1990 to 2000, there was observed an

increase in the urban expansion rate and intensity. The urban/built-up land expanded by

92.37 km2 with the intensity of 0.52% and annual average expansion rate of 9.24 km2. It

is indicated in the Figure 4.22 that the expansion during the period 1990-2000, has

occurred majorly towards south, along with south western and the western portion of

Lahore. During the period from 2000 to 2010, a slightl decline in the intensity and

expansion rate was observed. The urban/built-up area expanded by 72.31 km2, with the

intensity of 0.41 % and annual expansion rate for 7.23 km2. The period from 2010 to

2015, there was a massive increase in the urban expansion recorded to be 126.08 km2

with an intensity of 1.42% and annual average expansion rate of 25.22 km2.

4.3.7. Urban Change Detection of Lahore from 1973-2015

The land use change detection has been analyzed through post classification

technique of multi-temporal Landsat satellite imagery of 1973 to 2015. The post

classification technique specifies “from-to” change evidence. This classification approach

supports in mapping and measuring the phenomenon of conversion of land in urban

landscapes. The Spatial information of expansion of urban area is worthwhile to

comprehend the urban expansion direction in different periods from 1973-2015 (Figures

4.24 and 4.25). The post classification evaluation technique is very useful for data

acquired through different sensors with different spectral and spatial resolution.

This technique was used to detect land use changes, by analyzing independently

processed classified land use maps. The main feature of this method is its ability to

deliver expressive information on changing trends. This approach mainly depends on the

results of the classified images and data stored in GIS database. GIS provides compatible

medium for post-classification appraisals and facilitates qualitative valuation of the

factors, which has impact on urban expansion. GIS system has been utilized to

incorporate urban/built-up land class for the six time series and a thematic map was

generated to analyze trend of urban expansion as shown in Figures 4.24 and 4.25. During

the study span from 1973 to 2015, it was obvious that the urban/built-up areas expanded

in southeast, south and southwest direction of Lahore.

124

Figure 4.24: Urban Change Detection of Lahore from 1973 to 2015

Minallah, 2016

125

Satellite remote sensing implication proved to be effective in creating change

detection map (Figures 4.24 and 4.25), and is very useful in spatial understanding about

single entity and productive in analyzing, changing statistics and comprehensive visual

description of data. 1973-1980 era is supposed to be first spatial urban expansion period.

During this study span, there is 50 km2 increase in urban built-up area. Spatial patterns of

urban expansion depict that there is growth in built-up area while there is decline in

agriculture and vacant land. Most remarkable change in form of spatial urban expansion

is shown in southern side during this period. Urban expansion was rapid in 1980-1990

decade as depicted in Figure 4.24. It is evident from map that the urban spatial expansion

of Lahore occurred primarily on southern side of Lahore by converting vacant and

agricultural land into built-up area and urban uses.

Figure 4.25: Spatial Expansion of Lahore from 1973 to 2015

Minallah, 2016

Phenomenal increase in urban spatial expansion of Lahore is seen during the

period 1990-2000. During the period (1990-2000), the rate of the encroachment of

urban/built up areas expanded much more extensively in the similar direction of the

preceding period. It is clear from the Figure 4.25 that the urban spatial expansion during

this time frequently took place in the south, south western and western part of Lahore.

126

It is apparent from the study period during 1990-2000, there was extraordinary

urban spatial expansion of Lahore. Lahore had never experienced such a rapid physical

expansion throughout the past. Classified image analysis of decade (2000-2010) showed

slower urban expansion in this period. The rate of urban expansion during this period

slightly declined. It does not imply that population growth of Lahore decreased. The fact

lying behind this slower growth is development of existing approved housing societies.

One of the major factors behind the urban expansion during the period 2000 to 2010 is

encroachment of urban built-up areas, occupying extensively over agriculture and vacant

land which was reduced by 58 km2 & 12 km2 respectively. However the expansion of

urban/built up areas increased 126.08 km2 toward agricultural area more than vacant land

during 2010-2015.

Figures 4.24 and 4.25 depict an apparent Spatio-temporal urban expansion of

Lahore through the change detection maps of Lahore. Urban expansion encounters all

urban features and spread of urban area all over Lahore. The classified image results

show a noticeable expansion in populated communities. Urban expansion is more obvious

in southeast, south and southwest and western direction. Moreover Agricultural land

transformed into built-up area. Overlay analysis (Figure 4.25) of built-up area shows the

trends of change in 1973-2015.

4.4. Classification Accuracy Assessment

An imperative course of action related to the digital image classification is

accuracy assessment of the results after classification. This accuracy assessment has been

designed by confusion/error matrices. It is the most frequently used technique for per-

pixel image classification accuracy (Lu and Weng, 2007). Random selection of the

samples was done to avoid biased assessment of results (Jensen, 2005). 150 Ground

Control Points (GCPs) were designated for each land use class. Fieldwork strategies were

adopted and GPS device was used for the collection of comparison of sampled pixels

along with their corresponding land use type on ground. The accuracy assessment of the

historical satellite images for the period of 1973, 1980, 1990, 2000, 2010 and 2015 was

carried out by using following methods.

First of all, GCP sample points, collected randomly, were validated through field

observations for the land without any change in the period of the study, such as forests,

water bodies and ancient buildings. Secondly, additional confirmation of these land use

127

areas was made through published map and census report of population. The process of

accuracy assessment proceeded after picking up several test samples for each satellite

image for recognized classified results. Google images and base maps were used for

authentication and reference. The following Figure 4.26 indicates the descriptive example

of test samples of satellite image for accuracy assessment. Test samples are white in

colour before confirmation by using reference images. After confirmation, they were

turned to yellow.

Figure 4.26: An Example for Test Samples on an Image for Accuracy Assessment

Minallah, 2016

The accurate results are evident in Table 4.20, 4.21 and 4.22 indicating user

accuracy and producer accuracy, kappa index of agreement and over accuracy for all six

images. The user accuracy and producer accuracy for each images are calculated

separately. Error matrix was also drawn for the accuracy of the images.

The results drawn from the thematic Landsat MSS images for the years 1973 and

1980 certified the accuracy of 78.67% and 82.47 % respectively. Similarly, the Landsat

images of TM, ETM, OLI_TIRs of 1990, 2000, 2010 and 2015 indicate the accuracy of

Test Samples

128

85%, 87%, 89 % and 92% respectively. The four images of TM, ETM, and OLI/TIRs

system for the years 1990, 2000, 2010 and 2015 demonstrate a little higher accuracy as

compared to the MSS images of the year 1973 and 1980. This slightly higher result may

be due to the higher spectral, spatial and radiometric resolution. This analysis was good

enough for the accurate assessment and acknowledgement in terms of analyzing urban

change detection. The producer’s accuracy ranged from 50 % to 97% while the user’s

accuracy ranged from 60% to 86% for each land use class type.

Table 4.19: Overall Classification Accuracy and Kappa (κ) Statistics 1973 1980 1990 2000 2010 2015

Overall Accuracy (%) 78.67 82.47 85.00 87.42 89.66 92.67

Overall Kappa (k) 0.5349 0.7242 0.7701 0.8145 0.8385 0.8808

Minallah, 2016

Table 4.20: User’s and Producer’s Accuracy for each Land use type

Land use 1973 (%) 1980 (%) 1990 (%) 2000 (%) 2010 (%) 2015 (%)

P U P U P U P U P U P U

Built-up Area 76.9 71.4 82.2 78.1 90.4 89.1 93.5 92.1 97.1 92.9 94.1 96.0

Vacant Land 50.0 64.7 67.6 92.0 63.6 87.5 68.4 86.6 65.0 81.2 85.7 85.7

Agriculture 88.4 82.8 88.8 86.7 89.8 84.5 90.2 83.6 94.5 89.6 96.1 94.8

Water Bodies 50.0 62.5 70.0 60.0 73.3 68.7 78.9 83.3 72.7 80.0 85.7 66.6

*P for Producer Accuracy and U for User Accuracy

Minallah, 2016

Table 4.21: Conditional Kappa for each Category

Class Name Conditional Kappa (K)

1973 1980 1990 2000 2010 2015

Built-up Area 0.6872 0.7305 0.8317 0.8653 0.8844 0.9394

Vacant Land 0.5864 0.8973 0.8576 0.8475 0.7881 0.8424

Agriculture Land 0.4418 0.7204 0.7242 0.7529 0.8210 0.8947

Water Bodies 0.5982 0.4653 0.6591 0.8093 0.7865 0.6503

Minallah, 2016

Kappa index of agreement for each classified image is shown in the Table 4.19.

The results of the Kappa index of agreement is 53% for 1973, 72% for 1980, 77% for

1990, 81% for 2000, 83% for 2010 and 88% for the year 2015. The numerical visibility

regarding producer’s accuracy and user’s accuracy is shown is tabulated in Table 4.20 for

129

each land use type. Throughout the study period, image classification from 1973 to 2015,

the lowest user’s accuracy and the producer’s accuracy were not calculated. However, the

higher user’s and producer’s accuracies were detected for the urban built-up and

agricultural land classes due to much consideration of research for the change in urban

land use and urban expansion analysis.

130

CHAPTER 5: LAND SURFACE TEMPERATURE VARIATIONS

5.1. Introduction

The environmental changes are continually occurring on the Earth’s surface

because of urban development and expansion, as natural greenery and agricultural land

are turned into non-transpiring surfaces like metal asphalt and concrete. Such changes

produce the urban-rural disparity with relevance to air and land surface temperature.

Increasing urban development is seen all over the world particularly in developing

countries like Pakistan. It is necessary to keep the effects of such change in land use on

the climate in check. At the moment, global change in temperature is the biggest issue

surrounding mankind. It is vital to realize such alterations at a small scale since the

city/regional scales show more human population density and influences of such

atmospheric changes can be sensed on these scales. For the analysis of natural

environment affected by urban expansion, land surface temperature is one of the key

indicators and this indicator is governed by vegetation. In the present research, land

surface radiant temperature is retrieved from radiometrically corrected Landsat thermal

images of Lahore from 1990-2015. An attempt is made in this study to observe the land

surface temperature variations in correspondence with NDVI, NDBI indices and land use

changes, and their effects on Land surface temperature of Lahore. A predictive regression

model is developed which is an appropriate and convenient way to find a correlation

existing between the changes in land use and temperature.

In this chapter, the results that highlight the outcomes of the research are

discussed. The presented findings of the present research describe the prospective of land

surface temperature change as well as highlight the certain urbanization parameters i.e.

rapid population growth, land use changes, increasing number of registered vehicles,

increasing number of factories and greenhouse gases , as various factors of air and land

surface temperature change. Secondly, in this chapter, three different data time series of

MMiT, MMxT and MAT of metrological data are used to discuss the trends and temporal

changes in atmospheric temperature from 1950 to 2015, with emphasizing their root

causes in order to comprehend likely effects. To examine the significant change detected

in land surface temperature variation with the passage of time, linear regression and

Pearson correlation method are applied respectively. Moreover, this chapter also

131

encompasses the discussion on the spatial variations of land surface temperature in

Lahore. In addition, some of the hotspots of temperature are also pointed out.

The land use dynamics of Lahore have been indicated in the different time spans,

demonstrating variant thermal environment effects in different areas of urban expansion.

Moreover, the study further establishes the correlation that exists between NDVI, NDBI

and LST of Lahore. Finally the massive scale of hot spot is observed, over the densely

populated and industrial areas, where the urban heat islands are likely to develop, whereas

the cold areas are observed over the green spaces and water bodies.

5.2. Factors Increasing Land Surface Temperature

Earth’s climate is subject to so many influences that can be categorized into

natural and anthropogenic (human-induced) factors. Scientists have been conducting

observations in the climatic change due to the factors other than any natural influences of

the past since the beginning of the 20th century. The change in the climate, referred to as

Global warming, happened rapidly as compared to the other climate changes observed in

the past and that is why it yields greater importance to human population. With rapid

population growth and economic advancement, the influence of human activities on the

temperature has increased. The mean maximum and minimum temperature of Lahore

recorded to be 30.8°C and 17.8°C.

Figure 5.1: Factors Increasing Land Surface Temperature

Minallah, 2016

Population Growth

Urban Expansion/Lan duse Changes

Increase No. of Vehicles ,

& No. of Factories per year

Transformation of Natural Cover to Urban

Structure i.e. Impervious Surfaces

Air and Land Surface Temperature Rising

132

Urban population growth is one of the most important existing geographic

phenomena all over the world. The process of rapid urban expansion of Lahore was

recorded since 1951, ultimately increasing air and land surface temperature. The major

factors of this rapid urban expansion including education and health facilities,

employment opportunities, industrial and commercials activities in urban centers have

been contributing towards urban migration, a mass exodus of the people from rural to

urban centers. Due to this massive migration, urban centers expand day by day. Urban

expansion is the alteration of natural land cover to land use accompanying with urban

population growth and economic activity. Urban areas covered with impervious surfaces

such as buildings, road networks and steady built-up structures have higher ratio of solar

radiations, greater thermal capacity of absorption and conductivity as accumulated during

the plenty of sunlight in daytime and reradiating at night. Therefore, cities tend to

experience a comparatively higher air and land surface temperature with adjacent rural

areas. These thermal differences in aggregation with heat given off by urban house and

various human factors including transportation, industries, Greenhouse gases and

deforestation contribute to the increase in heat and develop UHI.

