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APPLICATION OF REMOTE SENSING AND GIS IN URBAN LAND SUITABILITY MODELING AT PARCEL LEVEL USING

MULTI-CRITERIA DECISION ANALYSIS

Thesis submitted to the Andhra University, Visakhapatnam in partial fulfillment of the requirements for the award of

Master of Technology in Remote Sensing & Geographic Information System

by

M Raghunath, Senior Grade Lecturer, SJ(Govt.) Polytechnic,

Bangalore

Internal Supervisor Mr. Sandeep Maithani Scientist-SE, HUSAG,

IIRS, DehraDun

External Supervisor Dr. H.Honnegowda, Director, KSRSAC,

Bangalore

iirs Indian Institute of Remote Sensing

National Remote Sensing Agency Dept. of Space, Govt. of India

Debra Dun-248 001 INDIA

2006

Application of RS & GIS for Urban Land Suitability Modeling at Parcel Level using MCDA 2

Chapter No. Description Page No.

Certificates i

Acknowledgement iii

List of Maps and Diagrams iv

List of Tables vii

1 Introduction 1-1

2 Cadastral Maps 2-1

3 Basic Concepts of Remote Sensing and GIS 3-1

4 Multi-Criteria Data Analysis 4-1

5 Urban Land Suitability Evaluation 5-1

6 Study Area – Bangalore 6-1

7 Methodology and Database Creation 7-1

8 Analysis and results 8-1

9 Summary and Conclusion 9-1

Appendix-A A-1

Bibliography B-1


Karnataka State Remote Sensing Application Center,

6th Floor, Multi-Storeyed Buildings, Vidhana Veedhi,

Bangalore – 560 001, Karnataka (State), INDIA

CERTIFICATECERTIFICATECERTIFICATECERTIFICATE

This is to certify that Sri. M. Raghunath, Senior Grade Lecturer in Civil

Engineering, S.J.(Govt.) Polytechnic, Bangalore, has carried out the project

entitled “ Application of Remote Sensing and GIS for Urban Land Suitability

Modeling at Parcel Level using Multi-Criteria Decision Analysis (MCDA) “ as a

partial fulfillment for the award of M.Tech(RS&GIS) degree in stream of ‘Urban

and Regional Planning’ by Andra University at the Karnataka State Remote

Sensing and Applications Center, Bangalore.

The report contains original work carried by the trainee and has used the

available data at this center.

(Dr. H . Honne Gowda)

External Guide &

Director, KSRSAC

Bangalore


Indian Institute of Indian Institute of Indian Institute of Indian Institute of Remote SensingRemote SensingRemote SensingRemote Sensing

(National Remote Sensing Agency)(National Remote Sensing Agency)(National Remote Sensing Agency)(National Remote Sensing Agency)

(Dept. of Space, Govt. of India)(Dept. of Space, Govt. of India)(Dept. of Space, Govt. of India)(Dept. of Space, Govt. of India)

Kalidas Road, PB No: 135, Kalidas Road, PB No: 135, Kalidas Road, PB No: 135, Kalidas Road, PB No: 135,

DEHARADUN DEHARADUN DEHARADUN DEHARADUN –––– 248 001 (INDIA) 248 001 (INDIA) 248 001 (INDIA) 248 001 (INDIA)

CERTIFICTECERTIFICTECERTIFICTECERTIFICTE

This is to certify that Sri. M. RaghunathM. RaghunathM. RaghunathM. Raghunath, Senior Grade Lecturer in Civil

Engineering, S.J.(Govt.) Polytechnic, Bangalore has carried out the project

entitled “ Application of Remote Sensing and GIS for Urban Land Suitability “ Application of Remote Sensing and GIS for Urban Land Suitability “ Application of Remote Sensing and GIS for Urban Land Suitability “ Application of Remote Sensing and GIS for Urban Land Suitability

Modeling at Parcel Level using MultiModeling at Parcel Level using MultiModeling at Parcel Level using MultiModeling at Parcel Level using Multi----Criteria Decision Analysis (MCDA) “Criteria Decision Analysis (MCDA) “Criteria Decision Analysis (MCDA) “Criteria Decision Analysis (MCDA) “ as a

partial fulfillment for the award of M.Tech(RS&GIS)M.Tech(RS&GIS)M.Tech(RS&GIS)M.Tech(RS&GIS) degree in stream of ‘Urban Urban Urban Urban

and Regional Planningand Regional Planningand Regional Planningand Regional Planning’’’’ of Andra UniversityAndra UniversityAndra UniversityAndra University at the Karnataka State Remote Karnataka State Remote Karnataka State Remote Karnataka State Remote

Sensing and Applications Center (KSRSAC)Sensing and Applications Center (KSRSAC)Sensing and Applications Center (KSRSAC)Sensing and Applications Center (KSRSAC), Bangalore.

The report contains original work carried by the trainee and has duly

acknowledged the data sources and the facilities used by him.

(Mr. Sandeep Maithani) (Mr. Sandeep Maithani) (Mr. Sandeep Maithani) (Mr. Sandeep Maithani) (Mr. B.S.Sokhi)(Mr. B.S.Sokhi)(Mr. B.S.Sokhi)(Mr. B.S.Sokhi)

Internal Project Guide Head, HUSAG, IIRS

Scientist, HUSAG, IIRS Dehardun

Dehradun

(Dr. V.K. Dadhwal)(Dr. V.K. Dadhwal)(Dr. V.K. Dadhwal)(Dr. V.K. Dadhwal)

Dean, IIRS

Dehradun


ACKNOWLEDGEMENT

It gives me immense pleasure to express my sincere appreciation of the

assistance rendered to me by all those who helped me in completing this

project. At the outset I express my deep sense of gratitude to the Prof.Prof.Prof.Prof.

BasavarajBasavarajBasavarajBasavaraj, Director, Department of Technical Education, Government of

Karnataka, Bangalore, for deputing me to undergo M.Tech(RS&GIS) Degree

program at Indian Institute of Remote Sensing, NRSA, Department of Space,

Dehradun. I express my heartfelt gratitude to Mr. P.S. RoyMr. P.S. RoyMr. P.S. RoyMr. P.S. Roy, the former Dean who

opened the gateway for M.Tech (RS&GIS) program and selected me to undergo

this course. Also I thank Dr.V.K. DadhwalDr.V.K. DadhwalDr.V.K. DadhwalDr.V.K. Dadhwal, the present Dean who permitted me

to do my project work at KSRSAC, Bangalore.

I sincerely thank Mr.Mr.Mr.Mr. GovilGovilGovilGovil, Head, Photogrammetry and remote Sensing

(PRS) Division and also the faculty members of PRS Division, who helped me to

understand the science and technology of RS&GIS.

Also, I sincerely express my gratitude to Mr. B.S.SokhiMr. B.S.SokhiMr. B.S.SokhiMr. B.S.Sokhi, Head, Human and

Urban Settlement Analysis Group (HUSAG), for his valuable guidance,

cooperation and suggestions. Nevertheless, I thank Mr. Sandeep MaithaniMr. Sandeep MaithaniMr. Sandeep MaithaniMr. Sandeep Maithani,

Scientist, who helped and spent long hours with me, day and night, during my

short stay at IIRS, Dehradun. Also I thank Miss. Sadhana Jain, Scientist, and Dr.

Bharath, Scientist, in HUSAG for their valuable guidance and cooperation during

the course of study.

I would like to express my deepest gratitude to Dr.H. HonnegowdaDr.H. HonnegowdaDr.H. HonnegowdaDr.H. Honnegowda,

Director, Karnataka State Remote Sensing Applications Center, Department of

IT&BT, Govt. of Karnataka, Bangalore for granting permission to carry out my

project work at his premises and extending his fullest support by providing

proper guidance, suggestions, Quick Bird Satellite data, the cadastral maps, the

hardware and software and above all available soil and ground water prospects

data for my study area. I do not find suitable words to express my thanks to the

staff of KSRSAC who shared their experiences and helped me to learn the

practical aspects of RS and GIS.

Last but not the least, I put on record the encouragement, cooperation

and selfless sacrifice, during my stay away from home, of my wife Y.S.Veena

and my little daughter R. Sanjana, which lead me to successfully complete this

project work.

Bangalore M. Raghunath


List of Maps and DiagramsList of Maps and DiagramsList of Maps and DiagramsList of Maps and Diagrams

Figure 1.1: Unstructured and structured problems

Figure 2.1: A typical Cadastral Map (Gunjur Village, Bangalore Urban South Tq.)

Figure 2.2: Legend for Cadastral Maps

Figure 3.1: Energy interactions in the atmosphere

Figure 3.2: Stages in Remote Sensing process

Figure 3.3: Passive sensors

Figure 3.4: Active Sensors

Figure 3.5: Ground, Air and Space Borne Remote Sensing

Figure 3.6: Satellite orbits

Figure 3.7: Various data input devices/methods

Figure 3.8: Components of GIS

Figure 3.9: GIS Thematic layers representing real world features

Figure 3.10: Representation of the real world and showing differences in how a

vector and a raster GIS will represent this real world.

Figure 3.11: Vector data format

Figure 3.12: Raster data format

Figure 4.1: Relationship among the elements of MCDA

Figure 4.2: Framework of Spatial Multi-criteria Decision Analysis

Figure 6.1: Location map of Bangalore City

Figure 6.2: Study area over topographic sheet mosaic of Bangalore

Figure 6.3: Location Of Study Area Over Master Plan

Figure 6.4 Location Of Study Area Over BDA Administrative area

Figure 6.5: Population distribution in BMP wards

Figure 7.1: Methodology of Creating Urban Suitability Map at Parcel Level

Figure 7.2: Subset Study Area of Quick Bird Merged Image

Figure 7.3: Existing Manually Digitized Landuse/landcover Map


Figure 7.4: Ground Water Prospects Map

Figure 7.5: Soil Depth Map

Figure 7.6: Soil Texture Map

Figure 7.7: Land Value(Government) Map

Figure 7.8: Proximity to Road Network Map

Figure 7.9: Proximity to Built-up Area Map

Figure 7.10: Master Plan Constraint Map

Figure 7.11: Built-up Area Constraint Map

Figure 7.12: Water Bodies Constraint Map

Figure 7.13: Overlay of Geo-referenced Cadastral Map of Gunjur Village over

Quick Bird Merged Image

Figure 7.14: Overlay of Geo-referenced Cadastral Vector Parcel Layer over

Quick Bird Image

Figure 7.15: Village wise Parcel Vector Layer

Figure 7.16: Overlay of Cadastral Vector Parcel Layer over Quick Bird Image

Figure 8.1: Graph showing the comparison of areas of Urban Land Suitability

classes of different models.

Figure 8.2: Graph showing the comparison of areas of different Urban land

suitability classes of all models for Gunjur village

Figure 8.3: Pie Chart Showing the Percentage of Areas of Different Urban Land

Suitability Classes of Parcel No 303 – Models 1&2


Suitability Classes of Parcel No 303 – Model -3


Suitability Classes of Parcel No 303 – Model-4

Figure 8.6: Urban land Suitability Map For Urban Suitability Model 1





Figure 8.10: Parcel Level Urban Land Suitability Map for Model – 1




Figure 8.14: Gunjur Village Parcel Level urban land suitability map: model-1




Figure 8.18: Gunjur Village Parcel Level urban land suitability map for Parcel

No: 303 : model-1


No: 303 : model-2


No: 303 : model-3


No: 303 : model-4


List of Tables:List of Tables:List of Tables:List of Tables:

Table 1.1: Percentage of urban population and its contribution to national

income

Table 1.2: Specifications of Quick Bird satellite

Table 1.3: SOI maps

Table 1.4: Villages and their extent in Hectares in the Study Area

Table 3.1: Satellite Imagery for Different Levels of Development Planning

Table 3.2: Operational Satellites

Table 4.1: Comparison of MODM and MADM

Table 5.1: List of physical parameters and their importance in land suitability

for urban development

Table 5.2: Sources of data

Table 6.1: Rainfall in Bangalore Urban District

Table 6.2: Population of Bangalore from 1950 to 2015

Table 6.3: Ward / CMC- wise Population as per census 2001

Table 7.1 : Areas of Landuse/Landcover Classes in the study area

Table 7.2 : Areas of Ground Water prospects Classes in the study area

Table 7.3 : Areas of Soil Depth Classes in the study area

Table 7.4 : Areas of Soil Texture Classes in the study area

Table 7.5 : Areas of Land Value Classes in the study area

Table 7.6 : Areas of Proximity to Road Classes in the study area

Table 7.7 : Areas of Proximity to Built-up Classes in the study area

Table 7.8 : Areas of Constraint (Master Plan) Classes in the study area

Table 7.9 : Areas of Constraint(Built-up) Classes in the study area

Table 7.10: Areas of Constraint(Water Body) Classes in the study area

Table 8.1 : Saaty’s scale of importance

Table 8.2: Importance matrix for Model-1





Table 8.6: Weightage derived from the Saaty’s AHP method:

Table 8.7: Ranking System For The Categories Of The Factors/Parameters

Table 8.8: Comparative Gross Areas of Suitability Classes of Different Models

Table 8.9: Village-wise Comparative Areas of Different Land Suitability

Classes and models

Table 8.10: Example showing Gunjur Village Multi-Suitability Class Affiliation of

Same Parcel To different classes : Model - 1

Table 8.11 :Example showing Gunjur Village Multi-Suitability Class Affiliation

of Same Parcel To different classes : Model – 2

Table 8.12:Example showing Gunjur Village Multi-Suitability Class Affiliation of

Same Parcel To different classes : Model – 3

Table 8.13:Example showing Gunjur Village Multi-Suitability Class Affiliation of

Same Parcel To different classes : Model – 4

Table 8. 14: Showing Comparative Areas in Hectares of all the four Models

pertaining to Parcel No. 303 of Gunjur Village

Appendix–A: Example showing Gunjur Village Parcel-wise Suitability Class

Assignment : Model - 4


CHAPTER 1: INTRODUCTIONCHAPTER 1: INTRODUCTIONCHAPTER 1: INTRODUCTIONCHAPTER 1: INTRODUCTION

The urban areas in developing countries have witnessed tremendous

changes in terms of population growth and urbanization. In the absence of

proper urban management practice, uncontrolled and rapid increase in

population pose enormous challenges to governments in providing adequate

shelter to the millions homeless and poor in urban areas. This has also posed

great concern among urban planners. Urban growth due immigration has led to

increase in population density. There is an increase in slum and squatter

settlements in cities and urban area. It has been estimated that more than 30%

of urban population lives in slums and squatters. This has led to shortage of

facilities and increasing demand of urban land for residential purposes. The

migration of rural people to urban areas hoping for better job opportunities,

better standard of living and higher level of education will not stop. One of the

reports says that India is one of the most rapidly urbanizing countries in the

world. Therefore, there is an urgent need to regulate the urbanization process

in a systematic and scientific way for future development.

YEAR %AGE URBAN POPULATION

TO TOTAL POPULATION

%AGE CONTRIBUTION TO

NATIONAL INCOME

1951 17.3 29

1981 23.3 47

1991 25.7 55

2001 28.5 61

Table 1.1 : Percentage of urban population and its contribution to national income

In general, the development planning system adopted in India is the

integrated planning approach consisting of a set of four inter-related planning

levels as follows.

- Regional Planning:

It is synonymous with macro level scale ranging from 1:250,000 to

1:1,000,000. It is very long-term plan covering a state or a cluster of

states. This is meant for spatial-economic development of the region.

- Perspective Planning:

It is also synonymous with macro level (District Level) at 1:50,000

to 1:250,000 scale. It is also a long-term planning exercise and


addresses the policy issues relating to the spatial-economic development

of the district or cluster of districts.

- Development Planning:

It is synonymous with the meson-level (Block Level) at 1:25,000 to

1: 50,000 scale. It is conceived within the framework of sanctioned

perspective plan.

- Project Planning:

It is synonymous with micro-level (village level) at a scale from

1:5,000 to 1:500. It is detailed annual work layout for the executing

agency. It is prepared within the framework of Development Plan

The Above development planning exercises have to be viewed in the

context of specific problems associated with developing countries which

emanates primarily due to rapid population growth and limited resource

availability resulting in regional and intra-district imbalances at the socio-

economic development levels. These problems find further manifestation in the

form of low income levels, smaller land holdings, poor social services, inferior

infrastructure, poor literacy and hygiene, environmental degradation etc. Also

in context of spatial planning support in India, following problems are

encountered

- non-availability of country wide latest topographic and cadastral

database

- non-availability of technological know-how at working level

- lack of institutional back-up for implementing and monitoring the

development plans

- Security restriction problems related to the use of topographic database

or high resolution spatial data.

Urban planning is a complex phenomenon that requires enormous data

to support the decision. It is a process of identifying problems and finding

solutions using an information system.

Urbanization is a dynamic phenomenon, which keeps on changing with

time. Therefore, accurate and timely data is required for proper urban planning.

Urban planners use variety of data and methods to solve the problems of urban

areas. With the launch of artificial satellites and availability of remotely sensed

data, which gives synoptic view of the planning areas, the urban planners are

equipped with new tool. Today very high resolution data such as 1m PAN from


IKONOS, 0.6m PAN from QUICKBIRD and 2.5m PAN from CARTOSAT-1 satellites

are available at reasonable cost. This spatial data combined with other data can

provide better ability to understand the urban problems clearly and arrive at

suitable solutions. Spatial Expert Support System (SESS) can be used to analyze

complex relationships that have been difficult to handle by traditional methods.

It is a tool that can make integrated analysis possible and allow planners to

design models for development and to determine the various solutions

available to government to deal with rapid growth of cities and deterioration of

the environment. The urban planners need authenticated and accurate data and

sophisticated computer tools for making dynamic decisions. Remote sensing

and GIS are such tools or aids, which help the planners to accurately create and

manage data. GIS is used as analysis tool as a means of specifying logical and

mathematical relationships among map layers to get new derivative map layers.

Any new data can be added to existing GIS database easily. Thus remote

sensing data provides reliable, timely, accurate and periodic spatial data while

GIS provides various integrating tools for handling spatial and non-spatial data

to arrive at solution for decision making.

Successful implementation of Decision Support System (DSS) depends on

designing or choosing a right system that reflects the degree of problem

structure. Structured versus unstructured decision problems is the core of

concept of designing the DSS. The decision problems can be

- Structured or well defined either by decision-maker or on the basis of

appropriate theory,

- Unstructured or improperly or ill defined without any basis of

appropriate theory. Structured decisions can be programmed whereas

unstructured decisions can not be programmed. However, most real-life

problems lie in between two extremities called as semi-structured

decision problems. This is the area where DSS can play a major role.

Land or site suitability analysis for urban development falls within the

semi-structured decision problem category. Multi-Criteria Spatial Decision

Support System (MC-SDSS) and Spatial Expert Support System (SESS) both can

be utilized in the decision-making process. The key difference is that the

objective SDSS is to support decision making rather than to replace decision

maker while SESS focuses on providing a recommendation to the user based on

expert knowledge or replace a decision maker.


Figure 1.1: Unstructured and structured problems

• Aims and Objectives:Aims and Objectives:Aims and Objectives:Aims and Objectives:

The objective of the present study is to use Remote Sensing and GIS

techniques for Urban Land Suitability Modeling at parcel level using Multi-

Criteria Decision Analysis.

This study involves

- mapping of study area at parcel level using cadastral maps

- site suitability analysis using various parameters for urban

development

• Data Used:Data Used:Data Used:Data Used:

- Remote sensing satellite data:

Quick-Bird satellite data has been used with the following specifications as

given in the Table 1.2

Date of Acquisition 24th Feb 2004 Acquisition

Client KSRSAC, Bangalore

Date of launch 18th Oct’ 2001 Launch

Information Launch site SLC_2W,Vandenberg Airforce Base,

California, USA

Altitude 450 Km, Sun-synchronous

inclination Orbit

Revisit frequency 1 to 3.5 days depending on

latitude at 70 Cm resolution

Per-orbit

collection 128 GB ( 57 single area images )

DEGREE OF PROBLEM STRUCTURE

DECISION

COMPUTER

COMPUTER& DECISION


Nominal swath width 16.5 Km at nadir

Accessible ground

swath

544 Km centered on the satellite

ground track ( to ~ 30o off nadir)

Single area – 16.5 Km X 16.5 Km

Swath width &

area size

Areas of interest Strip - 16.5 Km X 165 Km

Circular error 23 m Metric

Accuracy Linear error 17 m

PAN 61 cm GSD at nadir Spatial

Resolution MSS 2.44 m GSD at nadir

PAN 445 – 900 nm

Blue : 450 – 520 nm

Green : 520 – 600 nm

Red : 630 – 690 nm

Spectral

Resolution MSS

NIR : 760 – 900 nm

Table 1.2: Specifications of Quick Bird satellite

- Cadastral maps:Cadastral maps:Cadastral maps:Cadastral maps:

The Cadastral maps or land records have been evolved on a varying scale

from 1:3500 to 1:8000 for the purpose of revenue collection by British Empire

in India. These maps have become obsolete due to irregular statutory surveys.

Therefore, the cadastral maps sometimes do not represent the true picture of

ground reality with respect to ownership and possession. The accuracy of

cadastral maps is less due to use of conventional chain and compass surveying

at that time. The conventional cadastral system is a multi-purpose system

catering to the needs of legal, fiscal, planning and other administrative

requirements. A cadastral map essentially comprises of land records of each

village, which were created by an aggregation of the graphical sketches of

individual land holdings and descriptive details of land parcels such as title and

extent. A village is the smallest administrative unit having well-defined

geographical boundary and separate land records.


- Topographical maps:Topographical maps:Topographical maps:Topographical maps:

Survey of India (SOI) topographic maps on scale of 1:50000 have been

used for identification of villages and guide map on 1:20000 for identifying

road network.

SOI MapSOI MapSOI MapSOI Map ScaleScaleScaleScale Year of SurveyYear of SurveyYear of SurveyYear of Survey Year of publicationYear of publicationYear of publicationYear of publication

57H9, 57H13 1:50000 1971-72 1973

Guide map 1:20000 1997-99 2002

Table 1.3: SOI maps

- Software used:Software used:Software used:Software used:

Erdas Imagine 8.5 version

Arc/Info 8.3

Arc Map

Arc View 3.2a

Microsoft Office 2000

• Scope and Limitations:Scope and Limitations:Scope and Limitations:Scope and Limitations:

1. Bangalore urban district consists of totally 865 villages and practically it

is impossible to cover all villages for the project work due to limited

project time and constraints on finance and human resources.

2. Therefore, the following 29 villages are considered for study area as in

table 1.4

3. The detailed analysis is done for only one village i.e. Gunjur


Sl no Names Area

(hect)

Population

(census 2001)

1 Amblipura 33.80 234

2 Baligeri 182.95 1604

3 Bellandur 82.06 3525

4 Bellanduru amanikere 489.69 0

5 Boganalli 249.81 571

6 Chikkabellandur 176.17 493

7 Chinnappanalli 112.84 198

8 Devarbisanahalli 149.96 1700

9 Doddakannalli 338.78 2873

10 Gunjur 794.20 4090

11 Hagaduru 292.34 3689

12 Kachamaranalli 259.89 716

13 Kadabisanalli 74.54 813

14 Kaikondanahalli 68.74 1301

15 Khane khandya 31.84 0

16 Kodathi 19.77 1353

17 Kundalalli 246.71 1643

18 Mullur 208.77 1253

19 Munnekollala 320.05 5321

20 Nalluralli 194.91 999

21 Panatur 306.32 2553

22 Ramagondanalli 134.59 3548

23 R-narayanapura 34.80 1524

24 Siddapura 138.28 1902

25 Sorhunse 226.51 3659

26 Sulakunte 146.82 1605

27 Tubaralli 161.40 1958

28 Varthur 498.10 8111

29 White field 193.93 3578

Total 6168.55 60814

Table 1.4: Villages and their extent in Hectares and population in the study area


Chapter 2 : Cadastral MappingChapter 2 : Cadastral MappingChapter 2 : Cadastral MappingChapter 2 : Cadastral Mapping

• Introduction:Introduction:Introduction:Introduction:

The Cadastre is one of the country's basic registers. It shows individual

cadastral units of land-ownership and parcels and areas separated from them

that originally compiled for purposes of taxation. For many decades, traditional

cadastral systems have tended to enjoy a reputation for reliability, well defined

processes, and a well recognized guarantee of security of private land

ownership. Tremendous technological progress, social change, globalization,

and the increasing interconnection of business relations with their legal and

environmental consequences, however, have put a strain on the traditional

systems. They cannot adapt to all the new developments. An obvious indication

of this is the many reforms that cadastral systems are going through.

Cadastre is a methodically arranged public inventory of data concerning

properties within a certain country or district, based on a survey of their

boundaries. Such properties are systematically identified by means of some

separate designation. The outlines of the property and the parcel identifier

normally are shown on large-scale maps which, together with registers, may

show for each separate property the nature, size, value and legal rights

associated with the parcel. Land registration and cadastre usually complement

each other, they operate as interactive systems. Land registration puts in

principle the accent on the relation subject-right, whereas cadastre puts the

accent on the relation right-object. According to United Nations report

(ECOSOC E/1322 of 1949), Cadastral surveys, unlike scientific surveys of an

informative character which may be amended with changing conditions or

because they are not executed according to the standards now required for

accuracy, cannot be ignored, repudiated, altered or corrected, and the

boundaries created or re-established cannot be changed so long as they

control rights vested in the lands affected.

In developing countries cadastral surveying and cadastral mapping are

often criticized for being slow and expensive, and one of the major limitations

on economic development. Yet most authorities would agree that some form of

cadastral mapping in developing countries is essential for economic

development and environmental management.


• History Of Cadastral Maps:History Of Cadastral Maps:History Of Cadastral Maps:History Of Cadastral Maps:

The raw cadastral map (Figure 2.2) or land records (on the scale varying

between 1: 3500 & 1: 8000 scale) in India have evolved almost a century ago,

but they have remained practically unchanged till date. The statutory periodic

resurveys for creation of up-to-date land records were also not conducted in

the last 7 to 8 decades. Consequently, the cadastral maps are out of tune with

today's developmental imperatives and are unable to serve the contemporary

requirements.

The present cadastral system was evolved by the British for the purpose

of revenue collection and management. These cadastral maps were required to

be updated every 30 years. However, most of the states have not carried out

any survey and settlement operations at frequent intervals. Hence, the cadastral

maps are by and large out-dated and do not reflect the ground realities with

regard to ownership and possession. The accuracy of the original cadastral

surveys, which were carried out based on the technology and standards relevant

at that time, are wholly inadequate now due to rapid technological

advancements and achievements.

The conventional system in Karnataka as well as in India is a

multipurpose cadastral system catering to legal, fiscal and other administrative

requirements like planning and monitoring development programmes. This

essentially comprises of land records of each village, which are created by an

aggregation of the graphical sketches of individual landholdings and the

descriptive details of the land parcels such as title and extent. Village is the

smallest statutorily recognized administrative unit, having well-defined

geographical boundary and separate land records. A typical cadastral map

(1:7920 scale) representing a village in Karnataka is given in Figure 2.2. and the

legend of topographical features represented in these cadastral maps is given

in Figure 2.3. These records satisfied the minimum requirements such as

showing the reputed ownership of the landholder, the extent of land owned by

him, and in some case the landuse, land revenue etc. In some parts of the

country, the conventional land records comprised of additional details such as

irrigation facilities, agricultural/crop statistics, location-based remission of

taxation and livestock census. There are no common standard scales being

followed. The scale of cadastral maps varies from 1:3500 to 1:8000. Also,

these maps have not been updated over the years.


The digital cadastral map is the fundamental component of any cadastral

system. Its major advantage is that it displays the spatial relationships between

features depicted on it that contain two basic types of map information, Spatial

information, which describes the location and shape of geographic features and

their spatial relationships to other features and descriptive information about

the features.

The typical digital cadastral maps can speed up the processes of field

survey, the storage, retrieval and analysis of data, and the preparation and

production of cadastral maps and plans. This has two advantages - it reduces

the human mistakes that occur in writing down and subsequently transcribing

field survey observations, and it facilitates the transfer of data for subsequent

computation and adjustment. While new surveys may benefit from the

availability of computer systems, many maps already exist only on paper, for

example in written records or on paper maps. Old maps must be converted into

computer-readable form if the advantages of modern information technology

are to be realized. The conversion of these existing maps and graphic images

into digital form is usually done by “digitizing”. The technology for digitizing

maps is readily available, though the processes are often labour intensive and

remain expensive. The priority in many cadastral systems is to manage textual

records more efficiently rather than to produce digital cadastral maps. Text

data may include the property reference number, the name and address of the

proprietor, the title number and form of tenure, details of any mortgages,

subleases or assignments, any caveats, and possibly details of annual rents and

rental payments and their due dates. In addition there may be references to

survey plans, land-use zones, planning applications, etc.

• Advantages Of Cadastral Maps:Advantages Of Cadastral Maps:Advantages Of Cadastral Maps:Advantages Of Cadastral Maps:

The main advantages of computerization of cadastral maps are as

follows.

- Speed up the collection and processing of cadastral survey data;

- Make significant reductions in the cost and space required for storing and

retrieving land records;

- Prevent unnecessary duplication of records;

- Simplify the preparation of “back-up” copies of registers in case of

disaster;

- Accelerate the processing of data for the first registration of title;


- Reduce the time and cost involved in transferring property rights and in

processing mortgages;

- Facilitate the monitoring and analysis of land and property;

- Provide better estimates of the value of land for taxation or compulsory

acquisition;

- Improve efficiency and effectiveness in collecting land and property

taxes;

- Assist the compilation of information and reports that were impossible or

very cumbersome to produce using manual systems;

- Provide mechanisms for quality control;

- Integrate the records of land ownership, land use and land value with

socio-economic and environmental data in support of physical planning;

- Assist in the allocation and monitoring permits to build on land;

- Manage property assets and ensure their efficient use and maintenance;

- Document and monitor archaeological sites and other areas of scientific

or cultural interest;

- Record tree preservation orders and conservation areas;

- Support the management of utilities such as water, sewerage, gas,

electricity, street lights, and telephones;

The scale of cadastral maps is of great importance. Since the object of

the map is to provide a precise description and identification of the land, the

scale must be large enough for every separate plot of land which may be the

subject of separate possession (conveniently called a “survey plot” or “land

parcel”) to appear as a recognizable unit on the map. When map data are stored

in a computer, they may be drawn at almost any scale and this can give an

impression of greater accuracy than the quality of the survey data may warrant.