Owing to urban heat island, the cost of energy increases and quality of life is

drastically affected. With each degree increase in temperature, utilization of power

subsequently increases for cooling purposes. The level of atmospheric and land surface

temperature increases due to the subsequent increased electricity use for air conditioning.

The earth’s rising atmospheric and land surface temperature are the hot issue in today’s

world. Ultimately, the multi-source data and remotely sensed imagery facilitate the

process of estimating variation in temperature and micro-level urban climate. The

following sections of the study discuss the major causes of climatic change, along with

factors having influence on land surface temperature.

5.2.1. Rapid Growth of Population

The available facts and figures on human population indicate that the population

growth of Lahore was recorded and estimated from 1.13 million in 1951 to 9.55 million

in 2015 (GoP, 2015) as shown in Figure 5.2. The population density of Lahore has also

increased by this alarming growth from 641 to 5,569 persons/km2 from 1951 to 2015

respectively. The urban population of Lahore is 82% while remaining is rural (GoP,

2015). The mean annual growth rate of Lahore during 1951-61 was 4.3%, which declined

133

to 3.32% during 1981-98. The growth rate, however, is still higher when compared to

other major cities of the world (IMPL, 2007).



Population density is directly proportional to the effects, generating heat as more

people will consume more energy leading to the emission of heat. Residential areas with

lower spatial distribution and higher density contribute to the factor influencing the

production of urban heat. It is also indicated as shown in the settlement density maps that

the areas with higher population density in the center of the city experience higher

temperature as compared to the areas in south towards rural areas away from the center of

the city have lower density as low heat island effects. The following scatter plot (Figure

5.3) is created for the comprehension of structure and distribution of correlated variables;

MAT and Population Growth.

Figure 5.3: Correlation between Population Growth and MAT of Lahore

Minallah, 2016

0

2

4

6

8

10

12

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Pop

ula

tion

(M

illi

on

s)

Year District Urban

R² = 0.6842

y = 0.1369x + 24.05223

23.5

24

24.5

25

25.5

26

1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

MA

T (

OC

)

Population (million)

134

The above mentioned scatter plot (Figure 5.3) of variables, population growth and

MAT, shows data sample points along with line rising from the bottom left to upper right,

representing the presence of significance correlation. Results have shown correlation

value of R2 0.68, indicating positive correlation between population growth and increase

of MAT of Lahore. The results obtained by Pearson correlation, significant degree of

correlation between growth of population and MAT of Lahore are shown in Table 5.1.

Using Pearson correlation coefficient, results were obtained, showing perfect positive

correlation between MAT and population change of Lahore. Results have shown in Table

5.1, Pearson correlation value 0.836, indicating strong positive correlations between

population growth and increase of MAT of Lahore at the significant percentage i.e. 95%

as measured by alpha value and P-value, which is not greater than 0.05.

Table 5.1: Pearson Correlation between Population Growth and MAT

Variable Analysis MAT Population

MAT

Pearson Correlation 1 0.836**

Sig. (1-tailed) - 0.005

N 66 8

Population

Pearson Correlation 0.836** 1

Sig. (1-tailed) 0.005 -

N 8 8

**. Correlation is significant at the 0.01 level (1-tailed).

Minallah, 2016

5.2.2. Land use Changes

The influence of land use is considered to be an important driver in climate

change. The change detection analysis of land use has become vital element in the current

policies for monitoring and supervision of environmental amendments. The influence of

land use on temperature has been detected widely after 1980’s, by using satellite remotely

sensed data. The expansion of urban area is evident in the classified images from the

period 1973 to 2015. In the same vein, the population explosion decreased the agricultural

land and vegetation. It can be observed from the results that the agricultural

land/vegetation is declining in the City Centre as a result of new settlements. The various

land use types are identified in the satellite images in the Table 5.2 given below.

135

Table 5.2: Land use Patterns of Lahore form 1973 to 2015

Year Land uses Total Area

(Km2) Built-up Area Vacant Land Agriculture Water

1973 Area Km2 223.96 320.02 1213.23 14.79 1772

% 12.64 18.06 68.47 0.83 100

1980 Area Km2 273.29 305.44 1170.57 22.70 1772

% 15.42 17.24 66.06 1.28 100

1990 Area Km2 352.75 277.74 1117.82 24.17 1772

% 19.91 15.67 63.08 1.36 100

2000 Area Km2 445.12 242.23 1062.25 22.40 1772

% 25.12 13.67 59.95 1.26 100

2010 Area Km2 517.43 230.69 1004.99 18.89 1772

% 29.20 13.02 56.72 1.07 100

2015 Area Km2 643.51 196.34 915.71 16.44 1772

% 36.32 11.08 51.68 0.93 100

Source: Computed from Landsat Images

Human encroachments are also one of the major reasons in modification of natural

system that forces environmental and climatic change at global, regional and local levels.

Lahore, like other mostly urbanized areas in the world, is experiencing socio-cultural and

economic transformations due to urbanization and population explosion and

interconnection of these anthropogenic factors. This is the reason Lahore is extending and

becoming economically strong and industrialized with massive increase in urban built-up

land. Spatio temporal changes of land use are visible all over Lahore. Table 5.2 indicates

that land use experienced a sea change since 1973 to 2015 as shown in Figure 5.4.

5.2.2.1. Urban Built-up Area

Urban expansion accompanying massive changes in its morphological

patterns has been a factor in temporal and spatial land use change, at global, regional and

local level. Land use alteration indicates the patterns of developments of the city in terms

of reduction of agricultural land and loss of vegetal cover, expansion in urbanized

housing schemes and construction of sky scrapers, parking lots and increased density of

population. Spatio-temporal urban expansion is actually transformation of agricultural

136

and vacant land into impervious surfaces that can be detected in the land use classification

of Lahore from 1973 to 2015.

Figure 5.4: Land use Patterns of Lahore from 1973 to 2015

Minallah, 2016

137

The population of Lahore in 1972 was 2.17 million, which increased to 9.5 million

in 2015 with an increase of 350% in forty three years. Increasing demand of residential

areas with the growing population had risen due to urban expansion. In 1973, the urban

built-up land of Lahore was 223.96 km2, which increased in 2015 to 643.51 km2 as shown

in Table 5.2 and Figure 5.4.

Figure 5.5: Population and Urban built-up area of Lahore from 1973 to 2015

Minallah, 2016

Owing to the rapid growth of population and land use changes, the local climate

of Lahore had adverse effects. The ever increasing demand of the buildings for residential

and commercial purposes has led to expansion of the city in all the directions, particularly

towards south and southeast of Lahore city. Statistical data of the built-up area is acquired

through image classification. The following results were deducted in finding the

magnitude of change in urbanization and its impact on MAT.

Figure 5.6: Correlation between Urban built-up area and MAT of Lahore

Minallah, 2016

0

2

4

6

8

10

12

1973 1981 1990 2000 2010 2015

0

100

200

300

400

500

600

700

Pop

ula

tion

(m

illi

n)

Urb

an

bu

ilt-

up

are

a (

Km

2)

Urbanized Land Population

R² = 0.9333

y = 0.0039x + 23.40823

23.5

24

24.5

25

25.5

26

200 250 300 350 400 450 500 550 600

MA

T (

OC

)

Urban built-up area (Km2)

138

The results obtained by Pearson correlation in Table 5.3 is 0.870 which shows

ideal positive correlation between MAT increase and urban built-up land at the significant

rate of 95%, as obtained by alpha value and P-value which is not more than 0.05. The

results also highlight value of R2 which was 0.933 (Figure 5.6) indicating very strong

positive relationship between built-up area and MAT of Lahore. The results demonstrate

that the relationship between urban expansion and MAT is significant.

Table 5.3: Pearson Correlation between Built-up Area and increase of MAT Variable Analysis MAT Built-up Area

MAT Pearson Correlation 1 0.870*

Sig. (1-tailed) 0.012

N 66 6

Built-up Area Pearson Correlation 0.870* 1


N 6 6

*. Correlation is significant at the 0.05 level (1-tailed).

Minallah, 2016

5.2.2.2. Reduction in Agricultural Land

The massive, unplanned urban development created environmental disturbances

that is vulnerable and sensitive to the reduction of agricultural land and vegetation cover

transformed into the impervious surfaces which affect the temperature of Lahore and

create urban heat island phenomenon. During the last 6 decades, agricultural land has

been replaced majorly by impervious surfaces and increased built-up areas that have

affected the land surface temperature and redistribution of isolation.

The regression analysis between reduction in Agricultural land, loss of vegetation

and MAT of Lahore indicate negative correlations. The results show value of R2 is 0.728

(Figure 5.7) and indicate negative correlation between vegetation cover and increase of

MAT of Lahore.

The results shown in Table 5.4 with Pearson correlation value of -0.929 indicate

high negative correlations exist between the reduction in vegetation/agricultural land and

MAT of Lahore at a significant 95% as given value of alpha and P-value is not greater

than 0.05.

139

Figure 5.7: Correlation between Reduction in Agriculture Land and MAT of Lahore

Minallah, 2016

Table 5.4: Pearson Correlation between Reduction in Vegetation and MAT

Variable Analysis MAT Vegetation

MAT Pearson Correlation 1 -.929**


N 66 7

Vegetation Pearson Correlation -0.929** 1


N 7 7


Minallah, 2016

5.2.3. Increase in Registered Factories

The installation of industrial units has been ever increasing since 1990 -2015, but

a massive increase can be noted after 2005 as shown in Figure 5.8. This increasing

number of heavy industries and factory units also contributed to considerable increase in

the temperature of Lahore. The relation of the rise in temperature corresponding with the

number of industrial units is quantified by regression analysis. The results have been

obtained with the help of bivariate correlation analysis. Table 5.5 shows the results for

Lahore in order to indicate the correlation between MAT increase and industrialization.

R² = 0.728

y = -0.0035x + 28.678

23

23.5

24

24.5

25

25.5

26

900 950 1000 1050 1100 1150 1200 1250

MA

T (

OC

)

Agriculture Land

140

Figure 5.8: Number of Registered Factories in Lahore from 1990 to 2015

Source: GoP, 2015

Figure 5.9: Relationship between MAT and Registered Factories in Lahore

Minallah, 2016

Table 5.5: Pearson Correlation analysis between MAT and Factories in Lahore

Variable Analysis MAT Factories

MAT

Pearson Correlation 1 .954**

Sig. (1-tailed) .006

N 5 5

Factories

Pearson Correlation .954** 1

Sig. (1-tailed) .006

N 5 5


Minallah, 2106

500

700

900

1100

1300

1500

1700

1900

2100

2300

2500

199

0

199

1

199

2

199

3

199

4

199

5

199

6

199

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The result obtained by Pearson correlation is 0.95. A positive correlation between

the MAT increase and number of factories at a significant level of 95%, while P-value is

not greater than 0.05 was recorded. R2 is obtained to be 0.910 (Figure 5.9), indicating the

positive correlation between the MAT and factories of Lahore.

5.2.4. Increase in Registered Vehicles

The ever increasing number of registered vehicles is one of the major factors

responsible for changing climate of Lahore. The number of vehicles registered in the

period from 1990-2015 was obtained from the Excise and Taxation Office, Govt. of the

Punjab, Lahore. The increase in the number of registered vehicles is displayed in Figure

5.10. A comparison between the number of vehicles registered during different periods

supports the argument that the increase in number of vehicles is causing climatic change

in Lahore. The number of vehicles registered during 1990-2000, 2000-2010 and 2010-

2015 indicate a difference of 80000 vehicles from 1990-2015. The result is significant in

making comparison for the city of Lahore, comprising an area of 400 km2.

Figure 5.10: Number of Registered Vehicles of Lahore from 1990 to 2015

Source: Excise and Taxation office Lahore, Pakistan, 2015

According to the data received from Excise and Taxation Department Lahore, the

total number of registered vehicles in the city was 0.5 million in 1998, 1.2 million in 2005

and 3.5 million in 2007. The number of vehicles is getting multiplied in five to seven

years as there is no existing Master Plan of the infrastructure of the city, except

maintenance, remodeling, construction of underpasses and flyovers, and mega structures

of impervious structure like Metro Bus and Orange Train Track.

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Figure 5.11: Trends of Vehicles of Lahore from 1990 to 2015

Source: Excise and Taxation office Lahore, Pakistan, 2015

According to the Lahore Motor Registration Authority, 238,790 vehicles were

registered in the year 2007, while 290,919 vehicles were registered in 2011, showing 18

% increase. The increased number of vehicles in the city is causing threat to the

environment by emitting poisonous gases including carbon mono oxide, carbon dioxide,

unburned gases and smoke in the air. Utilizing bivariate correlation, the correlation

between increasing number of registered vehicles and increase in MAT for Lahore is

shown in Table 5.6.