Large-scale plans are initially much more expensive to make per unit area than

small-scale maps, but it must always be remembered that, once the large-

scale survey has been completed; accurate maps on any smaller scale can be

derived from them. The converse is not however true for although larger-scale

maps can easily be constructed by using computers.


Figur

e 2.1: A typ

ical C

adas

tral M

ap (Gun

jur Villag

e, B

anga

lore

Urb

an S

outh

Tq.)


Figure 2.2: Legend for Cadastral Maps


• Role Role Role Role Of RS And GIS In Cadastral Mapping:Of RS And GIS In Cadastral Mapping:Of RS And GIS In Cadastral Mapping:Of RS And GIS In Cadastral Mapping:

By far, Aerial photography played an important role in cadastral mapping.

Aerial photos will be accurate and provide quicker results compared to manual

surveying. But, the preparation of base maps from aerial photos involves high-

cost and sophisticated photogrammetric equipments. On the other hand,

satellite remote sensing is the quickest and cheapest available method for

mapping. The availability of high-resolution satellite imagery of less than 1m

resolution provides the best accuracy that is needed for cadastral level mapping

at 1: 4000 or even larger, for the purpose of developmental activities. With the

launching of high-resolution satellites, the Remote Sensing technology has

opened a new era in cadastral updating. Quickbird, a satellite launched by US

based corporate Digital Globe Inc., is providing images of resolution of 61

centimeter, and as on today, it is the only satellite providing highest spatial

resolution in the civilian market. This will facilitate the geo-referencing of the

cadastral maps as well as mapping of any ground feature with measurements of

one meter by one meter. The landuse/landcover, hydro geomorphology, soil,

drainage and transport information can be extracted from the PAN sharpened

multi-spectral imageries of Quickbird. Hence, remote sensing facilitates the

updating of the cadastral maps after bringing into digital domain along with

information on natural resources. In the proposed project, it is intended to use

Quickbird satellite data.

The process of the digital cadastral maps can be undertaken using what

have become known as Geographic Information Systems or GIS. This technology

has dictated and influenced many changes in the development of land

administration and cadastral systems, with more specialized spatial

information. The GIS technology for data management, manipulation, analysis

and integration arguably has had the greatest impact on the spatial information

environment, although in the future the communication technologies are

rapidly becoming the focus of attention. These technologies are expected to be

the norm for viewing, locating and using land related information in the years

ahead. It is accepted that when cadastral information is part of integrated

information systems, it can improve the efficiency of the land transfer process

as well the overall land management process.


A GIS consists of a data base, graphic facilities and software for data

processing. Using a GIS, different data can be retrieved from the database, or

data can be taken from two or more data sets and overlaid on the graphic

screen or printed out on hard copy such as paper.

• Cadastral Issues and Limitations:Cadastral Issues and Limitations:Cadastral Issues and Limitations:Cadastral Issues and Limitations:

- National level policies for appropriate land recordsNational level policies for appropriate land recordsNational level policies for appropriate land recordsNational level policies for appropriate land records:

The unsystematic land survey and land records are the major issues for

proper management of land. The administrators, planners and decision makers

feel that one of the major factors for delay in execution of land related projects

is the lack of information. Availability of modern methods of surveying and

mapping required to be adopted for generating a uniform system of maps and

to be associated with the land records available in the country.

---- Coordinated efforts:Coordinated efforts:Coordinated efforts:Coordinated efforts:

There is need to identify and adopt appropriate technology for collecting

cadastral data. Computerization of land records and their updating in a

consistent format is also needed to make macro planning. Establishment of a

system to develop HRD strategy and institutional arrangement in support of

national LIS are required to be framed. In addition to this, there is need to

create standards at national level for cadastral surveys, equipment, methods,

data measurement, data structure, scale, accuracy, symbology and data

exchange format.

---- Cadastral maps on national datum :Cadastral maps on national datum :Cadastral maps on national datum :Cadastral maps on national datum :

Cadastral map data base to be integrated with national datum so that the

Individual land parcel and the rights of the land holders in the parcel get prime

focus in all developmental activities launched by the government.

The existing cadastral systems have the following limitations:

---- Mapping standards:Mapping standards:Mapping standards:Mapping standards:

In India the mapping standards are set by the Survey of India (SOI) and

ideally all the mapping tasks at local level – cadastral level have to follow these

standards. But the existing maps do not conform to the general mapping

standards practiced by SOI.

---- Dimensional accuracy: Dimensional accuracy: Dimensional accuracy: Dimensional accuracy:

Any standard mapping exercise takes care of the dimensional accuracy by

following appropriate co-ordinate systems and projection parameters. In the

present case, these features are not according to the prescribed standards that


have been followed at the national level by SOI. Hence, the dimensional

accuracy varies largely.

---- Geo Geo Geo Geo----referencing:referencing:referencing:referencing:

All the cadastral maps of the State have been created without any geo-

referencing in terms of latitude and longitude. This makes it extremely difficult

to position or reference cadastral parcels and villages in a spatial environment.

---- Mosaicing and edge match of adjacent maps: Mosaicing and edge match of adjacent maps: Mosaicing and edge match of adjacent maps: Mosaicing and edge match of adjacent maps:

Due to the inherent reasons resulting due to non-compliance with the

survey of India standards, the adjacent maps of even a small region do not form

a seamless mosaic. The cadastral maps highlight that after clipping of

concerned village maps, there are overlaps and gaps along the borders of the

villages as the number of villages increased for mosaicking and there are no

continuation of permanent features like hills and streams in its adjacent

villages.

---- Topographic features on maps: Topographic features on maps: Topographic features on maps: Topographic features on maps:

The information captured in the cadastral map is very limited – more

emphasis has been on mapping only the land parcel boundaries. Survey number

information is the only attribute information available for a land parcel.

---- Bench Marks:Bench Marks:Bench Marks:Bench Marks:

Permanent immovable benchmarks placed by SOI are to be used as

standard reference marks for surveying. The cadastral maps have been created

using benchmarks that have no protection and are prone to human

interference. This has resulted in inaccuracy while resurveying.

- Updating new partitions:Updating new partitions:Updating new partitions:Updating new partitions:

The current practice of updating new partitions in land parcels is not

Oriented towards updating the original village map, instead an addendum

called Tippany is created – which holds a rough sketch of the area with

dimensions written across its boundaries along with the schedule of the

property.

Though there is a considerable effort involved in collecting the new

partition information, the very purpose of updating land information is not

completely realized. This is primarily due to the incompatibility between the

geo-referencing environments present in the map and the data recorded during

the partition survey.


Chapter 3: Basic Concepts of Remote Sensing and GISChapter 3: Basic Concepts of Remote Sensing and GISChapter 3: Basic Concepts of Remote Sensing and GISChapter 3: Basic Concepts of Remote Sensing and GIS

• Remote Sensing:Remote Sensing:Remote Sensing:Remote Sensing:

Remote sensing, in the simplest words, means acquiring information

about an object without touching the object itself. Conveniently, however,

remote sensing has become to imply that the sensor and target are located

remotely apart and the electromagnetic radiation serves as a link between

sensor and the object, the sun being the major source of energy illuminating

the earth. The pat of this energy is reflected, absorbed and transmitted by the

surface. A sensor records the reflected energy.

Remote sensing can then be defined as “The technique about an object

by a recording device or sensor that is not in physical contact with the object by

measuring portion of reflected or emitted electromagnetic radiation from the

earth’s surface.”

Figure 3.1: Energy interactions in the atmosphere

• Fundamental Principle Of Remote Sensing:Fundamental Principle Of Remote Sensing:Fundamental Principle Of Remote Sensing:Fundamental Principle Of Remote Sensing:

The basic principle involved is that the different objects based on their

structural, chemical and physical properties return (reflect or emit) different

amount of energy in different wavelength ranges (commonly referred to as

BANDS) of the electromagnetic spectrum incident upon it. Most remote sensing

programmes utilize the sun’s energy, which is a predominant source of energy.

These radiations travel through the atmosphere and are selectively scattered


and/or absorbed depending upon the composition of the atmosphere and the

wavelength involved. These radiations upon reaching the earth’s surface

interact with the target objects (earth surface features). Everything in nature has

its own unique pattern of reflected, emitted and absorbed radiation. A sensor is

used to record reflected or emitted energy from the surface. This recorded

energy is then transmitted to the users and it is processed to form an image,

which is then analyzed to extract information about the target. Finally the

information extracted is applied to assist in decision making for solving a

particular problem. Thus we can summarize the remote sensing process in the

following seven steps, which are depicted in Figure 3.2.

Figure 3.2: Stages in Remote Sensing process

• Stages in Remote Sensing Process:Stages in Remote Sensing Process:Stages in Remote Sensing Process:Stages in Remote Sensing Process:

---- Energy source Energy source Energy source Energy source: The first requirement for remote sensing is to have an

energy source, which illuminates the target of interest

---- Energy interactions with the atmosphere Energy interactions with the atmosphere Energy interactions with the atmosphere Energy interactions with the atmosphere: The energy on its way from

source to target and then, from target to the sensor comes in contact and

interacts with the atmosphere.

---- Interactions of energy with earth’s surface fe Interactions of energy with earth’s surface fe Interactions of energy with earth’s surface fe Interactions of energy with earth’s surface featuresaturesaturesatures: Different earth’s

surface features react differently to the incident energy. Portions of incident

energy are reflected, transmitted or absorbed by the surface.

---- Recording of energy by the sensor: Recording of energy by the sensor: Recording of energy by the sensor: Recording of energy by the sensor: The energy after interacting with

earth’s surface reaches the sensor (which is remote – not in touch with the


earth surface features) where it is recorded in a form, which can be

transmitted to and used by the users.

---- Data transmission and processing Data transmission and processing Data transmission and processing Data transmission and processing: The energy recorded by the sensor

is transmitted to a receiving and a processing station where the data are

processed into an image.

---- Image processing and analysis: Image processing and analysis: Image processing and analysis: Image processing and analysis: The processed image is interpreted to

extract the information about the earth’s surface features.

---- Application: Application: Application: Application: The extracted information is then utilized to make

decisions for solving particular problems.

Thus remote sensing is a multidisciplinary science, which includes a

combination of various disciplines such as optics, photography, computer,

electronics and telecommunication, satellite launching etc.

• Passive And Active Remote Sensing:Passive And Active Remote Sensing:Passive And Active Remote Sensing:Passive And Active Remote Sensing:

The sun provides a very convenient source of energy for remote sensing.

The sun’s energy is either reflected, as it is for visible wavelength or absorbed

and then re-emitted (for thermal infrared wavelength).

Figure 3.3: Passive sensors

Remote sensing systems, which measure this naturally energy, are called

as passive sensors. This can only take place when the sun is illuminating the

earth. There is no reflected energy available from the sun at night. Energy that

is naturally emitted can be detected day or night provided that the amount of

energy is large enough to be recorded.


Figure 3.4: Active Sensors

Remote sensing systems, which provide their own source of energy for

illumination, are known as active sensors. These sensors have the advantage of

obtaining data at any time of day or season.

Solar energy and radiant heat are examples of passive energy sources,

synthetic aperture Radar (SAR) is an example of active sensor.

In order to collect and record energy reflected or emitted from a target or

source, we require a sensing device (commonly called as sensor) residing on a

stable platform.

• Platforms:Platforms:Platforms:Platforms:

Platform is a stage to mount the camera or sensor to collect information

remotely about an object or surface. Platforms for remote sensors may be

situated on the ground, on an aircraft or balloon or on a spacecraft or satellite

outside of the earth’s atmosphere (Figure 3.5)

Ground-based sensors are often used to record detailed information

about the surface. In these systems sensors may be placed on a ladder, tall

building, crane etc.

Aerial platforms are aircrafts, which are primarily used to acquire aerial

photographs. Airplanes are used to collect very detailed images over any

portion of earth at any time. Balloons were also used to acquire aerial

photographs. Airplanes are also used to test the sensors before they can be put

onboard satellites.


The platforms in space (satellites) are not affected by earth’s atmosphere.

Satellites are objects; the moon is a natural satellite, whereas manmade

satellites include those platforms launched for remote sensing, communication

and telemetry (location and navigation) purposes. These satellites freely move

in their orbits around the earth and any part of the earth can be covered at

specified time intervals. It is these satellites that we get enormous amount of

remotely sensed data about earth’s surface.

Figure 3.5: Ground, Air and Space Borne Remote Sensing

• Satellite Orbits:Satellite Orbits:Satellite Orbits:Satellite Orbits:

The path followed by a satellite is referred to as its orbit. Satellites can be

categorized as Geostationary or near polar based on their orbits and altitude

(height above earth’s surface) as shown in Figure 3.6.

Figure 3.6: Satellite orbits


---- Geostationary Satellites: Geostationary Satellites: Geostationary Satellites: Geostationary Satellites:

The geostationary satellites are located at very high altitudes of

approximately 36,000 KM above the earth. They revolve at speed which

matches the rotation of the earth (24 hours) so they seem stationary, relative to

the earth’s surface and hence view one-third of globe. These satellites are used

for weather monitoring and communication.

---- Polar or Sun Polar or Sun Polar or Sun Polar or Sun----Synchronous Satellites:Synchronous Satellites:Synchronous Satellites:Synchronous Satellites:

Remote sensing satellites are designed to follow an inclined north-south

orbit. A satellites in this orbit has an inclination that carries the satellite track

westward at a rate such that it covers each area of the world at constant time of

the day called as local sun time as the satellite moves from north to south. This

ensures similar illumination conditions when acquiring images over a particular

area over a series of days.

These satellites travel from north to south o the sunlit side of the earth.

This is the descending pass of the satellite, while in the ascending pass the

satellite travels from south to north and it is the shadowed side of the earth.

• ResolutResolutResolutResolution of Satellite Images: ion of Satellite Images: ion of Satellite Images: ion of Satellite Images:

In general resolution is defined as the ability of an entire remote-sensing

system, including lens antennae, display, exposure, processing, and other

factors, to render a sharply defined image. Resolution of a remote-sensing is of

different types.

- Spectral ResolutionSpectral ResolutionSpectral ResolutionSpectral Resolution: of a remote sensing instrument (sensor) is

determined by the band-widths of the Electro-magnetic radiation of the

channels used. High spectral resolution, thus, is achieved by narrow

bandwidths width, collectively, are likely to provide a more accurate spectral

signature for discrete objects than broad bandwidth.

- Radiometric Resolution:Radiometric Resolution:Radiometric Resolution:Radiometric Resolution: is determined by the number of discrete levels

into which signals may be divided.

---- Spatial Resolution: Spatial Resolution: Spatial Resolution: Spatial Resolution: in terms of the geometric properties of the imaging

system, is usually described as the instantaneous field of view (IFOV). The IFOV

is defined as the maximum angle of view in which a sensor can effectively

detect electro-magnetic energy. Table 3.1 shows the recommended spatial

resolutions for various planning levels and their applications.


----Temporal ResolutionTemporal ResolutionTemporal ResolutionTemporal Resolution: is related to the repetitive coverage of the ground

by the remote-sensing system. The temporal resolution of Landsat 4/5 is

sixteen days.

Low ResolutionLow ResolutionLow ResolutionLow Resolution Medium Medium Medium Medium ResolutionResolutionResolutionResolution High ResolutionHigh ResolutionHigh ResolutionHigh Resolution

80 – 360 m 20-40 m 1-5 m

Level of PlanningLevel of PlanningLevel of PlanningLevel of Planning Macro Level (Regional

& Perspective)

Meso Level ( District/

Development)

Micro Level ( Project,

Micro-watershed, Village)

Scale MappingScale MappingScale MappingScale Mapping 1: 50000 to

1:1000000

1:25000 to 1: 50000 1:1000 to 1:5000

Application AreaApplication AreaApplication AreaApplication Area Demonstrated Applications Prospects

Crop acreage and Crop acreage and Crop acreage and Crop acreage and

Production ForecastProduction ForecastProduction ForecastProduction Forecast

Mono-crop areas - large

extents

Multi-crop areas -

medium extents

Mix-crop areas

-Cropping System Studies

-Parcel size for crops

grown

- input to precision

farming

Landuse PlanningLanduse PlanningLanduse PlanningLanduse Planning -Land use mapping at

Level-1 classification

-Wasteland mapping at

level-1

-Wetland mapping at

level-1

-Mapping at Level

2/3 classification

(Taluk/mandal level)

-Land use change

analysis

-Wasteland mapping

at level-2/3

-Wetland mapping

at level-2/3

- Cadastre/ field level

mapping - classification

level – 3 & 4

(Village/mandal)

- Inputs for tourism

development

Rural Development Rural Development Rural Development Rural Development

PlanningPlanningPlanningPlanning

-Regional maps

-Settlement network

-Land and water

resources

-development maps

-Cadastral level landuse

maps

-Land parcel maps

-Micro level watershed/

village planning

Urban PlanningUrban PlanningUrban PlanningUrban Planning -Urban Sprawl analysis

-Urban land use at

level1

-Transportation network

(Highways, Railways

etc.)

-Urban landuse

mapping (level-1)

-Urban suitability

analysis

-Mapping of major

transport network

Updating of city

guide maps

-Urban landuse mapping

(level 1 & 2)

-Slum typology

-Mapping of street level

-Urban road network

-Mapping of property

parcels Inputs for

infrastructure development


SoilsSoilsSoilsSoils -Soil family Association

mapping

-Land degradation

(Water logged, salt

affected, erosion prone)

-Soil series

association

-Land degradation

at level 2

-Soil series

-Land degradation at

micro level

Water ResourcesWater ResourcesWater ResourcesWater Resources -Watershed

characterization &

prioritization

-Glacier Inventory

-Groundwater

prospects

-Watershed

Prioritization

-Snow melt run-off

estimation

-Micro watershed planning

-Monitoring of

development schemes

-Drinking water site

selection

ForestForestForestForest -Forest type & density

Mapping

-Forest type &

density Mapping

-Detection of

degraded forest

areas

-Forest fire

monitoring

-Forest Species

identification

-Inputs for working plan

generation

-Habitat mapping

Biomass Estimation

Geology & MineralsGeology & MineralsGeology & MineralsGeology & Minerals Regional Geological

maps

Detailed geological

mapping

Oil, Gas and Mineral

Exploration

InfrastructureInfrastructureInfrastructureInfrastructure

PlanningPlanningPlanningPlanning

-Regional level corridor

planning

-Broad Site

Suitability analysis

-Mapping of major

road network

-Specific Project Site

Analysis

-Dams ,Highways ,Canal

Industries, Power Plants

DisasterDisasterDisasterDisaster -Flood Prone Area Maps

-Cyclone Monitoring

-Drought Monitoring &

Forecast

-Earthquake prone

areas

-Landslide prone area

mapping

-Slope stability mapping

-Post Disaster

Damage assessment

-Property Insurance

for Natural Disasters

-Post Disaster Relief

Management Support

-Tracing of approach

routes

-Waste disposal and solid

waste management

Meteorology & Meteorology & Meteorology & Meteorology &

OceanographyOceanographyOceanographyOceanography

-Monsoon Forecast

-Sea-surface temp

-Wind vectors

-Waves spectra

-Sea surface topography

- -

Table Table Table Table –––– 3.1: Satelli 3.1: Satelli 3.1: Satelli 3.1: Satellite Imagery for Different Levels of Development Planningte Imagery for Different Levels of Development Planningte Imagery for Different Levels of Development Planningte Imagery for Different Levels of Development Planning


Resolution(m)/Swath Width(km)Resolution(m)/Swath Width(km)Resolution(m)/Swath Width(km)Resolution(m)/Swath Width(km) Operational Operational Operational Operational

Systems Systems Systems Systems

SatelliteSatelliteSatelliteSatellite

Data Data Data Data

ProviderProviderProviderProvider PrimePrimePrimePrime LaunchLaunchLaunchLaunch

PANPANPANPAN MSSMSSMSSMSS RadarRadarRadarRadar

Repeat Repeat Repeat Repeat

Cycle Cycle Cycle Cycle

(days)(days)(days)(days)

LandsatLandsatLandsatLandsat----5555

ERSERSERSERS----2222

Radarsat1Radarsat1Radarsat1Radarsat1

IRSIRSIRSIRS----1C1C1C1C

Orbview2Orbview2Orbview2Orbview2

IRSIRSIRSIRS----1D1D1D1D

SpotSpotSpotSpot----4444

LLLLandsatandsatandsatandsat----7777

IRSIRSIRSIRS----P4P4P4P4

IkonosIkonosIkonosIkonos

Quick BirdQuick BirdQuick BirdQuick Bird

Cartosat1Cartosat1Cartosat1Cartosat1

EDC DAAC

Eurimage

Radarsat

Eosat,

NRSA

Orbimage

Eosat,

NRSA

Spot Image

EDC DAAC

NRSA

Space

Imaging

NRSA

NRSA

Orbital Sci.

Dornier

Spar

Aerospace

ISRO

Orbital Sci.

ISRO

Matra

Macroni

Orbital Sci.

ISRO

Eosat

Digital

Globe

ISRO

Mar-84

Apr-95

Nov-95

Dec-95

Aug-97

Sep-97

Mar-98

Apr-99

May-99

Sep-99

Oct-01

May-05

-

-

-

5.8/70

10/117

-

-

1/11x11

0.6/

16.5x16.5

2.5/

21.5x21.5

30-80 /185

36.25 /131

-

23.5-70.5

/142

1000/ 2800

23.5-70.5

/142

20/117

4000/1150

15/185

360/1420

4/11x11

2.4/

16.5x16.5

-

-

26/102

7.6-100

/

50-500

-

-

-

-

-

-

-

-

16

35

24

24

16

24

26

-

26

16

2

3

1to3.5

5

Table Table Table Table –––– 3.2: Oper 3.2: Oper 3.2: Oper 3.2: Operational Satellitesational Satellitesational Satellitesational Satellites


• Geographic Information System:Geographic Information System:Geographic Information System:Geographic Information System:

A geographic information system (GIS) is a computer based tool for

mapping and analyzing geographic phenomenon that exists and events that

occur on earth. GIS technology integrates common database operations such as

query and statistical analysis with the unique visualization and geographic

analysis benefits offered by maps. These abilities distinguish GIS from other

information systems and make it valuable to a wide range of public and private

enterprises for explaining events, predicting outcomes, and planning strategies.

Map making and geographic analysis are not new, but a GIS performs these

tasks faster and with more sophistication than do traditional manual methods.

In general, a GIS provides facilities for data capture, data management,

data manipulation and analysis and the presentation of results in both graphic

and report form, with a particular emphasis upon preserving and utilizing

inherent characteristics of spatial data.

The ability to incorporate spatial data, manage it, analyze it and answer

spatial questions is the distinctive characteristic of GIS.

A geographic information system, commonly referred to as GIS, is an

integrated set of hardware and software tools used for the manipulation and

management of digital spatial (geographic) and related attribute data.

• GIS Subsystems:GIS Subsystems:GIS Subsystems:GIS Subsystems:

A GIS has four main functional subsystems. These are

- data input subsystem

- data storage and retrieval subsystem

- data manipulation and analysis subsystem

- data output and display subsystem

- Data input:Data input:Data input:Data input:

A data input subsystem allows the user to capture, collect and transform

spatial and thematic data into digital form. The data inputs are usually derived

from a combination of hard copy maps, aerial photographs, remotely sensed

images, reports, survey documents etc as shown in figure 3.7.

- Data storage and retrieval:Data storage and retrieval:Data storage and retrieval:Data storage and retrieval:

The data storage and retrieval subsystem organizes the data, spatial and

attribute in the form which permits it to be quickly retrieved by the user for

analysis and permits rapid and accurate updates to be made to the database.

This component usually involves use of a database management system (DBMS)


for maintaining attribute data. Spatial data is usually encoded and maintained

in proprietary format.

Figure 3.7: Various data input devices/methods

---- Data manipulation and analysis: Data manipulation and analysis: Data manipulation and analysis: Data manipulation and analysis:

The data manipulation and analysis subsystem allows the user to define

and execute spatial and attribute procedures to generate derived information.

The subsystem is commonly thought if as the heart of a GIS, and usually

distinguishes it from other database information systems and computer aided

drafting (CAD) systems.

---- Data output: Data output: Data output: Data output:

The data output subsystem allows the user to generate graphic displays

normally maps and tabular reports representing derived information products.

• Components of GIS:Components of GIS:Components of GIS:Components of GIS:

An operational GIS also has a series of components that combine to make

the system work. A working GIS integrates five key components, namely

Hardware, Software, Data, People and Methods.

Figure 3.8: Components of GIS


- Hardware:Hardware:Hardware:Hardware:

Hardware is the computer system on which a GIS operates. Today, GIS

software runs on a wide range of hardware types, from centralized computer

servers to desktop computers used in stand-alone or networked configurations.

---- Software: Software: Software: Software:

GIS software provides the functions and tools needed to store, analyze,

and display geographic information.

---- Data: Data: Data: Data:

The most important component of a GIS is the data. Geographic data and

related tabular data can be collected in-house, compiled to custom

specifications and requirements, or occasionally purchased from a commercial

data provider. A GIS can integrate spatial data with other existing data

resources, often stored in a corporate DBMS. The integration of spatial data

(often proprietary to the GIS software) and tabular data stored in a DBMS is a

key functionality afforded by GIS.

---- People: People: People: People:

GIS technology is of limited value without the people who manage the

system and develop plans for applying it to real world problems. GIS users

range from technical specialists (who design and maintain the system) to those,

who use it in their everyday work. The identification of GIS specialists versus

end users is often critical to the proper implementation of GIS technology.

---- Methods: Methods: Methods: Methods:

A successful GIS operates according to a well-designed implementation

plan and business rules, which are the models and operating practices unique

to each organization.

• GIS Data Models:GIS Data Models:GIS Data Models:GIS Data Models:

GIS store information about the world as a collection of thematic layers

which can be linked together by geography. This simple but extremely powerful

and versatile concept has proven invaluable for solving many real-world

problems from tracking delivery vehicles, to recording details of planning

applications, to modeling global atmospheric circulation. The thematic layer

approach allows us to organize the complexity of the real world into a simple

representation to help facilitate our understanding of natural relationships.


Figure 3.9: GIS Thematic layers representing real world features

• GIS Data Types:GIS Data Types:GIS Data Types:GIS Data Types:

The basic data types in a GIS reflect traditional data found on a map.

Accordingly, GIS technology utilizes two basic types of data. These are:

- Spatial dataSpatial dataSpatial dataSpatial data – This describes the absolute and relative location of

Geographic features.

- Attribute dataAttribute dataAttribute dataAttribute data – which describes the characteristics of the spatial

features. These characteristics can be quantitative and/or qualitative in nature.

Attribute data is often referred to as tabular data.

The coordinate location of a forestry stand would be spatial data, while

the characteristics of the forestry stand, e.g. cover group, dominant species,

crown closure, height etc. would be attribute data.

• Spatial data models:Spatial data models:Spatial data models:Spatial data models:

Traditionally spatial data has been stored and presented in the form of a

map. Three basic types of spatial data models have evolved for storing

geographic data digitally. These are referred to as Vector, Raster and Image.

The following Figure 3.11 reflects the two primary spatial data encoding

techniques. These are vector and raster. Image data utilizes techniques very

similar to raster data, however typically lacks the internal formats required for

analysis and modeling of the data. Images reflect pictures or photographs of

the landscape.


Figure 3.10: Representation of the real world and showing differences in how a

vector and a raster GIS will represent this real world.

---- Vector Data Vector Data Vector Data Vector Data FormatFormatFormatFormat::::

All spatial data models are approaches for storing the spatial location of

geographic features in a database. Vector storage implies the use of vectors

(directional lines) to represent a geographic feature. Vector data is

characterized by the use of sequential points or vertices to define a linear

segment. Each vertex consists of an X coordinate and a Y coordinates.

Figure 3.11: Vector data format


Vector lines are often referred to as arcs and consist of a string of

vertices terminated by a node. A node is defined as a vertex that starts or ends

an arc segment. One coordinate pair, a vertex, defines point features. Polygonal

features are defined by a set of closed coordinate pairs. In vector

representation, the storage of the vertices for each feature is important, as well

as the connectivity between features, e.g. the sharing of common vertices

where features connect.

The vector data model does not handle continuous data, e.g. elevation,

very well while the raster data model is more ideally suited for this type of

analysis. Accordingly, the raster structure does not handle linear data analysis,

e.g. shortest path, very well while vector systems do. There are certain

advantages and disadvantages to each data model.

---- Ras Ras Ras Raster Data ter Data ter Data ter Data Format:Format:Format:Format:

Raster data models incorporate the use of a grid-cell data structure

where the geographic area is divided into cells identified by row and column.

This data structure is commonly called raster. While the term raster implies a

regularly spaced grid other tessellated data structures do exist in grid based

GIS systems. In particular, the quad tree data structure has found some

acceptance as an alternative raster data model.

Figure 3.12: Raster data format

Most raster based GIS software requires that the raster cell contain only a

single discrete value. Accordingly, a data layer, e.g. forest inventory stands,

may be broken down into a series of raster maps, each representing an


attribute type, e.g. a species map, a height map, a density map, etc. These are

often referred to as one attribute maps. This is in contrast to most conventional

vector data models that maintain data as multiple attribute maps, e.g. forest

inventory polygons linked to a database table containing all attributes as

columns. This basic distinction of raster data storage provides the foundation

for quantitative analysis techniques. This is often referred to as raster or map

algebra. The use of raster data structures allow for sophisticated mathematical

modeling processes while vector based systems are often constrained by the

capabilities and language of a relational DBMS.