Figure 5.12: Relationship between Vehicles and MAT of Lahore

Minallah, 2016

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Table 5.6: Pearson correlation between registered Vehicles and MAT of Lahore

Variable Analysis MAT Vehicles

MAT

Pearson Correlation 1 .928**

Sig. (1-tailed) - .004

N 6 6

Vehicles

Pearson Correlation .928** 1

Sig. (1-tailed) .004 -

N 6 6


Minallah, 2016

The tabulated figures show that the Pearson correlation is 0.92 (Table 5.6),

indicating positive correlation between the MAT and increasing number of vehicles per

year with P-value is greater than 0.05 and overall significance level is 95%. R2 is found to

be 0.86 (Figure 5.12), indicating the positive correlation between the Vehicles and MAT

of Lahore.

5.2.5. Increase in Greenhouse Gases

The Earth possesses natural greenhouse effects where sunlight is allowed to enter

into the atmosphere but the heat radiations are absorbed. Since the time of industrial

revolution, anthropogenic activities have also increased the ratio of GHGs in the air. The

ingredients of the greenhouse gases include Carbon dioxide (CO2) that occupies only a

small portion (0.03%) of atmosphere as the most important greenhouse gas. The recent

climatic change is also contributed by the carbon dioxide in the greenhouse gas. Carbon

dioxide is naturally released into the atmosphere by volcanic eruptions, animal respiration

and human activities concerning combustion of fossil fuels for energy and deforestation.

CO2 sustains for a considerable period in atmosphere and increases its effects. CO2

concentration has increased up to 30% since the industrial revolution. The second major

component in greenhouse gases is Methane (CH4). It is produced by natural and human

activities. The most considerable source of Methane is decomposition of organic matter

related to landfills and agriculture activities. Another contributing source is digestion of

ruminants of cattle etc. Methane as greenhouse gas is much stronger as compared to CO2

as it absorbs more heat, even if it is less in ratio in the atmosphere.

144

Nitrous oxide (NOx) is another strong greenhouse gas that is produced by

agricultural sector, significantly in the use of organic fertilizer. Nitrous oxide is also

produced in the air while burning fossil fuels. Chlorofluorocarbons (CFCs) are man-made

compounds specifically used for industrial use, in air-conditioners and in refrigerators.

The use of Chlorofluorocarbons has been regulated for the adverse effect concerning the

ozone layer under the Montreal Protocol. The industrial activity in the 20 th century grew

up to 40-fold, with greenhouse gases emission to be 10-fold. Ozone (O3) is also a potent

greenhouse gas which has a short life span in the atmosphere. Chemical reactions are the

main source of letting ozone emit volatile organic compounds from industrial plants,

power plants, automobiles and other commercial sources, including nitrogen oxides.

Besides the issue of trapping heat, the pollutant ground-level of ozone creates health

problems including respiratory system. It also causes damage to the ecosystem and crops.

An increase in the atmospheric CO2 concentration has been recorded to be 40 %

since it was recorded in pre-industrial era. It was indicated to be 280 parts per million

(ppm) by volume in 2010, while increase up to 396 parts per million (ppm) by volume

was noted in 2013. For the first time in the history, the monthly average concentration at

Mauna Loa rose up to 400 parts per million (ppm) by volume in 2014. The current ratio

of CO2 in atmosphere is higher as compared to the ratio recorded in at least 800,000

years. From 1850 till the end of the 20th century, the overall warming was recorded to be

2.5W/m2. CO2 contributes about 60% of the total figure and 25% of the contribution is

made by CH4. The remainder is provided by NO2 and halocarbons. This factor has

resulted in an increase of Earth’s average temperature from 15.5°C to 16.2°C in the last

one century. The level of warming effects resulting from the double quantity of CO2 in

the atmosphere from pre-industrial level is estimated to be 4W/m2.

LST is increasing owing to the increased ratio of these greenhouse gases that

retain more heat in the atmosphere. This increase in LST is also bringing about other

effects in climatic change. All these affects are termed as anthropogenic climatic change.

Most interestingly, certain ratio of GHGs is necessary to maintain human and animal life

because these gases absorb heat and maintain average temperature on Earth surface

around 14°C. If the greenhouse effects become nonexistent, life would freeze as average

temperature of earth would fall down to -19°C (WMO, 2016). Besides the beneficial side

http://www.epa.gov/ozone/

145

of greenhouse effects, the excessive ratio in Lahore is polluting its environment day by

day.

Figure 5.13: Greenhouse gases from 2008 to 2010 in Lahore

Source: EPA, 2015

Since the demand of the energy in the urban areas is rising, the emission is also

rising, resulting in air pollution with increased quantity of Sulfur dioxide (SO2), Carbon

dioxide, Nitrogen oxide (NOx), carbon monoxide CO, and particulate matter (PM)

released. All the factors contributing to the release of the matter in the atmosphere cause

problems for human respiration and increase air and surface temperature. The appendix 2

indicates that most of the gases are above the quality standards of national environment.

A correlation analysis indicates a positive relationship in the results between urban

density and the air pollutants in increased urban temperatures.

The air quality samples are taken from the different locations of the Lahore city as

these urban built-up areas are associated with population density and high land surface

temperature. By applying the statistical test on the obtained data of Landsat image

analysis, it has been detected that the massive increase in urban growth is the main cause

of change in temperature of Lahore. It is also considered to be the major factor

contributing to increased greenhouse gases that led to smog formulation. The smog

appears as a dense layer of clouds of suspended-particles over Lahore. This dense layer of

2008 2009 2010

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suspended particles matters (PM) in the atmosphere is triggering rise in minimum


5.3. Comparison of Contributing Factors of Changing Temperature

Trends

The following section, in detail, discusses the analysis of comparative aspects of

chosen contributing factors in temperature change of Lahore in a designated time period.

They are:

Population Growth of Lahore

Change of Land use Patterns

o Urban Built-up area

o Agricultural Land

Increase in Number of Registered Factories

Increase in Number of Registered Vehicles

Increase in Greenhouse Gases

The results acquired by Pearson correlation coefficient, are shown in Table 5.7,

which is presenting significant degree of correlation between urban expansion factors i.e.

growth of population, land use changes (i.e. urban built-up area and agricultural land),

increased industrial activities, increased registered vehicles, and greenhouse gases and

increase MAT of Lahore from 1950 to 2015.

Table 5.7: Degree of Correlation of Different drivers behind the Changing

Temperature Trends

Contributing Factors Degree of

Correlation

Type of

Correlation

Population Growth 0.83 Positive

Land

uses

Urban Built-up area 0.87 Positive

Agricultural land -0.92 Negative

Increase Number of Registered Factories 0.95 Positive

Increase Number of Registered Vehicles 0.92 Positive

147

According to the Table 5.7 and Figure 5.14, it can be concluded that the reduction

in agricultural land and growth of urban population and built-up areas are the major

factors among all in exercising effects on temperature of Lahore. The rate of increase of

urban built up area is similar to the reduction in agricultural land.

Figure 5.14: Comparison showing all Contributing Factors of Temperature Change

Minallah, 2016

5.3.1. Multiple Regression Analysis

The multiple regression model needs to be fitted to assess the relative importance

of different covariates. There is a strong correlation among the variables that play their

respective roles, each indicating indispensable for one another in the multi regression

model. In the Table 5.8, the results of multiple regression analysis indicate that these

variables have a strong impact on temperature of Lahore. The independent variables of

vehicle, population growth, factories and built-up area had a positive correlation with

temperature. Factory growth and vehicle registrations have correlated population increase

and increase of built up area which caused increase in the temperature of Lahore. The

agricultural land had negative correlation with the temperature of Lahore. As the

agricultural areas reduced due to the loss of vegetation cover, it caused an increase in the

temperature of Lahore. The R2 described that these variables had a strong impact on

temperature. The R2 showed direct variation in the dependent variables because of

independent variables.

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Table 5.8: Multiple Regression Analysis

Dependent Variable: Temperature

Method: Least Squares

Date: 11/17/16 Time: 07:32

Sample: 1951 to 2015

Included observations: 8

Variable Coefficient Std. Error t-Statistic Prob.

C 36.08774 14.15207 2.549997 0.1255

Vehicles 6.24E-07 4.91E-07 1.270813 0.3316

Population Growth 4.82E-08 8.86E-07 0.054438 0.9615

Factories 0.001872 0.004024 0.465161 0.6875

Built-up Area 0.000552 0.011500 0.047979 0.9661

Agriculture land -0.008686 0.009323 -0.931633 0.4499

R-squared 0.963461 Mean dependent var 24.79250

Adjusted R-squared 0.872113 S.D. dependent var 1.054335

S.E. of regression 0.377044 Akaike info criterion 1.000794

Sum squared resid 0.284324 Schwarz criterion 1.060375

Log likelihood 1.996825 F-statistic 10.54717

Durbin-Watson stat 2.728306 Prob (F-statistic) 0.088860

5.4. Atmospheric Temperature Trends of Lahore from 1950 to 2015

The urban climate of Lahore has mainly been affected by the large scale

urbanization. In addition to the global impact being exercised on the climate change,

indigenous factors also contribute to the climate change. The temperature trends of the

city demonstrate variations in different years. The temperature change throughout the

period of analysis is not constant. After 1980s, the rising temperature trend shows

consistency and regularity. The analyzed parameters of temperature, MMiT change

pattern is regular and steady throughout the study span. During 1950-2015, an increase is

observed up to 1.38°C in MMiT of Lahore (Fig. 5.15a). Maximum change in MMiT

during 1988-2015 is observed (Fig. 5.15a). MMxT shows less significance in the change

trends. During the period of 1950-2015, a decrease is observed in the change trends of

MMxT; 0.47°C (Fig. 5.15b).

The main increasing trend in MMxT is observed during the period of 1957-1987

(Fig. 5.15b). The rapid increase in MMiT affected the MAT of Lahore. MAT has also

gradually risen from the period 1950 to 2015. MAT increase for the last 65 years has been

observed to be 0.377°C (Fig. 5.15c). Post 1980s is significant for the massive sprawl in

Lahore. For the time span from 1980s to date, climate of Lahore has also been badly

affected by the urban population. During the most urbanized period, the intensity of the

149

temperature is recorded to be almost 93% of the total rise in MAT after 1950. The

prediction signifying further growth is also computed on the basis of currently analyzed

data. If the same patterns in the temperature trend persist, the future increase in

temperature until 2030 is predicted to be 0.658°C.

Figure 5.15: Atmospheric Temperature Variations of Lahore from 1950 to 2015

y = 0.0389x + 16.976

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Source: PMD, 2016

Figure 5.16: Trend line showing the future prediction of MAT of Lahore until 2030

Source: PMD, 2016

5.5. Land Surface Temperature Variations of Lahore, 1990-2015

The spatial variations of land surface temperature are essential to the study of

urban climate change. This section of analysis pursues to estimate Spatio-temporal trends

of land surface temperature variation of Lahore from 1990 to 2015 through Satellite

Remote Sensing thermal images. Figures 5.17 to 5.20 indicate the spatial variations

observed in LST in the urban area of Lahore and point out hotspots of heat. The results

depict that temperature has revealed a considerable change from 1990 to 2015 and it

corresponds to land use changes and urban expansion. A comparison of land surface

temperature variations is presented with the help of two selected years 1990 and 2015.

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151

The section also describes the thermal zones within Lahore. Assessment of two Land

surface temperature variation maps (Figure 5.17 and 5.20) setting show modification of

the land use from agricultural land/vegetation use to the built-up area which always plays

a dynamic role in fluctuating temperature trends.

It is obvious by interpreting the thermal maps that the vegetation cover and water

bodies’ area have low temperature effects as heat sink or absorber. It is observed in

calculated results that the urban land use and density change have strong impact on the

changing trends of land surface temperature of Lahore. The points of both heat absorption

and heat emission clearly reflect rise in land surface temperature throughout Lahore i.e.

the River Ravi, walled city of Lahore and industrialized areas and vacant land

respectively. In the present study, land surface temperature of Lahore was assessed

through Radiative Transfer Method and the outcomes of the study are shown in the Table

5.9 and 5.10. The result shows that LST is strongly correlated to urban expansion, i.e.

land surface temperature increases with rapid increase in urban expansion.

Table 5.9: Descriptive Statistics of Land Surface Temperature of Lahore, 1990-2015 Image Acquired Min Temp (°C) Max Temp (°C) Mean Temperature (°C)

16-03-1990 16.5149 30.7528 23.6338

19-03-2000 16.9988 30.9987 23.9987

07-03-2010 17.2123 31.5668 24.3895

21-03-2015 17.4071 33.8357 25.6214

Source: Computed from Landsat Thermal Images

Table 5.10: Temperature Change from 1990 to 2015 Temperature (°C ) Change

Temperature 1990-2000 2000-2010 2010-2015 1990-2015

Maximum 0.2459 0.5681 2.2689 3.0829

Minimum 0.4839 0.2135 0.1948 0.8922

Mean 0.3649 0.3908 1.2319 1.9876

Source: Computed from Table 5.8

Temperature observations at MET observatories are measured at only small

number of places in a city, therefore, they are not reliable for acquisition of temperature at

all desired sites. The RS technique provides spatial data for all required locations. The

estimated LST maps illustrate temperature variations in Lahore as shown in Figure 5.17

152

to 5.20. The retrieved degree Celsius (°C) land surface temperature of Lahore is the

highest in 2015, based on the satellite image analysis derived results. The maximum

temperature was noted to be 33.83°C, according to the LST maps (Figure 5.20), while

minimum reaching temperature was 17.41°C in March, 2015. The increase in surface

temperature is attributed to the heat wave that is recurrent in such summer months. These

heat waves are found in the summer season for a few days.