• Spatial DatSpatial DatSpatial DatSpatial Data Relationships:a Relationships:a Relationships:a Relationships:

The nature of spatial data relationships is important to understand within

the context of GIS. The accepted theoretical solution is to topologically

structure spatial data.

Most GIS software segregate spatial and attribute data into separate data

management systems. Most frequently, the topological or raster structure is

used to store the spatial data, while the relational database structure is used to

store the attribute data. Data from both structures are linked together for use

through unique identification numbers, e.g. feature labels and DBMS primary

keys. An integral number assigned by the GIS software usually maintains this

coupling of spatial features with an attribute record for a given geographic

feature. Most often the GIS software is properly generated. This attribute’s

record once a clean topological structure is properly generated. This attribute

record normally contains the internal number for the feature, the user’s label

identifier, the area of the feature, and the perimeter of the feature. Linear

features have the length of the feature defined instead of the area.

• Topology:Topology:Topology:Topology:

Topology is a mathematical approach that allows us to structure data

based on the principles of feature adjacency and feature connectivity. It is in

fact the mathematical method used to define spatial relationships. Without a

topologic data structure, in a vector based GIS most data manipulation and

analysis functions would not be practical or feasible.


Chapter 4: MultiChapter 4: MultiChapter 4: MultiChapter 4: Multi----Criteria Decision Analysis (MCriteria Decision Analysis (MCriteria Decision Analysis (MCriteria Decision Analysis (MCDA):CDA):CDA):CDA):

• IntroductionIntroductionIntroductionIntroduction:

MCDA problems involve a set of alternatives that are evaluated on the

basis of conflicting and incommensurate criteria. Criterion can be an attribute

or objective. Accordingly MCDA can be classified into to categories, namely

Multi-Attribute Decision Analysis (MADA) and Multi-Objective Decision Analysis

(MODA). Both MADA and MODA problems are further classified into single

decision maker problems and group decision problems. These categories are

subdivided into deterministic, probabilistic and fuzzy decisions.

- Deterministic decision problems assume that the required data and

information are known with certainty and there is a deterministic relationship

between every decision and the corresponding decision sequence.

- Probabilistic analysis deals with a decision situation under uncertainty

with respect to available data and decision making sequence but treats

uncertainty as randomness.

- Fuzzy decision analysis also deals with a decision situation under

uncertainty with respect to available data and decision making sequence but

considers inherent imprecision of information involved in decision making.

COMPARISON OF MODA AND MADA

MODA MADA

Criteria defined by Objectives Attributes

Objectives defined Explicitly Implicitly

Attributes defined by Implicitly Explicitly

Constraints defined Explicitly Implicitly

Alternatives defined Implicitly Explicitly

No. of alternatives Large Small

Decision maker’s control Significant Limited

Relevant to Design/search Evaluation/choice

Decision modeling

paradigm Process oriented Outcome oriented

Data structure Vector based GIS Raster based GIS

Table 4.1: Comparison of MODA and MADA


• Elements of MultiElements of MultiElements of MultiElements of Multi----Criteria Decision Analysis (MCDA):Criteria Decision Analysis (MCDA):Criteria Decision Analysis (MCDA):Criteria Decision Analysis (MCDA):

MCDA problems involve six components:

- A goal or set goals the decision maker attempts to achieve

- The decision maker or group of decision makers involved in the

decision making process along with their preferences with respect to evaluation

criteria

- A set of evaluation criteria (objective and/or attributes) on the basis of

which the decision makers evaluate alternative courses of action

- The set of decision alternatives that is decision or action variables

- The set of uncontrollable variables

- The set of outcome or consequences associated with each pair.

The relationships between the elements of MCDA are shown in Figure-

4.1. The central element of this structure is a decision matrix consisting of a set

of columns and rows. The matrix represents the decision outcomes for a set of

alternatives and a set of evaluation criteria.

The structure of columns consists of levels representing the decision

makers, their preferences and evaluation criteria. These elements are organized

in a hierarchical structure. The rows of the decision matrix represent decision

alternatives. The decision outcomes depend on the set of evaluating

alternatives.

A criterion is a standard judgment or rule on the basis of which the

alternatives decisions are ranked according to their desirability. Criterion is a

generic term including the concept of attribute and objectives. Attribute

represents the properties of elements of a real world geographical system.

More specifically, an attribute is a measurable quantity or quality of

geographical entity or a relationship between geographical entities. An attribute

is used to measure performance in relation to an objective. An objective is a

statement about the desired state of the system under consideration. It

indicates the directions of improvement of one or more attributes. Objectives

are functionally related to or derived from a set of attributes

The state of nature or environment refers to the uncontrollable

environmental factors such as weather condition, physical condition, economy

etc. Each state is assumed to be independent of other states and immune to

manipulation by the decision makers. As such, the states of nature reflect the

degree of uncertainty about decision outcomes. Ultimately, the decision


problems require that the set of outcomes to be ordered so that the best

alternatives can be identified.

Figure 4.1: Relationship among the elements of MCDA

• Framework of Spatial MultiFramework of Spatial MultiFramework of Spatial MultiFramework of Spatial Multi----critercritercritercriteria Decision Analysis:ia Decision Analysis:ia Decision Analysis:ia Decision Analysis:

Framework organized in terms of the sequence of activities involved in

spatial multi-criteria analysis is shown in figure. It integrates the different

phases of decision-making and major elements of MCDA.

---- Problem definition:Problem definition:Problem definition:Problem definition:

Any decision making process begins with the recognition and definition

problem. It is a gap between the desired and existing states as viewed by a

decision maker. It overlaps the intelligence phase of decision making. The GIS

capabilities for data storage, management, manipulation and analysis offer

major support in the problem definition stage.

---- Evaluation criteria:Evaluation criteria:Evaluation criteria:Evaluation criteria:

Once decision problem is identified or defined, the spatial multi-criteria

analysis focuses on the set of evaluation criteria (i.e. Objectives and attributes).

A measurement scale must be established for each attribute. The degree to

which objectives are met, as measured by the attributes is the basis for

comparing alternatives. The evaluation criteria are associated with geographical

entities and therefore, relationships between entities, can be represented in the

Attribute1 Attribute2 Attribute3 ---- Attributen

Alternative1 Outcome11 Outcome12 Outcome13 ---- Outcome1n



------ ------ ------ ------ ---- ------

Alternativen Outcomen1 Outcomen2 Outcomen3 ---- Outcomenn

ST

AT

E O

F

EN

VIR

ON

MEN

T

Preferences Weight1 Weight2 Weight3 ---- Weightn

GOA

DECISION MAKER 1 DECISION MAKER 2

OBJECTIVE 1 OBJECTIVE 2 OBJECTIVE 3


form maps. Evaluated criteria maps are also called as attribute maps or

thematic maps or data layers.

---- Alternative and Constraints:Alternative and Constraints:Alternative and Constraints:Alternative and Constraints:

The process of generating alternatives could be based on the value

structure and related to the set of evaluation criteria. Decision variable is

assigned to each alternative. Depending on the problem situation, the decision

variables may be deterministic, probabilistic, or linguistic. A set of decision

variables defines the decision space. In real world situation, very few spatial

decision problems can be considered without any constraints or restrictions.

---- Decision maker’s preferences and decision matrix:Decision maker’s preferences and decision matrix:Decision maker’s preferences and decision matrix:Decision maker’s preferences and decision matrix:

The preferences are expressed in terms of weights of relative importance

assigned to the evaluation criteria under consideration. The purpose of criteria

is to express the importance of each criterion relative to the other criterion. For

given set objectives, attributes and associated weights, the input data can be

organized in the form of decision matrix.

---- Decision rule:Decision rule:Decision rule:Decision rule:

This step brings together the result of the preceding three steps. This is

accomplished by an appropriate decision rule of aggregation function. Decision

rules dictate how best to rank alternative or to decide which alternative is

preferred to another. Since a decision rule arranges alternatives according to

their preferences with respect to the set of evaluation criteria, the decision

problem depends on the selection of best outcome and the identification of the

decision alternative yielding this outcome.

---- Sensitivity analysis:Sensitivity analysis:Sensitivity analysis:Sensitivity analysis:

It is performed to determine the robustness of outcomes. Sensitivity

analysis is defined as a procedure for determining how the recommended

course of action is affected by changes in the inputs of the analysis. It aims at

identifying the effects of changes in the inputs (i.e. Geographical data and

decision maker’s preferences) on the outputs. If the changes do not

significantly affect the outputs, the ranking is consider3ed to be robust

otherwise we may use information about the output to return to problem

formulation step. In other words, sensitivity analysis may be considered as an

exploratory process-by which the decision maker achieves a deeper

understanding of the structure of the problem.


Figure 4.2: Framework of Spatial Multi-criteria Decision Analysis

---- Recommendation:Recommendation:Recommendation:Recommendation:

The end result of a decision making process is a recommendation for

future course of action. The recommendation should be based on the ranking

of the alternatives and sensitivity analysis. Visualization techniques are of major

importance in presenting and communicating the results to the decision maker

and interest groups. The solution of spatial multi-criteria decision problems

should be presented in both decision (geographical) space and criterion

outcome space.

RECOMMENDATION

PROBLEM

DEFINITION

EVALUATION CRITERIA

DECISION MATRIX

DECISION

SENSITIVITY ANALYSIS

CONSTRAINTS

ALTERNATIVE

DECISION MAKER’S PREFERENCE

INTELLIGENCE PHASE (GIS)

DESIGN PHASE (MCDM)

CHOICE PHASE (MCDM / GIS)


Chapter 5: Evaluation Chapter 5: Evaluation Chapter 5: Evaluation Chapter 5: Evaluation oooof Land Suitability f Land Suitability f Land Suitability f Land Suitability for Urban Development:for Urban Development:for Urban Development:for Urban Development:


The essence of land evaluation is to compare or match the requirements

of each potential land use with the characteristics of each kind of land. The

result is a measure of the suitability of each kind of land use for each kind of

land. These suitability assessments are then examined in the light of economic,

social and environmental considerations in order to develop an actual plan for

the use of land in the area. When this has been done, development can begin.

Ideas on how the land should be used are likely to exist before the formal

planning process begins. These ideas, which often reflect the wishes of the

local people, are usually included among the possible uses to be assessed in

the evaluation and will thus influence the range of basic data that needs to be

collected.

A wide range of specialist knowledge is needed to collect and analyze all

the data relevant to land evaluation. The work is best undertaken by a

multidisciplinary team that includes social and economic expertise as well as

biophysical scientists. Ideally, such a team should work together throughout the

study so that each member can influence the others with his or her special

knowledge and viewpoint.

In practice it is not always possible to field the whole team at once. In this

case, the physical aspects of land are usually studied and mapped first to

provide a geographical framework into which the socio-economic dimensions

are inserted later. A two-stage approach is obviously less well integrated and

will take longer to complete.

The reliability of a land evaluation can be no greater than that of the data

on which it is based. Ideally, fresh data should be obtained to answer all

questions raised by the study, although time and expense usually prevent this

being done as thoroughly as is possible. The one really important requirement

is that the evaluation process can be 'automated' and carried out quite rapidly

once all the necessary data are available, by setting up a computerized data

bank or geographical information system, and establishing rules or decision

trees to carry out the matching process which produces the evaluation.


• Stages in Land Suitability Evaluation:Stages in Land Suitability Evaluation:Stages in Land Suitability Evaluation:Stages in Land Suitability Evaluation:

Following are the different stages in Land Suitability Evaluation.

- Defining objectives

- Collecting the data

- Identifying land uses and their classification

- Identifying the physical parameters

- Identifying environmental and socio-economic issues

- Assessing land suitability

---- Defining objectives: Defining objectives: Defining objectives: Defining objectives:

The definition of objectives is a critical step in the evaluation procedure.

It also ensures that the investigations set off in the right direction, with a good

chance of providing all the advice that the planner will need.

The objectives must establish the boundaries and thus the size of the study

area. The objectives may be one or more as follows

- To ensure that all development is managed to protect the quality of life

- To encourage the development of a coordinated network of

environmental resources and open spaces through preservation

initiatives and the development process.

- To control the pace of development through availability of developable

land and adequate infrastructure

- Provide flexibility in development design that reflects the growing

needs and desires of the community.

- Ensure that the future land use plan provides for an appropriate mix of

land uses while preserving existing neighborhood characteristics

- Protect and conserve natural, agricultural, historic, scenic and open

space resources to improve the quality of life.

- Approve development only where adequate infrastructure exists or will

be available.

- Promote in-fill and redevelopment in existing communities

- Promote a range of alternative community designs to facilitate a pattern

of sustainable development.

The study team needs guidance on these issues because the choices

must reflect the special interests of the planners and the aspirations of local

people. Without this guidance the choice of land-use alternatives to be

considered could be infinite.

http://www.fao.org/docrep/u1980e/u1980e03.htm#1. defining objectives

http://www.fao.org/docrep/u1980e/u1980e03.htm#2. collecting the data

http://www.fao.org/docrep/u1980e/u1980e03.htm#3. identifying land uses

http://www.fao.org/docrep/u1980e/u1980e04.htm#6. identifying environmental and socio economic issues

http://www.fao.org/docrep/u1980e/u1980e04.htm#5. assessing suitability


In framing the objectives, the need for comparisons in land-use planning

must be recognized. The prime objective of the study may be to establish the

suitability of a particular kind of use, but this can be achieved most effectively

by making comparisons with other feasible uses of the same land.

Environmental conservation is always an objective of land evaluation.

The major stages are to:

- identify relevant types of land use;

- carry out surveys to establish needs and wishes of the local land users

and needs of the community as a whole;

- And rank objectives in order of priority.

- Collecting the data:Collecting the data:Collecting the data:Collecting the data:

Reliable knowledge of land characteristics, and of the way these differ

from place to place, is essential to good land evaluation. The range of data that

could be relevant to land evaluation is huge, and collecting it can be costly, in

both time and money. There are three main ways to minimize costs:

- Focus on data that are essential to the evaluation;

- search out and make maximum use of existing data; and

- use new technology in data collection.

The need and kinds of physical data that may be considered for land

suitability analysis for urban development are summarized in the Table 5.1.

Sl

No Parameter Category Constraint Development Considerations

Soil depth Foundation

inadequacy

Very deep to deep soils are required for

urban development from the foundation

point of view as well as providing infra-

structural facilities. The cost to be

incurred for developing rocky areas is

very high and uneconomic.

1 Soil

Soil

texture

Foundation

inadequacy

Areas with unsuitable foundation

materials such as swelling / shrinking

soils, compressible soils etc. Pile

foundation is required in such soils which

is expensive.

2 Physiographic Slope Steepness and

stability

Land where high and medium slopes

(more than 7 per cent) provides

constraint for urban land use and


development. It is uneconomic to develop

this type of lands.

3 Land use

Agricultur

al and

forest

lands

Productivity

Productive agricultural and forest lands

should not be considered for

development as they are essential for

producing food and fiber and fiber/wood

etc.

4 Flood

Flood

plains and

low lying

areas

Land

subjected to

flooding

Development of low lying areas is not

cost effective.