The statistical data of estimated temperature of the year 1990 shows that the

minimum surface temperature recorded was 16.51°C while the maximum temperature

recorded was 30.75°C. The mean temperature was 23.63°C in 1990. It is reflected in the

Figure 5.17 that the urban built-up area, vacant land and industrial areas exhibited have a

high temperature while agricultural land, vegetation and water bolides show lower

temperature comparatively. The ‘hotspots’ are easily recognizable in the LST distribution

map (Fig. 5.17). The center of the city (around walled city) and industrial area and vacant

lands are found to be the most extensive hot spots. The difference of the temperature in

hot spots is reflected not only in the state of vegetation cover, solar illumination and

atmospheric influences, but also variation in land use type.

Figure 5.17: Land Surface Temperature Variations of Lahore in 1990

Minallah, 2016

153

Figure 5.18 reflects the increase in the mean land surface temperature from

23.63°C in 1990 to 23.99°C in 2000. The increase in temperature of the study period,

from 1990 to 2000 is noted to be 0.39°C as described in Table 5.10. The increase in

population of Lahore has led to massive replacement of natural resources with impervious

surfaces such as bridges, roads, parking lots, buildings, pavements and other concrete

structures. The population increase is noted to be 4 million in 1990 which shot to 6

million in 2000. These heat islands are capable of retaining heat in the sun light and

omitting it at night, causing stress to the environment and affecting the urban micro-

climate of Lahore.

The mean temperature of the year 2000 was 23.99°C, as shown in Table 5.9,

signifying that the land surfaces experience a little difference in LST during the

mentioned periods 1990-2000. The maximum LST in the year 2000 was 30.99°C and

minimum LST recorded in 2000 was 16.99°C. The central area of Lahore can be easily

differentiated from the peripheries. The central area showed the temperature of 29°C,

while the temperature of the outskirts is lower, i.e. 16 to 27°C. The urban built-up areas

of Lahore have expanded eastwards and southwards.


Minallah, 2016

154

Figure 5.19 reflects the spatial variation of temperature of Lahore for the year

2010. The LST ranged from 17.21°C to 31.57°C with a mean temperature of 22.38°C.

The ground thermal pitches in the center of Lahore, comprising buildings, parking lots,

houses, cement pavements and infrastructure were particularly concentrated and

determined. The population density in the center of Lahore was greater, along with the

anthropogenic activities which were much more than ever before. Resultantly, the higher

surface temperature was recorded in Lahore.

Owing to the commercial activities and industrial consumption in the industrial

areas, the highest land surface temperature is measured. The Figure (5.19) shows the east,

southeast and south parts of Lahore exhibit the high temperature due to the vacant and

built-up land. The effects of the vegetation cover can be noted in the areas showing

lowest LST values over densely vegetation cover and agricultural land. The extreme

temperature detected in the built up, vacant land and industrial areas ranges from 27°C to

31.56°C.


Minallah, 2016

In response to the building geometry in the urban areas, the circulation of the wind

is limited. These environmental conditions lead to human discomfort and require air

155

conditioning. Moreover, air conditioners and electric generators are used in urban areas,

causing more heat and increasing temperature. However, in the rural and the suburban

areas, the heat is lower owing to the vegetation cover and agricultural land still in use.

Changes of LST in urban and rural were significant in causing notable urban heat island

effects. Figure 5.20 demonstrates the Spatio-temporal distribution of emissivity-corrected

LST of Lahore in 2015.

The readings of the land surface temperature for the year 2015 show that the

highest temperature is 33.83°C and the lowest temperature is 17.40°C. The mean

temperature is 25.62°C. Higher land surface temperatures mainly increase in industrial

zones and urban centers, that is, the UHI effects. After identifying the land surface

temperature variation maps, it was indicated that the maximum land surface temperature

values existed mostly in the center of the city, also known as walled city, featured by

densely built-up area, commercial centers and deep street canyons. The maximum

temperatures in the urban and the suburban areas experienced land surface temperature

within 28°C to 33°C. The LST observed in the River Ravi, water channels, canals, along

with green spaces was lowest surface temperature.


Minallah, 2016

156

The mean land surface temperature in 2015 was noted to be 25.62°C as compared

to the temperature of initial reference period of 1990, i.e. 23.68°C. The maximum

temperature of 33.83°C and the minimum temperature of 17.40°C was estimated for 2015

which was also higher than the initial reference period of 1990, i.e. 30.75°C maximum

and 16.51°C minimum surface temperature. The comparison of both the periods proves a

definite increase in temperature as shown in Table 5.8 and 5.9. The Spatio-temporal

distribution of emissivity-corrected temperature, ranging from the period of 1990 to 2015

of Lahore is shown in Figure 5.17 to 5.20.

5.6. Town wise Trends of LST of Lahore between 1990 and 2015

The method of digital remote sensing provides a spatial extent of surface urban

heat island (SUHI) effects, along with magnitude of hotness of whole of the city. Figure

5.17 and 5.20, reflect that the distribution of the impervious surfaces (IS) is directly

associated with the higher temperature. The LST town wise comparative study (Figure

5.21a & b) for the year 1990 and 2015 reflects the higher land surface temperature with

the extension of development going-on in the urban regions. Some of the hot spots in the

entire study region boast heat islands effects. According to Town wise LST map, high

temperature areas are Shalamar Town, Data Ganj Baksh Town, Gulbarg Town, Ravi

Town, Nishtar Town, Iqbal Town, and Saman Abad Town as Shown in Figure 5.21a.

The map of LST 1990 in Figure 5.17 indicates that the most widespread hot spot

is found in the urban built-up, industrial zone and bare soil located in the west and the

south of the city. On the other hand town wise LST map of March 1990, as shown in

Figure 5.21a, exhibits that low land surface temperature areas are Samanabad Town,

Wagha Town, Cantonment and Aziz Bhatti Town of Lahore. For comparison, LST is also

assessed for March 2015 as shown in Figure 5.20. According to the estimation, 1.98°C

(Table 5.10) LST has increased in last 25 years. It is observed that in Figure 5.21b

Shalamar Town, Data Ganj Baksh Town, Gulbarg Town, Ravi Town, Nishtar Town,

Iqbal Town, and Saman Abad Town were warmer in the year March 2015 than the year

March 1990. It is worth mentioning that in the area of Aziz Bhatti Town, Nishtar Town

and Wagha Town, there were no urbanization and development, therefore the lower

temperature was experienced in the year 1990. However the expansion of Lahore city in

these areas was urbanized and the temperature rose in 2015 as compared to 1990. In the

157

year 2015, the widespread hotspot was identified in the old city, industrial areas, airport

along with impervious runways and barren lands located in the east and southeast of the

city.

Figure 5.21: Town wise Comparison of LST of Lahore in 1990 and 2015

Minallah, 2016

158

Figure 5.22: Town wise Trends of LST of Lahore in 1990 and 2015

Minallha, 2016

The reduction of the diversity of species and damage to the eco-system can be

attributed to the conversion of natural vegetation and consumption of the cultivated land

into built-up land. The built-up areas comprise buildings, pavements, parking lots, roads

and respective infrastructure. All these building materials and concrete contribute towards

the increase in land surface temperature of Lahore. Similarly, transportation and the

combustion of the vehicles add to more air pollution, creating troubles for health issues

and contributing towards smog problem the number, of industries in Lahore has also been

a contributing factor in raising land surface temperature. The heat island effect is

prominent in industrial areas. The assessment of environmental condition and measures

for the policy making for the protection of the environment can be carried out by

considering the above mentioned factors.

5.7. The Correlation between LST and Urban Land use Patterns

The correlation between land use type and land surface temperature was examined

for further understanding of the influence of urban expansion and development on land

surface temperature variation of Lahore. The characteristics of the relationship between

temperature variations related with different types of land use are summarized in Table

5.11. The map in Figure 5.23 represents mean LST by altered land use type. It is evident

that the densely populated, built-up and industrial areas in the city are a factor of

temperature increase. The heat consumed by a human body along with the heat stored in

the impervious structures that absorb heat in the day time and release during the night

Aziz

Bhatti

Town

Data

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g

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ar

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Mean LST 1990 22.71 24.38 24.42 23.63 23.76 24.35 23.35 24.77 23.53 22.91

Mean LST 2015 25.41 25.17 27.85 25.62 25.69 25.78 25.31 25.57 26.14 27.25

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time is also a contributing factor. The highest land surface and air temperature is always

found in the surroundings of urban built-up areas, open surfaces and industrial zones.

It indicates that the urban growth is contributing to the temperature raised by

substituting green cover with non-transpiring, non-evaporating surfaces of metal, stone,

asphalt and concrete. In contrast to that, the land under agricultural use, and growing

crops in the surrounding rural areas has cooler temperature as compared to the urban

environment. Areas under vegetation cover show much lower temperature in the last 25

years as the vegetation cover reduces the heat ratio stored in the surface and soil through

transpiration.

Table 5.11: Land Surface Temperature Variations with Different Land uses

Land uses

Year Built-up Area (LST) Vacant Land (LST) Agriculture (LST) Water (LST)

Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max Mean

1990 18.78 29.11 23.19 19.68 30.75 23.16 18.78 27.03 21.10 16.51 28.28 20.78

2000 18.54 29.89 24.22 17.44 27.53 22.49 17.01 24.51 20.76 16.99 24.22 20.61

2010 19.95 30.03 24.99 18.80 30.57 24.68 18.20 24.61 21.41 17.22 22.03 19.62

2015 17.86 33.84 26.17 17.41 33.27 25.63 19.12 31.60 24.87 19.13 28.47 21.71

Source: Computed from Landsat Thermal Images

There is a dire need of studying the effects of land use on LST and understanding

the characteristics of thermal signature of different land uses. Figure 5.23 indicates the

average surface temperature by each land use type during the span from 1990 to 2015.

The variations in surface radiant temperature reflect the effects of different land use types

on urban thermal setting, as shown in the Figure 5.23. The Table 5.11, illustrates that the

patterns of the distinguishing temperature are related to the thermal physiognomies of the

each land use types. To comprehend the urban expansion impact on LST, the thermal

signature of different land use classes was acquired by land use map overlaid with a LST

map of the same year. The maximum and minimum and mean values of LST by type of

land use classes are indicated in the Table 5.11 and in Figure 5.23.

It is evident that the built-up or urban land exhibited the highest mean temperature

(23°C in 1990, 24°C in 2000, 24°C in 2010, and 26°C in 2015), followed by vacant land

(23°C in 1990, 22°C in 2000, 24°C in 2010 and 25°C in 2015) as shown in Figure 5.23.

This highlights that the urban expansion is associated with the rising temperature by

transforming natural vegetation cover and agricultural land with urban structure i.e. non-

160

evaporating, non-transpiring exteriors such as asphalt, metal, stone and concrete. The

standard deviations of hotness values are less in relation for both land use classes,

demonstrating that the surfaces of urban do not exhibit big differences in surface

temperature due to the non-evaporating, non-transpiration, dry nature of urban material.

Water bodies experience minimum lower temperature as compared to other land use

classes (19°C to 21°C). Table 5.11 indicates the statistics of the surface temperature,

followed by the agricultural land (21°C to 24°C). Henceforth, the water bodies and the

vegetation cover are cooler as compared to urban built-up and vacant land.

Figure 5.23: Land Surface Temperature Variations with Different Land uses

Minallah, 2016

161

In Table 5.11, the results of land surface radiant temperature indicate the

variations in land use classes. The temperature of the densely populated residential and

commercial areas exhibit maximum temperature, followed by the medium densely

populated area, vacant land, vegetation and park area. It is evident that the impervious

surfaces are warmer as compared to the vegetative covered areas. Urban and suburban

areas experience development in the south and south western side in low-cost residential

housing schemes. Moreover, the industrial zone and the highly built-up areas contribute

to concentration of the heat island. The standard deviation value of the LST is

comparatively lower for the urban cover indicating that there is no such wide variation in

the urban surfaces because of non-evaporative and non-transpiring materials.

The minimum land surface temperature in 1990 was detected in areas with water

bodies (River Ravi) followed by (16°C) thick vegetation covers (18°C). The pattern in

2015 showed contrast where the minimum temperature was found at water bodies (River

Ravi), 17°C, followed by natural vegetation cover 19°C. This difference is due to eroding

vegetation cover by the year 2015. This modification in temperature pattern is attributed

to the difference in the state of vegetative area, solar illumination, atmospheric influences

and satellite remotely sensed TM, ETM+ and OLI_TIRs dataset.

The data acquisition in the same season showed difference in the surface

temperature of the water bodies. There is considerably low temperature in thick

vegetation zones in 2015, as the dense vegetation cover reduces the intensity of heat

absorbed in soil through the natural process of transpiration. Crop lands have thin vegetal

cover and naked soil. Various factors like water content, vegetation and surface soil are

key to contribute to the difference observed in the surface radiant temperature values. The

relationship between the land surface temperatures, texture of the land cover, land use

changes, influence the land surface temperature of Lahore. GIS and remote sensing

techniques with image processing help in visualizing the land surface alterations by urban

expansion.

5.8. Correlation between LST and indices

5.8.1. Relationship of LST to NDVI

NDVI served as an indicator of vegetal profusion in the area and is then used to

measure land surface temperature. The areas with the highest vegetal cover showed low

162

LST as the area under vegetation cover determines the land surface temperature through

the process of evapotranspiration from surface to atmosphere by latent heat flux. The

correlation between the NDVI and LST is valuable for the understating of urban micro

climate. The urban green spaces add to moisture in the air and reduce the impact of heat

in the urban climate. The highest NDVI values were found in the south and southeast,

where vegetal cover and cropland are mostly located in the city of Lahore. The lowest

NDVI values were detected in the densely residential and built-up areas with less

vegetation cover.

Figure 5.24: Spatial Distribution of LST and NDVI of Lahore in1990

Minallah, 2016

The Spatio temporal distribution of LST and NDVI can be exemplified from the

Figure 5.24 to 5.27. Correlation analysis from Pixel to pixel was carried out to determine

the association between temperature and NDVI. It is demonstrated in the Table 5.12 that

land surface temperature tends to be strongly correlated with NDVI values in all kinds of

land use in the study span of 1990 to 2015. Figure 5.24 to 5.27 indicate the LST values to

be strongly negatively correlated to NDVI. Through Spatio temporal maps of LST and

NDVI, it is demonstrated that the features in pixels in NDVI with high values have low

LST, while the regions with lowest NDVI values have higher LST values.

163

Figure 5.25: Spatial Distribution of LST and NDVI of Lahore in 2000

Minallah, 2016

Figure 5.26: Spatial Distribution of LST and NDVI of Lahore in 2010

Minallah, 2016

It also implies that the areas displaying lowest NDVI values have less vegetal

cover as a consequence of urban expansion, whereas the higher NDVI values have thick

164

vegetal cover, and therefore, temperature increases with the decrease in vegetal density.

In case of Lahore, a strong correlation is observed between LST and NDVI, which

ensures the prospect of using linear regression in predicting LST if the values of NDVI

are known.

Figure 5.27: Spatial Distribution of LST and NDVI in 2015

Minallah, 2016

Henceforth, accurate LST values can be predicted by using NDVI. It is observed

that the NDVI values have decreased from 1990 to 2015 due to urban expansion and

reduction of vegetal cover in the city of Lahore. Spatial distribution of NDVI is not only a

matter of influence of reduced green spaces, rather can also be attributed to availability of

solar radiation, topography and other factors. NDVI is generally utilized in measuring

Land Surface Greenness (LSG), established on the postulation that values of NDVI are

directly comparative to the area under vegetal cover in an image per pixel area. Table

5.12 indicates the relationship between LST and vegetation density.

Table 5.12: Relationship between Vegetation Density and LST

Year NDVI

Minimum

NDVI

Maximum

LST

Minimum

LST

Maximum

Correlation

(R2)

1990 -0.264706 0.664336 16.5149 30.7526 0.988

2000 -0.439494 0.766422 16.9988 30.9987 0.977

2010 -0.285714 0.732484 17.0471 31.5668 0.975

2015 -0.178791 0.590235 17.4071 33.8357 0.984

Minallah, 2016

165

Figure 5.28 with scattered plots indicates the relationship between LST and

NDVI. The regression line produced expressive explanation, showing strongly negative

correlation with LST. These values of correlation can easily be visualized by plotting LST

values for vegetation cover index. The consequences indicate that the thick vegetal

covered areas can reduce the effects of temperature and UHI. The strongly negative

correlation between the temperature and Normalized Difference Vegetation Index

specifies that the greater biomass of vegetation has lower LST. The NDVI and surface

radiance temperature have direct impact on changes in land use type. The values for four

periods of analysis showed that the NDVI and LST are correlated negatively at the

Pearson index, i.e. -0.994, -0.989, -0.988 and -0.992.

Figure 5.28 (a, b, c and d): Relationship between NDVI and LST form 1990 to 2015

y = -15.62x + 26.255

R² = 0.9888

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y = -11.68x + 24.953

R² = 0.9774

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166

Minallah, 2016

5.8.2. Relationship of LST to NDBI

Normalized Difference Built-up Index is materialized to extract built-up area from

urban land use and strengthen building information. The values of NDBI are related to

temperature to investigate the impact of built-up area on surface temperature. Positive

correlation in association between NDBI and temperature indicates that built-up area

increases surface temperature. The correlation between NBDI and surface temperature is

directly proportional to each other, regions higher in land surface temperature have high

density constructions as compared to the regions with no construction with lower

temperature. The level of urbanization and industrialization is reflected clearly in NDBI

index.

y = -14.056x + 26.606

R² = 0.9758

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2010 ( c )

y = -20.992x + 29.34

R² = 0.9841

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2015 ( d )

167

Figure 5.29: Spatial Distribution of LST and NDBI of Lahore in 1990

Minallah, 2016


Minallah, 2016

It can be demonstrated in the analysis of four NDBI index maps as shown in

Figure 5.29 to 5.32, that highest NDBI is found in the inner circle of the city, indicated

with red highlighted background. These areas with highest NDBI values are airports,

168

industrial zones and residential areas in the city of Lahore. The River Ravi has the lowest

NDBI index.


Minallah, 2016


Minallah, 2016

169

The lowest NDBI recorded in 1990 was in small agricultural area towards the

south and south east of the city. In 2015, however, the lowest NDBI index was found in

cropland in the south and southwest of the city. The higher NDBI values were found in

the central area of Lahore, concentrated by sky scrapers and concrete surfaces, parking

lots and high building density and the lower NDBI values were recorded in the east and

southeast outskirts (Figure 5.29 to 5.32). Furthermore, graphs of the scatterplots were

prepared to discover the correlation concerning surface temperature and NDBI.

Meaningful explanations were yielded by regression model in which NDBI values were

positively related with LST (Figure 5.33).

Table 5.13: Relationship between Built-up area and LST

Year NDBI

Minimum

NDBI

Maximum

LST

Minimum

LST

Maximum

Correlation

(R2)

1990 -0.645161 0.414634 16.5149 30.7526 0.989

2000 -0.623188 0.441441 16.9988 30.9987 0.977

2010 -0.521127 0.492228 17.0471 31.5668 0.984

2015 -0.607582 0.751279 17.4071 33.8357 0.984

To examine the relationship between LST and NDBI, sample points were

randomly selected from land surface temperature and NDBI maps were utilized to

produce regression fitting and to estimate coefficient of Pearson correlation (Figure 5.33).

The values for four periods of analysis showed that the NDBI and LST are correlated

positively at the Pearson index, i.e. 0.995, 0.989, 0.992 and 0.992. NDBI was effeciently

used to characterize the changes in LST.

Figurer 5.33 (a, b, c and d): Relationship between NDBI and LST form 1990 to 2015

y = 13.681x + 24.72

R² = 0.989215

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170

Minallah, 2016

y = 13.232x + 24.245

R² = 0.977315

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2000 ( b )

y = 14.017x + 23.809

R² = 0.9843

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y = 11.867x + 24.17

R² = 0.984215

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171

5.9. Cross Validation of Satellite and MET Station Data

Table 5.14 and 5.15 illustrate the cross authentication of LST of Lahore computed

from Landsat thermal images with Lahore urban and rural MET observatory data. The

statistical data of estimated LST of the year 1990 show that mean LST was 23.6°C while

the mean LST was 25.6°C in 2015. The mean LST increased by 2°C between the periods from

1990 to 2015. The atmospheric temperature data analysis show that mean atmospheric

temperature was 22.6°C while the mean atmospheric temperature was 23.4°C in 2015 at urban

MET station. The mean atmospheric temperature increased from 1990 to 2015 by 0.8°C in both

urban and rural Met stations. So both the data sources satellite and MET Station data,

confirm that the temperature of Lahore has increased Sfrom 1990 to 2015.

Table 5.14: Cross Validation of LST with Lahore Urban (PBO) MET Station Data

Date of

Acquisition

Acquisition Source

Satellite (LST) Urban (PBO)

Max (°C) Min (°C) Mean (°C) Max (°C) Min (°C) Mean (°C)

16-03-1990 30.7 16.5 23.6 27.9 17.3 22.6

19-03-2000 30.9 16.9 23.9 27.6 14.0 20.8

07-03-2010 31.5 17.0 24.3 25.5 15.6 20.6

21-03-2015 33.8 17.4 25.6 28.5 18.3 23.4

Table 5.15: Cross Validation of LST with Lahore Rural (APT) MET Stations Data

Date of

Acquisition

Acquisition Source

Satellite (LST) Rural (APT)

Max (°C) Min (°C) Mean (°C) Max (°C) Min (°C) Mean (°C)

16-03-1990 30.7 16.5 23.6 28.6 15.1 21.9

19-03-2000 30.9 16.9 23.9 27.5 15.6 21.6

07-03-2010 31.5 17.0 24.3 25.5 16.5 21

21-03-2015 33.8 17.4 25.6 27.7 17.7 22.7

Figure 5.34: Comparison between LST with Urban-Rural MET Stations Data

Source: Minallah, 2016; PMD, 2016

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LST (Satellite) Met. Urban (PBO) Met. Rural (APT)

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16-03-1990 19-03-2000 7/3/2010 21-03-2015

172

The land surface temperature values obtained in this research from the thermal

images are likely to be a bit higher than the air temperature because of the roughness and

coarseness of the land apparent which exercises impact on the surface radiance

temperature. It is also noted in the present study that effective measurement of the land

surface temperature must confirm to the importance of the nature of apparent, its

unevenness on emissivity and then should be incorporated. The emissivity values should

be obtained for various land use types and incorporated for the estimation of land surface

radiant temperature.

5.10. Urban Heat Island of Lahore

An urban heat island formed in a cosmopolitan city experiences warmer

atmosphere as compared to its immediate rural vicinity. This is attributed to the

anthropogenic activities pursued in the urban areas. Urban landscape also experiences

changes due to developments including building, streets, roads and other infrastructure by

replacing the vegetation cover with impervious surfaces while the permeable surface with

moist becomes impermeable and dry. These land use changes contribute to urban areas

which become warmer as compared to the countryside, ultimately forming a heat island.

The process of evaporation of the water in plant cools the surrounding areas.

In Figure 5.35 (a & b), the minimum and maximum temperatures as change in the

air temperature on the annual basis are shown in the rural and urban MET observatories.

The regression results of annual air temperature are given in the Table 5.16 and 5.17,

showing the net change in minimum and maximum air heat for the time span of 1950-

2015. Both the countryside and urban MET observatories demonstrating the minimum

and maximum air temperature were indicating increase at different rates. It is significant

to acknowledge that the minimum temperature increased in the urban stations as

associated to the countryside stations.

On the other hand, the study period from 1950-2015 has no momentous variation

in the maximum temperature in either of the stations. In the given Table 5.16, it is

indicated that the highest temperature increase was observed and measured to be 1.38°C

in the urban station. The decrease in the minimum temperature at Lahore airport station,

is observed and measured to be -1.44°C. The decreasing trend in the minimum

temperature is also observed in airport station.

173

Table 5.16: dTmin and dTmax over the period of 65 years (1950-2015) at Lahore

urban station and Lahore airport rural station

Period dTmin/6.5 decades dTmax/6.5decades

Lahore (PBO) Lahore (APT) Lahore (PBO) Lahore (APT)

Annual 1.38 -1.44 -0.47 -1.75

Minallah, 2016

Table 5.17: Regression results of temperature of Lahore urban station and Lahore

airport rural station during 1950 to 2015

Period Minimum Temperature Maximum Temperature

Lahore (PBO) Lahore (APT) Lahore (PBO) Lahore (APT)

Annual

y = 0.0386x +

16.983

y = 0.002x +

17.382

y = -0.0135x +

31.256

y = 0.0013x +

30.469

R² = 0.6014 R² = 0.0044 R² = 0.1325 R² = 0.0016

Minallah, 2016

Figure 5.35(a) illustrates the analysis and variability of mean maximum air

temperature of both the stations (urban and rural). The graph of maximum temperature

change trends in the urban areas shows tendency of increase from 1950 to 1998,

especially the year 1998 which was declared to be the warmest year in the history. Before

1998, the difference in the annual mean maximum temperatures of both the stations (rural

and urban) was higher as compared to the trend of increasing temperature till 1998, at

urban station in particular. The period after 1998-2015, the mean maximum temperature

of urban station shows decreasing trend while mean maximum temperature of rural

station shows increasing trend.

The Figure 5.35(b) indicates that the time period from 1950-1967 experienced

almost same trend in temperature change at both the stations, but afterwards, the period

from 1968-1994, the trends seem to differ in minimum temperature in urban station as

compared to the rural station. The year 1995 onwards, the minimum temperature of

Lahore started increasing at a rapid pace as the city was the center of massive urban

development and anthropogenic activities. The Figure 5.35 (b) also highlights the effects

of urbanization on the urban temperature. An analogous proportion is observed in the

temperature trends of increase at urban station, owing to the population growth

throughout the study period. The increase in population growth keeps escalating the

minimum temperature which affects the mean annual temperature of Lahore.