5 Erosion Ravenous

land

Land

subjected to

gully erosion

These are loose and unconsolidated

material areas where the development

cost is quite high.

6 Ground water

Excellent,

very good

and good

prospects

Nil

These are the areas to be conserved for

the purpose of future water requirements

and not to be taken up for the

development as water has already been

over exploited.

7 Surface water Lakes /

ponds Nil Needs conservation for future use

8 Drainage Rivers /

streams

Poorly or

excessively

drained

Land where the drainage status is a

problem

9 Deposition Mining /

quarrying

Water storage

/ depositional

land area

Land where sediment removal during

conventional earthworks for urban

development is likely to be excessive,

causing damage to receiving waters and

depositional land areas.

10 Road Road

network Infrastructure

Areas nearer to transportation network

have higher potential for development.

Areas away from the road network require

development of infrastructure facilities

and are expensive.

11 Rail Rail head

network - do - - do -

Table 5.1: List of physical parameters and their importance in land suitability

for urban development


The new technology that is available for land evaluation consists mainly

of the use of remote sensing and computers. Stereoscopic examination of

paired, black and white, photographs obtained by conventional aerial

photography - the best tested form of remote sensing - remains the mainstay

for interpretation of landform, vegetation, land use, soils and geology, and for

other purposes such as contouring.

Sources of data

Type of

survey Data source Range of data

Satellite Digital tapes, photographs,

other imagery

Water resources, vegetation, land

use, infrastructure, landform, soils

Aircraft Photographs (conventional,

infra-red), radar imagery

Landform, soils, vegetation, land

use, farm boundaries, water

resources, crops, infrastructure

Ground Reports, questionnaires, maps

Soil, climate, landform, vegetation,

land use, population, social and

economic data

Table 5.2: Sources of data

While the newer forms of remotely sensed imagery (such as infra-red and

radar) may not yet match the precision or stereoscopic capability of

conventional air photography, they have other advantages. Each image sensed

from space covers a comparatively large area - especially helpful in analyzing

and mapping landform. Satellites return at regular intervals to obtain new

imagery of the same sites, so that libraries of sequential imagery can be built

up showing the changes at a single site over time. Satellites can now record at

up to seven different wavelengths simultaneously. Radar wavelengths are

particularly useful in the humid tropics because they can obtain images of the

Earth through dense cloud.

Computers can now be used to store and manipulate the huge amounts

of data needed in land evaluation. Tough, portable, micro-computers are being

increasingly used to record, store, interpret, test and communicate data at the

survey site itself. The main impact of these new technologies has been to save

time and money, and to extend the range and depth of land evaluation,


allowing data a greater complexity of land-use alternatives to be collected than

was possible in the past.

However, many kinds of data have to be collected in traditional ways. The

soil surveyor must dig or drill holes to describe the sequence of soil 'horizons'

with depth. The hydro-geologist may have to drill deeper holes to prove the

existence of suspected groundwater whilst hydrologists set up gauges on

streams to measure surface water flow. The meteorologist has to rely on

systematic measurements of change in the weather at established weather

stations. Agriculturalists, economists and sociologists observe people in action

in farms, villages and markets and, by means of questionnaires and other

enquiries, establish the patterns of their business. These and other scientists

collect the central core of basic data on land much as they have done for

decades.

• IdentiIdentiIdentiIdentifying lfying lfying lfying land useand useand useand usessss and their classification and their classification and their classification and their classification

---- D D D Definitions:efinitions:efinitions:efinitions:

The distinction between land cover and land use is fundamental. They are

defined as follows (Sims, 1995; De Bie, 1995):

‘Land cover is the observed physical cover, as seen from the ground or

through remote sensing, including the vegetation (natural or planted) and

human constructions (buildings, roads, etc.) which cover the earth's

surface. Water, ice, bare rock or sand surfaces are counted as land cover.’

‘Land use is based upon function, the purpose for which the land is being

used. Thus, a land use can be defined as a series of activities undertaken

to produce one or more goods or services. A given land use may take

place on one, or more than one piece of land, and several land uses may

occur on the same piece of land.’

Definition of land use in this way provides a basis for precise and

quantitative economic and environmental impact analysis, and permits precise

distinctions between land uses if required.


-Classification of land cover/land useClassification of land cover/land useClassification of land cover/land useClassification of land cover/land use::::

Classification is an abstract representation of the situation in the field

using well-defined diagnostic criteria, the classifiers. Sokal (1974) defined it as

"the ordering or arrangement of objects into groups or sets on the basis of

their relationships". A classification system describes the names of the classes

and the criteria used to distinguish them. A classification is, therefore, scale

independent and is independent of the means used to collect information

(whether satellite imagery, aerial photography or field survey or a combination

of them are used).

---- Classification structure: Classification structure: Classification structure: Classification structure:

- Hierarchical Systems: Hierarchical Systems: Hierarchical Systems: Hierarchical Systems:

Classification systems come in two basic formats, hierarchical or

non-hierarchical. A hierarchical classification offers more flexibility

because of its ability to accommodate different levels of

information, starting with structured broad-level classes which

allow further subdivision into more detailed sub-classes.

----Criteria for a (Reference) Land Cover Classification:Criteria for a (Reference) Land Cover Classification:Criteria for a (Reference) Land Cover Classification:Criteria for a (Reference) Land Cover Classification:

There are many classification systems in existence throughout the world.

However, there is no single internationally accepted land cover classification

system. Such a system should meet the criteria that:

- It must be comprehensive.

- It should be a common reference basis for all derived (and when

possible existing) classifications.

- It meets the needs of a variety of users (it should not be single project

oriented) which may take only part of the classification and develop from

there according to their own specific needs.

- it must be arranged in a hierarchical structure to be used at different

scales and at different levels of detail allowing cross-reference of local /

regional with continental/global maps without loss of information. Some

existing classifications are designed to be used at a specific scale and/or

consider only or mainly classes derived from satellite imagery.

- It must be able to describe all land cover features as derived from its

general definition.

- It must be adaptable to the variety of land cover types (all possible

combinations of the classifiers should be considered).


- A class must be defined by a combination of well-defined diagnostic

criteria, the classifiers. In most classifications there is an unclear or

unsystematic description of the classifiers from which the class should be

derived.

- Classes must be mutually exclusive and unambiguous.

- A clear distinction must exist between the types of classifiers used.

Often no underlying common principle has been identified and used to

define land cover. These factors influence land cover but are not inherent

features of it. This type of combinations are frequently applied in an

irregular way and often do not follow any hierarchy. This leads to

confusion in the final nomenclature.

- The diagnostic criteria or classifiers used in the classification must be

selected because of easy measurement and permanence.

- It should be suitable for mapping and monitoring purposes.

- It must be scientifically sound and practically oriented.

A primary source for the persistent use of “land use land cover”, or LULC,

is the so-called Anderson classification system, published by James Anderson

and colleagues in the 1976 USGS Professional Paper 1964. The Anderson

system formed the backbone of most land feature mapping done by the USGS

during the last three decades, although the classification schema has been

slightly altered over time. Land cover mapping is not standardized at present. It

is unlikely that it can be standardized at finer levels of detail given the vast

differences in the landscape across geographies, and the many different

purposes for which land feature mapping is required. The USGS has produced

several iterations of land cover and use maps for the continental U.S. that may

be considered as standard-setting.

The classification system used for NLCD is modified from the Anderson

land-use and land-cover classification system. Many of the Anderson classes,

especially the Level III classes, are best derived using aerial photography. It is

not appropriate to attempt to derive some of these classes using Landsat TM

data due to issues of spatial resolution and interpretability of data. Thus, no

attempt was made to derive classes that were extremely difficult or

“impractical” to obtain using Landsat TM data, such as the Level III urban

classes. In addition, some Anderson Level II classes were consolidated into a

single NLCD class.


- Similarities and differences between Anderson and NLCD systems Similarities and differences between Anderson and NLCD systems Similarities and differences between Anderson and NLCD systems Similarities and differences between Anderson and NLCD systems:

-Urban or builtUrban or builtUrban or builtUrban or built----up classes:up classes:up classes:up classes: Commercial, Industrial, Transportation,

and Communications/Utilities (all separate Anderson Level II

classes) were treated as one NLCD class (Commercial/Industrial/

Transportation). No attempt was made to derive Anderson Level III

classes in NLCD. “Recreational” grasses, such as those that occur in

golf courses or parks (treated as an urban class by Anderson) are

considered to be a non-urban class in NLCD (a subdivision of

“Herbaceous Planted/Cultivated). Residential (an Anderson Level II

class) was divided into Low and High Intensity classes in NLCD.

- Water:Water:Water:Water: Anderson Level II Water classes (Streams/Canals, Lakes/

Ponds, Reservoirs, Bays, Open Marine) were classed as a single

class (Open Water) in NLCD.

---- Agriculture: Agriculture: Agriculture: Agriculture: Agricultural areas that are herbaceous in nature

(Cropland and Pasture; Anderson Level II) are subdivided into four

NLCD classes: Pasture/Hay, Row Crops, Small Grains and Fallow.

---- Rangeland: Rangeland: Rangeland: Rangeland: No rangeland class (Anderson Level I) is identified by

NLCD. Rather, “rangeland” is subdivided by NLCD into

Grasslands/Herbaceous and Shrub land classes.

---- Forest land: Forest land: Forest land: Forest land: Evergreen Forest, Deciduous Forest and Mixed Forest

are the same in both Anderson and NLCD. Clear-cut and burned

areas are classed as “Transitional Bare” areas in NLCD.

---- Wetlands: Wetlands: Wetlands: Wetlands: Two classes are defined by NLCD. These are Woody

wetlands and Emergent/Herbaceous wetlands. These are very

analogous to the Anderson Level II wetland classes.

---- Bare: Bare: Bare: Bare: Three NLCD classes are recognized. These are: Bare

Rock/Sand Clay, Quarries/Strip Mines/Gravel Pits and Transitional

Bare. These represent a consolidation of Anderson Level II classes.

---- Tundra: Tundra: Tundra: Tundra: While “tundra” is treated as a distinct Anderson Level I

class, tundra (including arctic/alpine vegetation) is considered to

be either “Grasslands/Herbaceous” or “Shrub land” classes by

NLCD.


• Identifying environmental and socioIdentifying environmental and socioIdentifying environmental and socioIdentifying environmental and socio----economic issues:economic issues:economic issues:economic issues:

The land suitability not only is based on a set of physical parameters but

also very much dependent on the socioeconomic factors. Before a land use can

be recommended in a development plan, its environmental and socioeconomic

implications must be evaluated further. A new or improved land use can

succeed only if it can be adapted to fit local social and economic conditions.

Socio-economic investigations are therefore a vital part of land evaluation,

starting with the initial formulation of the study's objectives. Attention needs to

be given to markets (local, national and perhaps even international), population

levels and growth rates, the availability of skilled and unskilled labour,

transport of products and inputs, availability of building materials etc. Local

religions and cultures may be important. Political circumstances cannot be

ignored, and any analysis should take account of the needs of all members of

the population, including minority groups.

• Assessing suitability:Assessing suitability:Assessing suitability:Assessing suitability:

Suitability is a measure of how well the characteristics of a land match the

requirements of urban development. The preparation of urban development

plan requires consideration of all components of the environment that exist

before the new plan’s creation and the environment to be created by the new

development plan. The plan may not be effective if any of these components

are treated separately or loosely. Therefore the development plan should inter-

relate all elements that form a community. It is primarily because, the land is a

concrete form and any plan must be flexible enough to change established uses

either to correct mistakes or to accommodate changing needs. The steps that

are followed in the preparation of development plan proceeds from deciding

what land to develop to when and how to develop it. So the development plan

should encompass physical characteristics, constraints and socioeconomic

possibilities. Basically it refers to the potentiality of the land for the

development. Land potentiality includes both land suitability as well as land

value. The land suitability designates land according to its physical capability

regardless of any planner’s conceptual interest. The integration of land

suitability map and land value map produces a land potential map which can be

later combined with the socio-economic variables to prepare final alternative

development plan.


Identification of suitable areas for urban development is, therefore, one

the critical issues in the preparation of the development plan. The land

suitability not only based on a set of physical parameters but also very much on

socioeconomic factors. The composite effect of these parameters determines

the degree of suitability and also helps in further categorizing the land into

different classes of development. Also, the process of suitability assessment is

very much dependent upon the prevalent conditions such as high pressure on

land for development. If the pressure is on the land is too high, then it may lead

to a high order of speculation and development of land which is otherwise not

suitable from suitability analysis point of view. Therefore land suitability may be

viewed as prioritization of land for urban development.


Chapter 6: Study AreChapter 6: Study AreChapter 6: Study AreChapter 6: Study Area a a a ---- Bangalore Bangalore Bangalore Bangalore


The new Bangalore district came into existence from August 15th 1986

with the division of the erstwhile Bangalore district into Bangalore Rural and

Bangalore Urban districts. It is smallest among the districts of Karnataka State

with an area of about 2,191sqkm, but in population it stands first. Bangalore

district, as shown in Figure 6.1, has three taluks viz., Anekal, Bangalore North

and Bangalore South. The city is the headquarters of both the Bangalore Urban

and Bangalore Rural districts.

Figure 6.1: Location map of Bangalore City


• HHHHistoryistoryistoryistory::::

Several speculations have been made about how the name "Bangalore"

came about. Based on information from the Gazetteer of India, Karnataka State,

Bangalore District section, the name "Bangalore" is an anglicized version of

"Bengalooru," a word in the local Kannada language that was given to a town.

The story goes that this word was derived from the phrase "benda kaalu ooru,"

which translates into "the town of boiled beans". It is said that King Ballala of

the Hoysala dynasty lost his way in the jungle while on a hunting expedition.

Tired and hungry, he encountered a poor, old woman who offered him the only

food she had - some boiled beans. Grateful to her, the king named the place

"bende kaalu ooru." However, historical evidence shows that "Bengalooru" was

recorded much before King Ballala's time in a 9th century temple inscription in

the village of Begur. "Bengalooru" still exists today within the city limits in

Kodigehalli area and is called "Halebengalooru" or "Old Bangalore."

- Kempe Gowda Marks The Four Corners Of The City:Kempe Gowda Marks The Four Corners Of The City:Kempe Gowda Marks The Four Corners Of The City:Kempe Gowda Marks The Four Corners Of The City:

Another historical figure instrumental in shaping the city of Bangalore is a

feudal lord who called himself Kempe Gowda, and who served under the

Vijayanagara Kings. Hunting seemed to be a favorite past time in those days.

During one of his hunting bouts, Kempe Gowda was surprised to see a hare

chase his dog. Either his dog was chicken hearted or the hare was lion-hearted

one does not know, but the episode surely made an impression on the feudal

lord. He told himself this is a place surely for heroes and heroics, and he

referred to Bangalore from then onwards as "gandu bhoomi" (heroic place).

Kempe Gowda-I, who was in charge of Yelahanka, built a mud fort in 1537.

With the help of King Achutaraya, Kempe Gowda-I built the little towns of

Balepet, Cottonpet, and Chickpet, all inside the fort. Today, these little areas

serve as the major wholesale and commercial market places in the city. Kempe

Gowda's son's erected the four watch towers to mark the boundaries of

Bangalore which are traceable even today and they stand almost in the heart of

the present city. A hundred years later the Vijayanagara Empire fell, and in

1638, it was conquered by Mohammed Adil Shah, the Sultan of Bijapur.