174

Figure 5.35: The mean maximum & minimum temperature variations of Lahore at

Lahore Airport and Shadman observatories.

Source: PMD, 2016

The long-term urban heat island of Lahore as highlighted in Figure 5.36 shows the

trends of temperatures of the two MET stations (rural-urban) of Lahore. The urban site

MET station is situated at Shadman while the rural site at Lahore airport. The difference

in the air temperature (both minimum and maximum) of urban and rural, is observed to be

increased, owing to the effects exercised by urban heat island phenomenon (Figure 5.36).

The massive change in the urban growth and population explosion related with the land

use alteration contributed to the changing temperature of urban areas. The transformation

of the vegetal cover into built-up land use has higher impact on minimum and maximum

air temperature (Figure 5.35 (a & b)), changing the mean annual temperature of Lahore. It

is also noted that the urban sprawl and population growth increased the impervious

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surfaces, affecting the minimum temperature more as compared to the maximum

temperature.

Figure 5.36: Urban and rural site Temperature Trends to represent long term UHI

Source: PMD, 2016

The Figures 5.37 and 5.38 display the urban heat island and some hotspots of

temperature over Lahore. In the urban areas, the impervious surfaces preserve heat and

create urban heat island. Besides the impervious surfaces, some of the other factors also

affect the UHI. In large metropolises, the preservation of heat is higher than that of the

smaller cities. Fig 5.37 & 5.38 show the spatial variability of heat island of Lahore where

the densely built-up area, industrial area and the urban land use change demonstrate

higher heat island effects as compared to the country side. The Figures 5.37 and 5.38 also

highlight the maximum temperature of urban heat island in the industrial area,

commercial area, transport network and densely built-up areas.

It supports the fact that concentration of heat in urban area is due to the

impervious structure which affects the local climatic conditions. The distribution of

surface land temperature variations is presented in the Figures 5.37 and 5.38. It is due to

the vertical developments in the city entre and new developments are found at the

peripheries of the city. The original land cover has been destroyed by the new developing

sites that enhance LST.

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Figure 5.37: Presence of Urban Heat Island in 1990

Minallah, 2016

Figure 5.38: Presence of Urban Heat Island in 2015

Minallah, 2016

177

The areas with higher land surface temperature values are identified to confirm the

contributing factors for developing heat island. It is observed that highest Land surface

temperature values are found in industrial areas, vacant lands, densely populated and

built-up areas. The higher land surface temperature at vacant lands can be recorded as

activities related to the development carried on in those areas. In the same vein, the urban

micro-climate is affected by the ongoing development activities in the new housing

schemes in the urban area. The direction of the expansion of the city is also identified in

the vacant lands which show higher temperature.

Research related to urban heat island has significant implications in terms of

health issues pertaining to human beings. The consequence of the heat island, especially

in the hot summer season, can have severe implications in climatic change, at night in

particular. Heat that emerges round the clock allows no respite for the inhabitant

including those, who do not intake much quantity of water at night to stay hydrated. The

facility of air conditioning is not available for everyone in the underdeveloped countries

and poor neighborhood, besides it offers relief in hard environmental conditions, giving

rise to the outside temperature as the air conditioner consumes energy and exhausts heat.

In the conclusion of the present research, the LST variations are examined and it

is indicated that there is a momentous variation in the land use in terms of temperature

comparisons. In built-up areas, higher temperature is recorded, followed closely by the

vacant land, while the temperature is less in the areas with water bodies and vegetation. It

is because of the albedo along with the thermal capacity of different land use. It has also

been demonstrated that thermal data received from satellite can be materialized to

measure the spatial extent and magnitude of the urban heat island. An investigation of the

environment can be made by utilizing satellite remote sensing techniques efficiently and

effectively.

178

CHAPTER 6: SUMMARY, CONCLUSION AND

RECOMMENDATIONS

6.1. Summary

The present study used remote sensing techniques to evaluate urban expansion in

terms of the amount of expansion, the location of expansion and the rate of expansion that

has taken place in Lahore during the period from 1951 to 2015. The integration of GIS

and remote sensing techniques has brought about an effective and efficient approach to

identify urban expansion and to assess the impact of urban expansion on land surface

temperature. The study has shown that Lahore has experienced widespread changes in its

land use during the period from 1972 to 2015 at an annual rate of change of 4.46% in its

built area. From the present research it is deduced that, from 1951 to 2015, Lahore has

undergone a massive urban expansion associated with land use changes and this

phenomenon subsequently has resulted in the loss of non-urban lands such as agricultural

land and vegetation cover leading to modification of the thermal characteristics of the

urban land surface in Lahore. In the present study, image classification techniques of

remote sensing provided a comprehensive understanding of the nature, extent, rate, trends

and location of urban expansion and consequential thermal modification. This rapid urban

expansion of Lahore is the outcome of the accelerating economic and industrial activities

along with increase in its urban population as a result of rural to urban migration since

1951.

In the present era, the prevalent circumstances leading to change in land use

patterns and land surface temperature of Lahore, Pakistan are not strange phenomenon in

the urban world, as metropolitan and urban centers of the world have observed the threats

of visible change in urban land use and increase in land surface temperature. The impact

of urban expansion on urban micro-climate has been observed throughout the world and

the metropolitan cities of developing countries like Beijing (Liu et al., 2007), Delhi

(Mohan, 2013), Lahore (Sajjad et al., 2009 and 2015) and Shanghai (Chen et al., 2016),

are not safe and are subject to the environmental change due to the rapid urbanization.

Besides the economic, social and psychological effects of massive urbanization on the

natural environment, the process of urban expansion and its impact on temperature can be

measured quantitatively.

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According to the present study, since 1951 Lahore, has experienced remarkable

population growth, expansion and developmental activities and they have serious impact

on the urban climate of Lahore. The total urban built-up area of Lahore was 66 km2 in

1951 as shown in Figure 4.12a. The population of Lahore, as reported in 1951, was 1.135

million which increased to 1.626 million in 1961 and its average annual growth rate was

3.66% (GoP, 1961). The inter censual increase of population of Lahore during the decade

from 1951 to 1961 was 0.491 million. During the decade 1951-1961, the increase of

population of Lahore was noted to be 43.3% (Table 4.3).

During the next period of 1961-1972, total urban built-up area was increased to be

170 km2 as shown in Figure 4.12b. Since 1961, the population growth of Lahore began to

increase at a high rate. In the period between 1961 to 1972, Lahore grew at an

unprecedented average annual growth rate of 4.06% (Table 4.3) and during this period the

population of Lahore increased from 1.63 million in 1961 to 2.59 million in 1972 (GoP,

1972). The inter censual increase of population of Lahore during 1961-1972 was 59.2%

(Table 4.3).

The change in urban land use profile of Lahore continued from 1973 to 1980. In

1973, the urban/built-up land of Lahore was 223.96 Km2 which increased to 273.29 Km2

in 1980. It is also noted that the agricultural land decreased from 1213.23 Km2 in 1973 to

1170.57Km2 in 1980 as shown in Table 4.14 and Figures 4.13 and 4.14. During the period

from 1973 to 1980, the urban built-up area increased about 49.33 Km2 (22%) while

agricultural land reduced 42.66 Km2 (4%) as presented in Table 4.15. The population of

Lahore increased rapidly, up to 3.54 million in 1981 which was about 2.59 million in

1972 with annual population growth rate of 3.8% (GOP, 1984). About 1 million people

were added to the total population of Lahore in just one decade, 1972-1981.

During the period 1980-1990, the urban built up area of Lahore increased from

273.29 km2 in 1980 to 352.75 km2 in 1990 as shown in Table 4.14. During this phase, the

changed in urban built-up area of Lahore is 79.46 Km2 while agricultural land constantly

transformed and decreased 52.75 Km2 as presented in Table 4.15, while the population of

Lahore increasing rapidly, was 3.54 million in 1981 with the estimated population in

1990 was 4.95 million. An increase of 1.41 million people was reported to be added in

population of Lahore between 1980 and 1990. During 1980-1990, urban area expanded

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and included many rural settlements. The city saw rapid expansion eastward and in

southeast direction.

From 1990 to 2000, the urban built-up area of Lahore increased from 352.75 km2

in 1990 to 445.12 km2 in 2000 as shown in Table 4.14. The net accumulation of more

than 92.37 km2 (Table 4.15) of urban built-up land during the period 1990 to 2000 has

converted from agricultural and vacant land use to urban built-up area. The land used for

agriculture reduced from 1117.82 km2 in 1990 to 1062.25 km2 in 2000 (Table 4.14). The

population of Lahore was 4.95 million in the year 1990 and increased to 6.319 million in

1998. An increase of 1.369 million (GoP, 2000) people was reported in the population of

Lahore during the period 1990 to 2000. The average annual growth rate was 3.5%, during

the period from 1981 to 1998 whereas inter-censual increase of population of Lahore was

78.3%. From 1981 to 1998, an increase of 2.74 million of people was recorded in the

population of Lahore.

During the years 2000 to 2010, the built-up area of Lahore enlarged from 445.12

km2 in 2000 to 517.43 km2 in 2010 as shown in Table 4.14 and Figures 4.16 and 4.17.

From 2000 to 2010, the urban built-up land increased to 72.31 km2 (16%) while

agricultural area reduced to 57.26 km2 (5%) and vacant land 11.54 km2 (5%) decreased

from the land use profile of Lahore. During the years 2010 to 2015, the total urban land

recorded was 643.51 km2. The results indicate that in 2010 the total built-up area in

Lahore was 517.43 km2 as shown in Table 4.14 and Figures 4.17 and 4.18. It increased to

643.51 km2 in 2015, thus recording a growth of 57.05 km2 (24%) in urban built-up land.

During this time period, 2010 to 2015, agricultural land was continuously changed and

reduced 34.35(15%) as presented in Table 4.15. The Spatio-temporal analysis revealed

that 419.55 km2 (Table 4.15) of the urban built-up area increased from 1972 to 2015. The

population of Lahore was 2.59 million in 1972, while the population of Lahore was

recorded 9.55 million in 2015 (Estimated). With the growth of population, housing

demand and city development increased and cultivated land was converted into land for

housing colonies, industrial zones and roads. The new housing schemes were approved by

LDA at the cost of fertile agricultural land, to accommodate the demands of the

increasing population of 6.97 million people during the period 1972 to 2015. The targeted

areas under the expansion are south and south east and west along the major roads. As a

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consequence, the land surface temperature of urban land surface has increased in

comparison with countryside.

The prospect of land use modification as well as climax of the parameters of urban

expansion is a factor of increase in temperature in Lahore. Three different data time series

of MMxT, MMiT and MAT are used to encounter the arguments on the trends and

temporal changes in atmospheric temperature from 1950 to 2015. The outcomes

emphasize that there is no significant change in minimum and maximum air temperature

of Lahore at the airport station (Appendix 01). Figure 5.15 shows that minimum air

temperature rose more than maximum air temperature at urban station. There is not any

significant increase in the maximum atmospheric temperature at both stations, so the

mean temperature of Lahore is not significantly affected by the mean maximum

temperature. It is evident from the analysis that the increase in minimum temperature is

recorded in the urban station related to the rural station situated at the airport which is an

open area. The maximum temperatures recorded at both of the stations have not

experienced any significant change in the time span of 65 years. After 1995, the minimum

temperature started increasing rapidly as Lahore had been undergoing massive urban

development since 1981. It is observed that the increase in minimum temperature at urban

station is due to the population of the city which increased momentously during the study

period. It has been reflected by the results that the change in temperature, over the span of

sixty five years of the study period, is higher for MMiT as compared to MMxT of Lahore.

The Spatio-temporal distribution of emissivity corrected land surface temperature,

has been computed for the period from 1990 to 2015 of Lahore by thermal images. The

statistical data of estimated land surface temperature of the year 1990 shows that the

minimum LST was 16.51°C while the maximum LST was 30.75°C. The mean land

surface temperature was 23.63°C in 1990 as shown in Figure 5.17 and Table 5.9. The

minimum LST in 2000 was 16.99°C while maximum LST was 30.99°C. The mean land

surface temperature was 23.99°C in 2000 as shown in Table 5.9 and Figure 5.18. The

increase in mean land surface temperature of the study period, from 1990 to 2000, is

noted to be 0.36°C as presented in Table 5.10. The land surface temperature ranged from

17.21°C to 31.57°C as shown in Figure 5.19, with a mean land surface temperature of

24.38°C in 2010 as shown in Table 5.10. The increase in mean temperature of the study

period, from 2000 to 2010, is noted to be 0.39°C (Table 5.10). The readings of the LST

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for the year 2015 show that the highest maximum surface temperature is 33.83°C and the

minimum land surface temperature is 17.40°C as shown in Figure 5.20. The mean land

surface temperature for the year 2015 was 25.62°C. The increased mean land surface

temperature of the study period, from 2010 to 2015, was noted to be 1.23°C as shown in

Table 5.10. The increased mean land surface temperature of Lahore, from 1990 to 2015,

is noted to be 1.98°C as illustrated in Table 5.10. After making the land surface

temperature variation maps (Figures 5.17 to 5.20), it is indicated that the highest land

surface temperature values existed mostly in the center of the city, also known as walled

city, featured by densely built-up area, commercial centers and deep street canyons. The

relatively higher land surface temperatures are observed in industrial zones, urban centers

and highly densely built up areas.