---- Power Shifts from Sultans to Marathas to British: Power Shifts from Sultans to Marathas to British: Power Shifts from Sultans to Marathas to British: Power Shifts from Sultans to Marathas to British:

In 1638, Bangalore was conquered by Bijapur Sultan and ruled for next 50

years. Later it was captured by Mughals who held it for 3 years. In 1687, the

Mughal Sultan of Sira province sold Bangalore to king Chikkadevaraja Wodeyar

of Mysore for 3 lack pagodas, who built a second fort to the south of that built

by Kempegowda-I. In 1759, Hyder Ali received Bangalore as a jagir from

Krishna raja Wodeyar II. He fortified the southern fort and made Bangalore an

army town. When Tipu Sultan died in the 4th Mysore war in 1799, the British

gave the kingdom, including Bangalore back to Krishna raja Wodeyar III. The

British Resident stayed in Bangalore. In 1831, alleging misrule by Krishna raja

Wodeyar III, the British took over the administration of the Mysore Kingdom.

Under the British influence, Bangalore bloomed with modern facilities like the

railways, telegraphs, postal and police departments. In 1881, the British

returned the city to the Wodeyars. Diwans like Mirza Ismail, and sir

Vishweshwarayya were the pioneers to help Bangalore attain its modern

outlook. With the direct rule of the British Commissioners based in Bangalore, it

became the State Administrative HQ. The destiny of Bangalore thus took a

historic turn, making it eventually a major city of India and one of the fastest

growing in the world. After independence, Bangalore's choice as a state capital

was only logical. Mysore had too many associations with the royal family to be

the capital of a new state with an elected Chief Minister and a nominated

Governor. Finally, for an enlarged Karnataka, Bangalore was more central and

better linked with the major cities of the country.

---- Transformation of Bangalore City into Silicon Valley: Transformation of Bangalore City into Silicon Valley: Transformation of Bangalore City into Silicon Valley: Transformation of Bangalore City into Silicon Valley:

Today, Bangalore is booming, and a look at some of its nicknames says

why: "India's Silicon Valley," "Fashion Capital of India," "The Pub City of India,"

and so on. Home to well over 6.5 million people, and a base for 10,000

industries, Bangalore is India's fifth largest city and the fastest growing city in

Asia. The Bangalore has witnessed historical progression showing that workers

drifted from extractive agriculture to manufacturing and then services, followed

by a further shift to knowledge-based activities.

Lately, models of telematics-spurred information societies have been

forwarded as global phenomena that could spread to the entire world and usher

in sustainable development. Historically, Bangalore lacked major rivers running

around it or in the nearby environs. Artificial lakes or tanks were dug by the


city's kings to provide a water supply and support businesses, orchards,

military and administrative personnel. The defeat of the Vijayanagara Empire in

1565 swung trade routes and commercial activity in Bangalore's direction and

converted it into the leading economic locale in the Deccan plateau. Under Tipu

Sultan in the eighteenth century, the city experienced spurts in textiles,

metallurgy, ordnance and postal communications. British advent caused

industrial decline but made Bangalore a node within the colonial information

network, installing the first telegraph line in 1854. The city achieved a

reputation as a model princely state in the late colonial period. In 1898-9, it

had the first telephone lines in the country to coordinate anti-plague measures.

In 1900, it became India's first electrified city supplying power to run the Kolar

gold fields and steam textiles. M Visvesvarayya, the dynamic Diwan (chief

minister) of the Mysore kingdom from 1912 to 1918, flagged off major strides

for Bangalore in iron and steel, irrigation, education and engineering. He

imagined Bangalore as”science city" with "contributory facilities based on

information systems" as aids to trade at the time of India's independence, the

city had an emerging entrepreneurial and technological base. Being host to

public sector giants like Hindustan Aeronautics, Bharat Electronics and

Hindustan Machine Tools, Bangalore enjoyed a mushrooming of a range of

technical and service ancillaries in its conurbation. City planners followed

British city models and relocated factories from residential areas to distant

outskirts. Private businesses also expanded steadily due to the availability of

power, transportation and water. Prime Minister Jawaharlal Nehru took a

personal interest in Bangalore's profile as a scientific-industrial city. He saw it

as the "city of the future" and the "template of a modern India". By 1971, the

Bangalore metropolitan region supported a buoyant regional economy

attracting medium and small-scale industries. Corporate head offices fled the

left-wing militancy of Calcutta and settled in Bangalore, rewarding its liberal

industrial policy. As of 1991, the Bangalore region stood out among other

Indian cities for being an innovative haven, but "from a global perspective, it

was not an especially wealthy or healthy place". Dramatic unregulated growth of

the urban sprawl exceeded civic managerial capacity. All the major lakes

disappeared. Demand outstripped supply in housing and other major utilities.

More than 80% of newly built flats were being grabbed by land speculators,

pushing up real estate prices and slum populations. Environmentalists jabbed


at Bangalore as a "formerly model city". The cash-strapped state government

responded to this urban infrastructure crisis with the solution of partial

privatization. Geographic information systems were increasingly used to

superimpose spatial adjustments over existing maps in planning documents.

Computerized mapping and specialized consulting firms were hired by the

authorities to solve congestion and construction overkill. Equipped with

evolving technologies, planners drew new towns that could draw away

population from Bangalore as "counter magnets". Benchmarking programs

against the standards of Singapore, technocratic authorities pledged to deploy

the appropriate technology to enhance efficiency. It may be noted that a major

change in planning methodology evolved, wherein "centralized modes of

organization evolved into multi-entity networks constructed around electronic

information systems". The nature of the state was less "developmental" and

more in line with "coordinating" conditions for economic growth led by the

private sector. NGOs and citizen-based organizations were the other non-state

actors that played instrumental roles in the inter-organizational networks that

signified change in Bangalore. These "third force" groups strove for

decentralizing urban self-governance and involving the end-users of service

delivery in decision making about city amenities. Banking on the hypothesis

that information flows advance efficiency, they galvanized denizens for

participatory planning. Bangalore acquired an international reputation as India's

"Silicon Valley/Plateau" suddenly in the 1990s. But it was the denouement of

"the gradual accumulation of skills and capital since the beginning of the

twentieth century". The division of labor statistics in 1991 hardly fit the image

of a city with a milieu of innovation, with barely any Silicon Valley

characteristics. However, a series of state-engineered developments did

engender a niche within Bangalore's industrial economy that pushed technology

frontiers. Private enterprises like Wipro Infotech responded to Prime Minister

Rajiv Gandhi's electronics sector liberalization in 1984 and became one of the

city's first global successes. Infosys Consultants (later Technologies) bagged

software outsourcing and body-shopping agreements after Rajiv Gandhi's aides

facilitated a contract with General Electric in 1988. The success of these flag

bearers and USAID publications increased the interest of US technology

companies in Bangalore. Motorola, Oracle, Sun Microsystems and Hewlett

Packard established subsidiaries in the city, benefiting from its economic


liberalization policies. American companies were the largest group of foreign

investors fascinated by electronics and telematics in Bangalore. Software dove-

tailed with India's new export-oriented growth strategy and made up the most

positive contribution by Bangalore to the country's trade balance. The

Karnataka state government's interventions were also crucial in Bangalore's

leapfrogging technology curve. Besides launching the "Electronics City" complex

and building Software Technology Parks, it engaged in importuning propaganda

promoting Bangalore as a "Silicon Plateau" with themes like "the future is here".

Such marketing techniques intersected with a time of hyperbole and great

expectations for Indians trail blazing the fields of computers and

telecommunications. US commentators added fuel to fire by claiming that

"Bangalore has put together all the ingredients of a broad frontal attack on

American hegemony of the information revolution". But rising production costs

and infrastructure shortages emerged in the late 1990s and so did domestic

competitor cities like Pune and Hyderabad. Besides information technology,

several other ingredients determine Bangalore as an information society.

Bangalore urban district has an overall literacy rate of 86%. Bangalore University

boasts of 375 colleges that include 21 reputed engineering schools. The city is

home to 25,000 software and computer science engineers within an all-India

total of 220,000. In response to market demands for business-savvy techies,

the Indian Institute of Information Technology, Bangalore (IIIT-B) is churning

out batches of engineers who have undergone two terms of classes in industry

and corporate management. Bangalore's Indian Institute of Science (IISc) ranks

among the top 20 universities of the world. Its faculty members consult about

100 projects for industry every year. The availability of expert research

consultants and digitized databases are major causes for the clustering of New

Economy firms in the city. Bangalore also has a massive and pervasive print

culture, with 67 book publishers, 110 newspapers and countless specialized

magazines disseminating information to numerous social groups. A dense array

of film theaters makes the city an important source of visual information. More

than 80% of Bangaloreans own transistor radio sets, components of an

impressive electronic information system. Television broadcasting in India is

intertwined with the country's space program headquartered in Bangalore.

Cable and satellite penetration in the city is 59%. It has 1.6 million telephone

lines, one for every nine persons. Many cellular companies maintain


headquarters or technical offices in Bangalore. The Internet user population in

the city in 2001 stood at 80,000, emanating from 750 educational institutions.

Multinationals are benefiting from a vast increase in bandwidth for business

ends. It may be concluded that Bangalore "went online during a twenty-year

period" and "informatized" as a city. It can today be considered a regional

cluster within a global neo-liberal paradigm. This is both strength and

weakness, for worldwide booms and busts in IT and bio-informatics would

synchronize crests and troughs in Bangalore's economy. On the social front, the

application of hi-tech solutions has abetted transparency and popular

participation but also concentrated wealth and power in the hands of elites.

Digital democracy is a far cry in the network city. According to a study

conducted by the Confederation of Indian Industry (CII) and Karnataka's Vision

Group on Biotechnology, in 2001-02 there were 72 companies dealing in core

biotech areas employing over 5,000 people, of which 3,500 were scientists. The

total project investments were over Rs. 500 crores (1990-2001) with a total

venture capital funding of over Rs. 70 cores in the past two years, making a

total revenue of Rs. 700 crores, of which Rs. 250 crores came from exports. In

2002-03 there are about 100 biotech companies in Karnataka.

• Drainage:Drainage:Drainage:Drainage:

There are no major rivers flowing in the district. The Arkavati River flows

in the district for a small distance in Bangalore North taluk. The Dakshina

Pinakini touches and borders of the district to the north-east of the Anekal

taluk. The Vrishabhavati, a tributary of the Arkavati, flows in the district before

joining the Arkavati near Muduvadidurga. The tributary takes its birth in the

Bangalore city at Basavanagudi and the Suvarnamukhi from Anekal taluk joins

the tributary before joining the Arkavati. The Basavanahole originating beyond

the Muthyalamadu falls passes through Anekal taluk and joins the Arkavati near

Kanakapura. The western portion of Anekal taluk is marked by a continuous

chain of hills through which several rivulets combine together and drain into

the Arkavati. The rain water falling on eastern portions of the taluk drains into

the South Pinakini beyond the state boundary.


• Climate:Climate:Climate:Climate:

Bangalore is considered to be climatically a well favored district situated

in the heart of South Deccan of Peninsular India. The climate of the district is

classed as the seasonally dry tropical savanna climate with four seasons.

- The dry season with clear bright weather is from December to

February.

- The summer season from March to May

- The South-west monsoon season from June to September.

- The post monsoon season starts from October and November.

On the basis of mean monthly temperatures, April is usually the hottest

month with the mean daily maximum temperature at 33.4°C and the mean daily

minimum at 21.2° C. on individual days, in hot season, the day temperatures

often go above 36° C. With the onset of the monsoon early in June, there is

appreciable drop in the day temperatures but that in night temperature is only

slight. In October, the temperatures are as in the south-west monsoon season.

Thereafter, temperatures decrease. December is generally the coolest month

with the mean daily maximum temperature at 25.7° C and the mean daily

minimum at 15.3° C Nights during January are however slightly colder than

during December.

The mean monthly relative humidity is the lowest in the month of March

and humidity is high during the period June of October, i.e., between 80% to

85% on the average. Humidity decreases thereafter and the period February to

April, the air is comparatively drier, the afternoon relative humidity being 25%

to 35%.

• Rainfall:Rainfall:Rainfall:Rainfall:

The average annual rainfall of Bangalore urban district is 751.6 mm and

the mean number of rainy days is about 57. Bangalore has three different rainy

periods covering eight months of the year followed closely one after the other.

Of these, June to September is the principal rainy season. Bangalore receives

54% of the total annual rainfall in the south-west monsoon period, 36% of the

annual rainfall during north east monsoon. Thus 80 percent of the annual rain

falls during the six months June to November Remaining 20 percent of the

rainfall during April and May. December to March is comparatively rainless

period, with a mean rainfall of 33 mm and about 3 to 4 rainy days. The details

of the rainfall for each taluk are given in the following Table 6.1.


District Name of the

taluk

Average annual rain fall in

mm

Bangalore North 789.03

Bangalore South 722.48 Bangalore urban

Anekal 737.35

Table 6.1: Rainfall in Bangalore Urban District

• Geology:Geology:Geology:Geology:

The entire Anekal taluk is composed of only one type of rock called

gneissic granites belonging to Precambrian age. These rocks are exposed as a

continuous chain of mounds raising 90 to 150 meters above the general

ground level on the western portion of the taluk. These form the Bannerghatta

group of hills. Rocky outcrops are few and far between the middle and eastern

portions of the taluk. Inclusions of quartz and pegmatite veins are common.

The depth of weathering varies greatly. The central and eastern portions of the

taluk show maximum thickness of the weathered mantle, extending to more

than 12 meters. The western portion of the taluk is deeply dissected and rocky.

The chief rock types occurring in Bangalore North taluk are granites and

gneisses. These are prominently exposed as a ridge running NNE and SSW

almost in the middle of the taluk. The granitic gneisses are crisscrossed by

Pegmatitic and Aplitic veins. Basic Xenolithic patches are common. Banding is

prominent. The rocks are highly jointed, and sheet jointing parallel to the

exposed surface is particularly characteristic of the Bangalore gneisses.

Granites and gneisses are intruded by a number of basic dykes. Dykes are

oriented east-west and as well as north-south. Cappings of laterites are found

at the highest point in Bangalore, beneath the laterite, the gneisses are deeply

weathered giving into various shades of clay. These exposures are well seen

near Yelahanka and neighborhood.

Bangalore south taluk comprises granites and granitic gneisses belonging

to Pre-Cambrian age. The granitic gneisses are exposed as continuous chains

of mounds raising 30 to 70 meters above ground level in the southern region of

the taluk. Granites are medium to coarse grained hard, compact and massive.

Granitic gneisses are distinctly banded and are in various shades of grey color.

The strike of foliation is usually NNW-SSE. They are traversed by Pegmatitic and

aplitic veins. Sheet jointing is very common. Granites and gneisses have


undergone different degree of alteration and decomposition. Southern and

eastern regions of the taluk show maximum thickness of weathered mantle

extending up to about 20 meters while the depth of the weathered zone is

maximum generally in the valleys, in highly cut-up terrain, as in the southern

parts of the taluk. Granites and granitic gneisses are traversed by vertical and

horizontal joints and are intruded by dolerite dykes.