The map in Figure 5.21a represents Town wise LST in March 1990. Town wise

comparison of land surface temperature of Lahore for the year 1990, reflects that the

areas of high temperature are Shalamar Town (24.77°C), Gulbarg Town (24.42°C), Data

Ganj Baksh Town (24.38°C), Ravi Town (24.35°C), Nishtar Town (23.76OC) and Iqbal

Town (23.63OC) as shown in Figure 5.21a. On the other hand, town wise LST map of

March 1990 as shown in Figure 5.21a exhibits that low land surface temperature areas are

Samanabad Town (23.35°C), Wagha Town (23.53°C), Aziz Bhatti Town (22.71°C) and

Cantonment (22.92°C) of Lahore. For comparison, LST is also measured for March 2015

as presented in Figure 5.21b. It is observed in Figure 5.21b that Shalamar Town

(25.57°C), Gulbarg Town (27.85°C), Data Ganj Baksh Town (25.17°C), Ravi Town

(25.78°C), Nishtar Town(25.69°C), Iqbal Town (25.62°C), and Samanabad Town

(25.31°C) have been warmer in March 2015 than March 1990. According to the

assessment, 1.98°C (Table 5.10) land surface temperature of Lahore has increased in last

25 years from 1990 to 2015.

In the conclusion of the present research, the LST variations have been examined

and it has been noted that there is a relationship between modification in the land use and

temperature as displayed in Figure 5.6, 5.7 and 5.23. In built-up land, the highest

temperature is recorded, followed by the vacant land, while the temperature is lower in

the areas with water bodies and vegetation as shown in Figure 5.23. The analysis of

relationship between the remote sensing indices and LST shows that NDVI values

indicate negative correlation between LST, while NDBI values indicate positive

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correlation between LST with the thermal values. In most of the densely populated and

industrial areas of Lahore, high temperature is being experienced. The process of

intensification of land surface temperature of Lahore is gradual. One of the major issues

in intensification is the reduction in agricultural land in the vicinity of the city area. It is

significant to note that the cultivated land and green spaces had been transformed into

built-up areas and eventually in impervious surfaces, resulting in an increase in


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6.2. Conclusion

The land surface temperatures in urban areas are increasing gradually as a

consequence of massive land use changes taking place due to urban expansion. It has led

to reduction in agricultural land and loss of vegetation in cities around the world. Cities

are multifunctional centers of industrial and anthropogenic activities. These functions are

causing urban growth and indicate the impact of urbanization on local climate. Since

1800, studies concerning the recognition of rising temperature phenomenon in urban

areas have increased manifold. A number of scholars (Howard, 1818; Harwood, 2008,

Gabler et al., 2009, James et al., 2014) documented that temperatures in urban areas,

varied from those of the nearby countryside, in line with the greenhouse effects produced

by use of carbon fuelled machinery. UHI is a quantifiable pocket of warm air produced by

a large urban area. This comes as to no surprise as cities signify areas with higher density

of population and centre of concentration of human activities. The cities, in this regard,

consume 60 to 80% of energy produced globally and are contributing to CO2 emission

with equal share (OECD, 2010). A number of studies indicate that the land surface

temperatures of cities are generally (1-6°C) warmer than those of the adjacent rural areas

(Gabler et al., 2009). Several contributing factors include energy use, automobile,

industry, heat generating human activities, thermodynamic capacities of material,

structural geometry and impervious surfaces are responsible for storage of heat and re-

radiation of heat in the atmosphere. These factors in turn change the conditions that alter

the near-surface atmospheric temperature over the urban areas.

The findings of the present study reveal that the city of Lahore over the last few

decades has experienced a rapid population and urban growth. The expansion of urban

areas in Lahore city has influenced the local climate. The urban climate of Lahore is

affected not only by factors of global climactic change in the South Asian region but

indigenous factors of this change have also exercised an adverse impact. The emission of

CO2 in particular, and greenhouse gases in general, contribute towards urban warming in

Lahore. The significant variations in temperature trends of Lahore show increase in

temperature during various years. Moreover, land surface temperature is also

progressively rising in Lahore. One of the leading causes is reduction in the agricultural

area in the city. This notable change creating the climatic condition is known as ‘Urban

Heat Island’ in Lahore. Lahore is undergoing rapid expansion of urban areas and it has

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caused development of urban heat island. The UHI phenomenon requires the

comprehension of distribution of LST and spatial variations in order to investigate its

mechanism and locate possible solution. Urban heat island effect in Lahore can be

compared with the other major cities of the world as London (Kolokotroni et al., 2006),

Beijing (Liu et al., 2007), Tokyo (Fujibe, 2011), Delhi (Mohan, 2013), Lahore (Sajjad et

al., 2009 and 2015) and Shanghai (Chen et al., 2016) and the contrast calls for

modification in action plans to mitigate UHI effects.

The findings of the study elaborate that the night time temperature increase in

Lahore has also been the main source of change in climatic conditions on local scale. The

construction material is mostly concrete, asphalt and metal. Impervious surfaces absorb

radiation during the daytime and contribute to increase in temperature at night when the

heat absorbed during the day is emitted. The heat radiation in the atmosphere stays and

eventually cause an increase in the value of minimum temperature as speed of the wind in

the city is less than those of the surroundings. The results of the present study showed a

positive relationship between the urban density, air pollutants and increased temperatures.

For the purpose of analysis between air pollutant and air temperature, air quality samples

were collected from different locations in the city of Lahore, which show high population

density areas have high temperature (Appendix 3).

According to the findings of the present research, it is observed that the massive

increase in urban growth resulted in an increased emission of GHGs, particularly CO2,

Carbon monoxide and Sulfur dioxide, forming smog over the city in the shape of

suspended-particle laden layer of thick cloud. Lahore is the fourth worst city for smog

among the ten worst cities of the world for smog (Vergin, 2014). One of the factors

contributing to smog in cities is alternate energy resource in the industries; coal, wood

and other pollution producing fuels. Moreover, the traffic jams in the city also enhance

the emission of Carbon monoxide in the city, creating a thick film of smog. The cloud of

smog traps the emitted and reflected radiation from the surface of the Earth and produces

greenhouse effect. This effect is a major cause of increase in the minimum temperature of

the city. It also affects the mean annual temperature of Lahore. Geographically, the city of

Lahore comes in temperate zone (Low Latitude), located at 31°34' N latitude, where

temperature is comparatively higher, and wind cannot cross through the city easily.

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During winter, the city remains under the spell of low visibility as perpetual twilight

sustains over the city, the pollutants get stuck in the air and rains are generally rare.

The findings of this research lead us to conclude that the population growth of

Lahore was recorded and estimated from 1.13 million in 1951 to 9.55 million in 2015

(GoP, 2015). The population density of Lahore has also increased from 641 to 5,386

persons/km2 from 1951 to 2015. Especially the period of 1981-1998, the population of

Lahore increased from 3.5 million to 6.3 million respectively, adding three million more

people in the population of Lahore. The average annual population growth rate in the year

1981 was 3.5%. The next period, 1998-2015, experienced a further increase of 6.3 to 9.5

million with population growth rate 3.3 in the year 1998. Almost 3.2 million more people

were added to the population of Lahore. Both the periods of population analysis

confirmed the rapid urban population growth of Lahore. The massive urban population of

Lahore has practically more impact on its urban micro-climate.

The results revealed a substantial increase in the built-up area and impervious

structure for the period 1951 to 2015. The increase recorded in built up area of Lahore is

momentous as it shot from 66 km2 in 1951 to 643.51 km2 in 2015 and has caused an

increased land surface temperature and the urban heat island to experience as well. The

Spatio-temporal analysis reflects that 419.55km2 of the urban built up area has been

increased while 297.52 km2of agricultural lands has been reduced from 1973 to 2015. The

agricultural land of Lahore is noted to be reduced from 1213.23 km2 in 1973 to 915.71

km2 in 2015. The rate of change measured for the urban expansion of Lahore is 9.98% per

year with expansion intensity of 0.56% for the period from 1973 to 2015. The increase

can also be evaluated by the fact that the built-up area was 66 km2 in 1951, while it grew

to 643.51 km2 in 2015. The temporal analysis reflects that 577.51 km2 of the urban built-

up area has increased from the year 1951 to 2015. It has also been noticed that remote

sensing and GIS techniques are efficient for analyzing increased surface temperature with

regard to reduction in vegetation cover and increased urban development.

The results indicate that the land use changes and urban development increased

the mean land surface temperature of Lahore by 1.98°C during the study period from

1990 to 2015. The mean LST was 23.63°C in the year 1990 while it was 25.62°C in 2015.

The Spatio-temporal comparative analysis of temperature shows that the maximum land

surface temperature is 30.75°C while the minimum land surface temperature is 16.51°C

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as recorded in 1990 as compared to the temperature of 2015, which shows increase when

maximum land surface temperature is 33.83°C while the minimum is 17.40°C. Hence, the

huge alteration in urban land use with the reduction in the urban green spaces, has led to

an increased surface temperature and intensity of urban heat island in Lahore.

The mean atmospheric temperature increased by 1.69°C between the period from

1950 to 2015. The mean atmospheric temperature is 23.43°C in 1950 while mean

atmospheric temperature is 25.11°C in 2015 at urban MET station. It has been noted that

the minimum temperature increased which affected the mean annual temperature of

Lahore at the urban MET station as compared to the rural station. It has also been

observed that the minimum temperature after the year 1995, started increasing at a faster

rate as Lahore experienced rapid urban development and increased urbanization in this

period. The minimum temperature is subject to increase in mean annual temperature

(MAT) of Lahore, especially after 1990s. It is noteworthy that the minimum and

maximum temperature differences in urban and rural stations have shown in Figure 5.35

and accelerating trends since 1995, due to the impact of urban expansion on temperatures.

As the green spaces are converted into the impervious surfaces, the minimum temperature

is affected more than the maximum temperature by the conversion of natural land into

urban structure. This study also observed the impact of urban sprawl on minimum

temperature which is lot more extensive than on maximum temperature.

Lahore is the second largest city of Pakistan in terms of population size and is also

identified as being more vulnerable to increased land surface temperature. However, this

change in the temperature trends and land use of Lahore is not something unique as many

global cities of the developed world are threatened by the precarious change in LST.

Therefore, the effects of urban expansion on local climate are observed to be changing all

over the world. Similarly, cities in the developing world like Lahore are also in danger in

terms of its environmental changes due to urban expansion. For instance, increase in

mean annual temperature of Seoul, South Korea by 1.5°C (Chung et al., 2004), increase

in mean annual temperature of Sao Paolo Brazil by 2°C (Freitas et al., 2007), the

temperature increase of Dhaka (Alam and Rabbani, 2007), tendency of increase in

temperature of Beijing (Liu et al., 2007), increased temperature from 0.28 to 0.44°C in

the Yangtze River delta (Du et al., 2007), raised temperature of Jimeta-Yola, Nigeria by

9°C, (Zemb et al. 2010), increased temperature in Tokyo by 3°C, where the warming rate

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is higher at night time (Fujibe, 2011), Delhi maximum urban heat island reached 10.7°C

from 8.3°C (Mohan, 2013), raised LST in Bangkok due to impervious surfaces (Hokao et

al., 2012), increased urban heat island effect in Istanbul due to the increased artificial

surfaces (Balçik, 2014) and severe increase in surface urban heat island in Shanghai. It

has been identified that the conversion of agricultural lands into built-up areas is the

major source of increase in temperature in Shanghai (Chen et al., 2016). All the

aforementioned studies conducted in major cities of the world have confirmed the adverse

effect of urban expansion and urbanization on the local climate change. Lahore, one of

the big cities of the developing countries, is facing an environmental threat in terms of air

and surface temperature increase and change in land use due to urban expansion and

anthropogenic activities. The findings of the present study conclude that land use change

and anthropogenic activities have significant impact on the local climate of Lahore, and

this study can offer scientific reasoning for the future planning of the land to be used,

which is directly proportional to the climatic change.

In the present research, satellite remote sensing provides data for the assessment

of land surface temperature variation as this technique offers the opportunity to collect

data of larger areas simultaneously. It is not feasible to collect data over hundred square

kilometers for the assessment of land surface temperature by using instruments and

survey method as it would yield point data to be interpolated to have an access to wide

area coverage. Through remote sensing, it becomes easy to access data of a wide-area

coverage and reduces the problems of approachability. Satellite Remote Sensing

expedites the process of mapping land surface temperature variations and land use change

and intensity of urban heat island at the local and regional scale, as this technique is both

cost effective and accurate in assessment.

Finally, the integration of remote sensing and GIS techniques has demonstrated

that it is an effective and efficient methodology for analyzing and monitoring patterns of

urban expansion and its impact on land surface temperature. Moreover, the present

research shows that the integration of proportionate urban built-up area and greenery

spaces can provide a significant measure to reduce urban heat island effect. The

conclusions drawn from the study signify that the development and the maintenance of

green spaces is critical in sustainable urban planning. These measures reduce the urban

warming and associated effects of the climatic change.

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6.3. Recommendations

The present study aims at utilizing both readings of temperature from satellite

based LST and meteorological measurements to assess the impact of urban expansion on

land surface temperature of Lahore using remote sensing techniques. Further studies

related to the phenomenon of urban expansion and heat island can be carried out in the

future by keeping in view the recent high resolution satellite (SPOT) images to measure

the current trends, rate and directions of the urban expansion and to monitor the current

land use patterns of Lahore. Eventually, the impact of urban expansion can be analyzed

and assessed by utilizing the current thermal high resolution images in order to measure

the land surface temperature as well as to determine the trends and magnitude of urban

heat island. Furthermore, the population growth, number of vehicles and industrialization

can be monitored in terms of emission of gases which are a potential source of

temperature increase.

Higher resolution satellite imagery is recommended to investigate the

quantification and the classification of land use type for the detailed analysis of different

land use classes. The pixel based analysis and distinguished resolution of the imagery

provide precise and accurate results. It is recommended that for the retrieval of

temperature, ASTER & MODIS data should be utilized for the improvement in the

estimation of LST. The distribution of the each land use type and pattern can be further

investigated in terms of its impact on LST in the urbanized area. The study of the

seasonal changes of temperature correlating with different land uses is recommended. The

future researchers in the field should also try to provide information relating to the surface

temperature that exercises impact on the soil moisture.

Future researches established on the correlation between LST and NDVI are

pertinent for the estimation of temperature in less-vegetated or non-vegetated surfaces,

including water bodies, vacant lands, and man-made features, dead or stressed vegetation.

The influence of the each parameter will have significant potential for classification of

urban thermal environment with more advanced thermal infrared images. It is

recommended that further research may present the Vegetation Sensitivity Index (VSI) a

matric for assessing the sensitivity of a particular environment especially its vegetation

with respect to climatic change. VSI, is a new indicator of vegetation sensitivity to

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climate changeability and sensitivity index of vegetation productivity to a changing

climate based on water availability, temperature and cloudiness.

Further studies in the future are required to be more focused on the improvement

in retrieval techniques of land surface temperature to reduce the effects of inhomogeneous

atmospheric condition and thin cloud. Various atmospheric effects including variable

surface emissivity, partial water vapour absorption, sub pixel variation of land surface

temperature and urban structure and geometry affect the estimation of LST. It is,

therefore, suggested that these factors may be given consideration while computing the

accurate temperature in future studies. The evaluation of the future land use scenarios is

also recommended to make balanced strategies in spatial morphological arrangements of

different land use. It will also offer alternative to moderate the hot land use types by cold

ones.

It is pertinent to explore the patterns of anthropogenic activities and their impact

and dynamics that may reduce the urban heat island effects, ultimately decreasing global

warming. The errors in every land use type are needed to be removed by estimating

temperature accurately. The patterns of the study can also be extended for the estimation

of GHGs from the satellite data and to relate it with the observations of ground

observatories of CO2 and to comprehend the real-time variation of CO2 intensity on earth

surface.

Change in LST of Lahore in not only the result of alteration in urban land use of

Lahore but also the repercussion of multifarious changes which have been taking place in

cities/places near Lahore particularly in neighboring states of Punjab and Haryana (India)

where every year in the months of October/ November paddy fields are burnt to prepare

fields for winter crops like wheat. This factor is interesting and can further be investigated

in future research in this area of prime importance. The growing number of thermal

power plants to produce electricity in the province may also be responsible for increasing

temperatures of Lahore. In conclusion, these parameters are recommended to be

studied/analyzed in future research in this field.

The method of applying the remote sensing techniques as used in this research can

be applied as a substitute for traditional empirical observations in finding the in-situ data

for analysis of environmental and climatic change studies. It is recommended that the

191

similar methodology be utilized for the analysis of the regions other than Lahore in

Pakistan that experience massive urbanization. The researches in the future will also

provide the recommendations focused on managing thermal environment in accordance

with the land use management of the big cities. The future research should invite

investigation into the reduction of UHI effects with measures reducing heat in cities.

In analogous studies like this one, many statistical methods, e.g. regression and

time series, are applied to remotely sense data to infer results and future researchers

should be aware of the growing potential of the application of spatial statistics. It is

suggested that the researchers who undertake work in this field to examine the

relationships between land use type and surface temperature at a particular point in time

should apply spatial statistics for accurate and reliable results. Spatial statistics is directly

related to two fundamental properties of remotely sensed data, spatial autocorrelation and

spatial heterogeneity. This is particularly true when remote sensing applications

concerned with spatial variability, the relationships between different attributes and

statistical methods draw a particular result based on geostatsitical theory.

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APPENDICES

Appendix 01: Mean Annual Recorded Temperature (°C) 1950-2015

Year Urban Station (PBO) Rural Station (APT)

MMxT MMiT MAT MMxT MMiT MAT

1950 30.33 16.53 23.43 - - -

1951 31.83 17.31 24.57 - - -

1952 32.47 17.25 24.86 - - -

1953 32.51 18.08 25.3 31.65 18.5 25.08

1954 31.73 17.55 24.64 30.92 17.68 24.3

1955 30.96 17.25 24.11 30.45 17.3 23.88

1956 30.98 17.58 24.28 30.63 17.6 24.12

1957 29.71 16.88 23.3 29.93 16.94 23.44

1958 31.33 18.03 24.68 31.08 17.75 24.42

1959 30.73 18 24.37 30.23 17.83 24.03

1960 31.64 17.02 24.33 31.38 16.86 24.12

1961 30.2 17.44 23.82 30.04 17.18 23.61

1962 30.46 17.34 23.9 30.23 17.3 23.77

1963 31.19 17.62 24.41 30.97 17.68 24.33

1964 30.35 17.08 23.72 30.18 17.11 23.65

1965 30.84 17.48 24.16 30.56 17.4 23.98

1966 30.98 17.36 24.17 30.72 17.19 23.96

1967 30.28 17.63 23.96 29.74 18.31 24.03

1968 30.31 17.33 23.82 30.24 17.13 23.69

1969 32.18 18.15 25.17 31.27 17.62 24.45

1970 31.34 18.2 24.77 31.3 17.5 24.4

1971 30.96 17.95 24.46 30.91 17.32 24.12

1972 30.78 17.83 24.31 30.71 17.26 23.99

1973 30.62 18.43 24.53 30.43 17.91 24.17

1974 31.15 17.65 24.4 30.56 16.96 23.76

1975 30.37 17.62 24 29.91 16.7 23.31

1976 30.27 18.18 24.23 29.72 17.13 23.43

1977 30.93 18.33 24.63 30.05 17.4 23.73

1978 30.93 18.24 24.59 30.07 17.07 23.57

1979 30.91 17.84 24.38 30.17 16.7 23.44

1980 31.15 17.73 24.44 30.62 17.6 24.11

1981 31.77 17.67 24.72 30.28 17.22 23.75

1982 30 17.17 23.59 29.41 16.79 23.1

1983 29.67 16.98 23.33 29.13 16.5 22.82

1984 31.05 17.99 24.52 30.46 16.69 23.58

1985 31.65 18.28 24.97 30.93 17.63 24.28

1986 30.7 17.67 24.19 29.83 16.73 23.28

1987 31.9 18.42 25.16 30.92 17.68 24.3

1988 31.9 18.86 25.38 30.83 18 24.42

233

1989 31.12 17.73 24.43 30.36 16.86 23.61

1990 30.58 18.44 24.51 30.19 17.87 24.03

1991 30.7 17.73 24.22 30.52 17.48 24

1992 30.69 18.53 24.62 30.39 17.67 24.03

1993 31.59 18.77 25.18 30.79 17.69 24.24

1994 30.91 18.88 24.9 30.51 18.83 24.67

1995 30.53 18.7 24.62 30.07 17.7 23.89

1996 30.44 18.53 24.49 30.09 17.21 23.65

1997 28.6 18.6 23.6 28.28 17.22 22.75

1998 30.63 19.29 24.96 30.42 17.57 24

1999 31.17 19.72 25.45 31.12 18.42 24.77

2000 30.9 19.43 25.17 31.01 18.16 24.59

2001 30.67 19.53 25.1 30.88 18.49 24.69

2002 31.12 20.08 25.6 31.56 18.32 24.94

2003 29.94 19.48 24.71 30.45 17.7 24.08

2004 30.83 20.29 25.56 31.53 17.88 24.71

2005 29.87 19.36 24.62 30.32 16.58 23.45

2006 30.55 20.25 25.4 30.87 17.49 24.18

2007 30.53 19.78 25.16 30.9 16.53 23.72

2008 30.21 19.78 25 30.4 16.74 23.57

2009 31.1 19.97 25.54 31.27 16.9 24.09

2010 30.84 20.1 25.47 31.56 18.64 25.1

2011 29.92 19.49 24.71 30.75 17.98 24.95

2012 30.34 18.44 24.39 30.77 17.28 24.03

2013 30.03 17.94 23.99 30.33 17.21 23.77

2014 29.85 17.91 23.88 29.9 17.06 23.48

2015 30.45 19.76 25.11 31.31 19.58 25.45

Source: Pakistan Metrological Department, Lahore, 2015

Appendix -2: Annual Trends of Ambient Air Quality of Lahore

Annual Average NO NO2 NOx CO SO2 O3 PM2.5

ug/m3 ug/m3 ug/m3 mg/m3 ug/m3 ug/m3 ug/m3

2008 19.92 35.59 55.51 1.23 52.91 47.04 123.28 2009 18.35 37.72 56.06 1.48 67.51 49.49 128.76

2010 20.52 39.25 59.77 2.33 69.25 59.28 135.88

NEQS

(Annual Average) 40 40 5 80 130 25

Source: EPA, Lahore

234

Appendix -3: Air Quality Parameters VS Population density and Temperature

Stop

# Site

Populati

on

Density

Temper

ature OC

Air Quality

CO

(ppm)

SO2

(ppm)

PMs

(μg/m3/hr

NO2

(ppm)

Reference Values (NEQS) 25.8 5 5 25

1 Chauburji 159 35.54 5 20 2.76 0.10

2 Chowk Yaadgar 307 35.94 8 16 2.36 0.08

3 Kalma Chowk 62 35.54 5 20 8.17 -

4 Lahore Hotel Chowk 277 36.33 7 17 7.6 0.17

5 Laskmi Chowk 277 35.94 7 12 1.11 0.2

6 Liberty Market 34 35.54 6 12 2.21 0.15

7 Mochi Gate 274 36.33 5 10 4.53 0.1

8 Muslim Town Chowk 197 35.44 7 15 1.43 0.15

9 Regal Chowk 112 35.94 9 18 1.385 0.2

10 Samanabad Morr 347 36.33 10 20 1.93 0.18

11 Scheme Morr 223 37.11 11 18 2.38 0.22

12 Shadman Chowk 43 35.54 7 18 1.04 0.15

13 Yateem Khana Chowk 335 36.33 9 15 3.61 0.17

Source: Faculty of Environment and Public Health, Institute of Public Health, Lahore;

Lahore Urban Transport Master Plan 2012, Volume II

Appendix -4: Urban Population of Lahore, Punjab and Pakistan

Year Pakistan (%) Punjab (%) Lahore (%)

1951 17.8 3.5 74

1961 22.5 5.4 79

1972 25.4 9.18 82

1981 28.3 13.5 82

1998 32.52 23.02 82.4

2010 36 45.98 82.8

2015 39.2 82.2

Source: GOP, 2015

235

Appendix 5: No. of Registered Vehicles of Lahore

Year No. of Vehicles Year No. of Vehicles

1973 64646 1993 299902

1974 - 1994 339973

1975 - 1995 480167

1976 49453 1996 567186

1977 - 1997 671821

1978 - 1998 638089

1979 85937 1999 702734

1980 - 2000 723381

1981 93725 2001 769644

1982 110998 2002 831033

1983 - 2003 932396

1984 - 2004 1086547

1985 146097 2005 1253101

1986 169785 2006 1464344

1987 - 2007 1703007

1988 - 2008 1944709

1989 - 2009 2129990

1990 249335 2010 2387993

1991 250753 2011 2586460

1992 250753 2012 2687987

Source: Excise and Taxation Department, Lahore Pakistan

Appendix 6: No. of Registered Factories of Lahore

Year No. of Factories Year No. of Factories

1979 937 1998 1536

1980 998 1999 1240

1981 1005 2000 -

1982 1001 2001 1399

1983 994 2002 -

1984 821 2003 -

1985 864 2004 1454

1986 924 2005 -

1987 1016 2006 -

1988 1024 2007 -

1989 1137 2008 1805

1990 1206 2009 1899

1991 1210 2010 1986

1992 1281 2011 -

1993 1310 2012 -

1994 - 2013 2055

1995 1392 2014 2150

1996 1427 2015 2233

1997 1426

Source: Punjab Development Statistics, 2016

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