• Demography:Demography:Demography:Demography:

The population of Bangalore has had exponentially increased from a mere

7.64 lakhs in 1950 to 65.33 lakhs in 2005. Over a span of 55 years the

population has increased by 57.69 with an average decadal growth of 10.49

lakhs. The reasons for the fastest growth in population may be attributed to

dynamic transformation of Bangalore from industrial city to educational city,

educational city to health city, from health city to IT city and so on and also the

optimal weather conditions and cosmopolitan environment including the

generosity and hospitality of the local kannadigas.

Year Bangalore Population

1950 - 2015

Percentage of Indian

urban population (%)

1950 - 2015

Percentage of

total Indian population (%)

1950 - 2015

1950 764 000 1.2 0.2

1955 947 000 1.4 0.2

1960 1 173 000 1.5 0.3

1965 1 382 000 1.5 0.3

1970 1 616 000 1.5 0.3

1975 2 111 000 1.6 0.3

1980 2 812 000 1.8 0.4

1985 3 395 000 1.8 0.4

1990 4 036 000 1.9 0.5

1995 4 745 000 1.9 0.5

2000 5 567 000 2.0 0.6

2005 6 533 000 2.1 0.6

2010 7 469 000* 2.1 0.6

2015 8 391 000* 2.1 0.7

* Projected Population Source: UN 2005

Table 6.2: Population of Bangalore from 1950 to 2015


There are 100 wards within the Bangalore Mahanagara Palike (BMP) limits.

The population statistics as per census 2001 is as given the following Table

6.3. covering 100 BMP wards and 08 CMC’s excluding the new layouts being

developed and not handed over to either BMP or CMC.

Bangalore Wards / CMC Population as per census 2001

Bangalore (M Corp.) - Ward No.1 27637





































Pattanagere (CMC+OG) 96385

Dasarahalli (CMC+OG) 293359

Yelahanka (CMC+OG) 94234

Byatarayanapura (CMC+OG) (Part) 188514

Kengeri (TMC) 42455

Krishnarajapura (CMC) 186210

Mahadevapura (CMC+OG) 154223

Bommanahalli (CMC+OG) (Part) 224980

TOTAL 5581686

Table 6.3: Ward / CMC- wise Population as per census 2001


• Population Distribution:Population Distribution:Population Distribution:Population Distribution:

As per census 2001, the distribution of population in northern, southern

and south-eastern wards in dark brown coloured wards (ref: Figure 6.5) is more

han 60,000. The population in western and eastern parts is relatively less.

Figure 6.5: Population distribution in BMP wards

• Location of study area:Location of study area:Location of study area:Location of study area:

The study area for the project work has been restricted to only

29 villages out of 865 villages within the Bangalore urban district

keeping in mind the constraint of manpower and time required.

These contigous29 villages are located in south-eastern part of

Bangalore city as shown in Figure 6.2. The likely landuse plan for

these parcels and the surrounding villages as depicted in Master

Plan (2005-2015) proposed by the Bangalore Development

Authority is shown as in Figure 6.3. Also the location of study area

with respect to the administrative boundary of the statutory agency

for Bangalore urban development, the Bangalore Development

Authority is shown in Figure 6.3.


Chapter 7: The Methodology and Data Base CreationChapter 7: The Methodology and Data Base CreationChapter 7: The Methodology and Data Base CreationChapter 7: The Methodology and Data Base Creation


The overall methodology adopted for site suitability analysis for urban

development is given in the Figure 7.1. Various important factors relevant for

Bangalore such as present landuse/landcover, proximity to existing road

network, proximity to the built-up, soil depth, soil texture, ground water

prospects and land value are chosen so as to keep the model compact and

effective. These factor vector layers are weighted according to the weights

derived by the Saaty’s AHP method by creating a separate field in each layer.

Each class or category in each layer is given rank and these ranks are stored in

the database as separate field. And also the product of weight and rank are

computed and stored in another filed. Finally all the seven factor vector layers

are combined together by applying GIS UNION operation on two layers at a

time. Thus totally six UNION operations are carried out to seven factor vector

layers to get the final weighted combined factor vector layer. Similarly, three

constraints such as built-up area, water bodies and master plan are considered.

These three constraint vector layers are combined together by applying Boolean

AND operation on two layers at a time. Finally, the constraint combined vector

map is obtained by applying two Boolean AND operations on constraint layers.

Ultimately the INTERSECTION of the factor map and the constraint map give the

site suitability for urban development. The final parcel level urban land

suitability map is obtained by INTERSECTION of urban land suitability map and

the cadastral layer being created by onscreen digitization on the mosaiced

cadastral maps being geo-referenced over the merged Quickbird satellite

image. The vector layers are used to achieve maximum accuracy keeping in

mind the parcel level land suitability analysis.


• MethodologyMethodologyMethodologyMethodology::::

Figure 7.1: Methodology of Creating Urban Suitability Map at Parcel Level

LANDUSE MAP

MSS QUICK-BIRD SUB-IMAGE

PAN QUICK-BIRD SUB-IMAGE

MERGING MSS AND PAN IMAGES

PAN QUICK-BIRD IMAGE

ROAD NETWORK

MAP

BUILT-UP AREA MAP

LAND VALUE MAP

PROXIMITY TO ROAD MAP

PROXIMITY TO BUILT-UP

AREA MAP

SOIL DEPTH MAP

SOIL TEXTURE MAP

GROUND WATER

PROSPECTS

MSS QUICK- BIRD IMAGE

UNION OF WEIGHTED FACTOR MAPS

GEO-REFERENCING OF CADASTRAL MAPS WITH MERGED IMAGE

SCANNING CADASTRAL MAPS

INTERSECTION OF BOOLEAN CONSTRAINT MAPS

WATER BODIES MAP

MASTER PLAN

CREATION OF CADASTRAL BASE MAP

INTERSECTION TO GET CONTINUOUS URBAN SUITABILITY MAP

INTERSECTION

URBAN SUITABILITY MAP AT PARCEL LEVEL AND RECLASSIFICATION


• Factors:Factors:Factors:Factors:

They are the additive layers which are unioned to arrive at most favorable

areas for urban development in terms of higher attribute values.

---- Existing landuse landcover classifica Existing landuse landcover classifica Existing landuse landcover classifica Existing landuse landcover classification:tion:tion:tion:

---- Introduction: Introduction: Introduction: Introduction:

Information on existing landuse/cover (refer to Figure 7.3)

especially the extent and spatial distribution is a prerequisite for the

urban suitability studies or urban planning. Up-to-date landuse /

landcover map is required for monitoring the urban and rural

environment. Landuse refers to man’s activities and the various use which

they are put whereas landcover refers to the natural vegetation, water

bodies, rock/soil, artificial cover and others resulting due to land

transformations. Landuse is generally based on landcover. Landuse

encompasses several different aspects of man’s relationship to the

environment e.g., activity, ownership and land quality. Landcover is

represented by natural and artificial compositions covering the earth’s

surface at certain location e.g., building as cover for residential use,

green plants as a cover for agricultural crop.

---- Classification system adopted Classification system adopted Classification system adopted Classification system adopted

The classification system adopted in this study is developed

keeping in mind the following objectives.

- To demonstrate the technique used and its advantage in

obtaining information for urban area

-Utilization of VHR satellite data (refer to Figure 7.2) for

identification, mapping and classification of various landuses

in detail.

---- Built Built Built Built----up land:up land:up land:up land:

The physical extent of total built-up or developed land is about

1331.031331.031331.031331.03 hectares in the study area.

---- Agricultural land: Agricultural land: Agricultural land: Agricultural land:

Agriculture is the predominant landuse outside the urban area. The

area covered under agricultural landuse is about 2447.422447.422447.422447.42 hectares. The


agricultural land includes crop land, fallow land, water logged area. The

types of crops grown in the study area are mostly ragi, rice, fruits and

vegetables.

---- Plantations: Plantations: Plantations: Plantations:

The area under plantations is about 1510.761510.761510.761510.76 hectares. Mostly

plantations include eucalyptus, coconut and areca nut plantations.

---- Waste land: Waste land: Waste land: Waste land:

The area under vacant land is about hectares 420.35420.35420.35420.35 hectares. It

covers non-irrigable land, open spaces, scrubs.

---- Water bodies: Water bodies: Water bodies: Water bodies:

The total area under water bodies is about 429.32429.32429.32429.32 hectares. The

prime water body is the bellandur amanikere with a series of

interconnected tanks. Most of these tanks feed for paddy crop.

---- Transportation network: Transportation network: Transportation network: Transportation network:

The road network is one of the important parameter in identifying

the areas for urban development as it provides linkages between the

settlements. The major and minor pucca roads have considered for

digitization.

Sl NoSl NoSl NoSl No Landuse TypeLanduse TypeLanduse TypeLanduse Type Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Built-Up 1331

2 Agriculture 2447

3 Plantation 1511

4 Transport 30

5 Waste Land 420

6 Water Bodies 429

TotalTotalTotalTotal 6666169169169169 Table 7.1: Areas of Landuse/Landcover Classes in the study areaTable 7.1: Areas of Landuse/Landcover Classes in the study areaTable 7.1: Areas of Landuse/Landcover Classes in the study areaTable 7.1: Areas of Landuse/Landcover Classes in the study area

---- Ground Water Prospects: Ground Water Prospects: Ground Water Prospects: Ground Water Prospects:

Geomorphic features control the distribution of runoff and ground water

recharge. The structure of geological formation controls the occurrence,

movement and quality of ground water. Primary importance is the occurrence


and distribution of aquifers and their relationship with relatively associated

impermeable beds, which act as non-leaky to leaky confining layers and

barriers to ground water movement. The geological structures have marked

influence on the lateral and vertical extent of aquifers.

Existing hydro-geo-morphological map (scale: 1:50000) being prepared

by KSRSAC, Bangalore on the basis of geology, geomorphology and structure

has been used for extracting ground water prospects map (refer to Figure 7.4).

Hydro-morphologically, the study area has been divided into five zones

depending upon the ground water prospects in different geomorphic units.

These five zones are i) Very good, ii) Good, iii) Moderate, iv) Poor, v) nil

Alluvial plains are very good potential zones. Ground water occurs under

water table condition between 8 M to 20 M but deeper level due to presence of

loose fragmented material supports high value of permeability and more

chance of downward percolation of surface water. In valley fill and in-filled

valley area, the ground water prospects are mainly dependent on the thickness

of the weathered and colluvial soil. Therefore these areas have good ground

water prospects.

Sl NoSl NoSl NoSl No Ground Water ProspGround Water ProspGround Water ProspGround Water Prospectsectsectsects Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Good 1473

2 Moderate 4381

3 Moderate to Poor 21

4 Reservoir 295

TotalTotalTotalTotal 6169616961696169 Table 7.2: Areas of Ground Water prospects Classes in the study areaTable 7.2: Areas of Ground Water prospects Classes in the study areaTable 7.2: Areas of Ground Water prospects Classes in the study areaTable 7.2: Areas of Ground Water prospects Classes in the study area

---- Soil Information: Soil Information: Soil Information: Soil Information:

The soil map prepared by KSRSAC, Bangalore in the scale of 1:50000, is

being used for extracting the soil depth and soil texture layers. Each polygon

has the information related to the association of soil series. From these soil

types, soil depth and soil texture have been separated out and the maps have

been prepared separately. The extent and spatial distribution of soil depth and

soil texture play an important role in urban suitability analysis.


---- Soil depth: Soil depth: Soil depth: Soil depth:

As per IS Building code the minimum depth of foundation within soil

should not be less than 45 cm for buildings. Therefore the soil depths are

categorized into three classes (refer to Figure 7.5). They are i) deep (depth >90

cm), ii) moderately deep (depth >45 cm to depth < 90 cm), iii) shallow (depth <

45 cm).

SlSlSlSl No No No No Soil DepthSoil DepthSoil DepthSoil Depth Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Deep 3274

2 Moderately Deep 1048

3 Shallow 1848

TotalTotalTotalTotal 6169616961696169

Table 7.3: Areas of Soil Depth Classes in the study areaTable 7.3: Areas of Soil Depth Classes in the study areaTable 7.3: Areas of Soil Depth Classes in the study areaTable 7.3: Areas of Soil Depth Classes in the study area

---- Soil texture Soil texture Soil texture Soil texture

It is an important aspect with respect to the stability of foundations.

Clayey soils increase in volume due to absorption of water and may result in

differential settlement of foundations resulting in their failure. Therefore, highly

clayey soils are less suitable and sandy soils are more suitable for foundations.

According to the texture of soils five categories are made as follows (refer to

Figure 7.6). i) Settlement/Tank/Gullied land (not suitable), ii) Clay /Clay loam

(less suitable), iii) Sandy clay (moderately suitable), iv) Gravelly clay (suitable)

and v) Sandy loam/loamy sand (highly suitable).

SlSlSlSl No No No No Soil TextureSoil TextureSoil TextureSoil Texture Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Clay 2203

2 Clay loam 21

3 Gravelly clay 1026

4 Gullied land 3

5 Loamy sand 14

6 Sandy clay 1155

7 Sandy loam 723

8 Settlement 729

9 Tank 294


Table 7.4: Areas of Soil Texture Classes in the study areaTable 7.4: Areas of Soil Texture Classes in the study areaTable 7.4: Areas of Soil Texture Classes in the study areaTable 7.4: Areas of Soil Texture Classes in the study area


---- Land value: Land value: Land value: Land value:

It is one of the most important criteria for urban suitability analysis. Due

to latest happenings in Bangalore-IT sector, the land values have gone up

remarkably very high from last two years. The basic cost of land has

tremendous influence on the cost of urban development. The land values for

various have been obtained from the sub-registrar offices of Bangalore (South)

and Krishnarajapuram. Three categories have been made depending on the

range of rates per acre (refer to Figure 7.7). They are i) Very High (for land

values > Rs. 40 lakhs/acre), ii) High (for land values < Rs. 40 lakhs/acre and >

Rs.30 lakhs/acre), iii) Moderate (for land values < Rs. 30 lakhs/acre and >

Rs.20lakhs/acre) and iv) low (for land values < Rs. 20 lakhs/acre and > Rs.10

lakhs/acre), v) very low (for land values < 10 lakhs).

SlSlSlSl No No No No Land ValueLand ValueLand ValueLand Value Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Very high 194

2 High 1873

3 Moderate 1112

4 Low 2142

5 Very Low 848


Table 7.5: Areas of Land Value Classes in the study areaTable 7.5: Areas of Land Value Classes in the study areaTable 7.5: Areas of Land Value Classes in the study areaTable 7.5: Areas of Land Value Classes in the study area

---- Proximity to Road network: Proximity to Road network: Proximity to Road network: Proximity to Road network:

The road network is one of the important parameter in identifying the

areas for urban development as it provides linkages between the settlements.

The entire area has been classified as major road and minor road. Most of the

villages are connected by motorable metalled roads. For the purpose of urban

landuse suitability analysis, all important roads have been taken from the

transportation network map prepared using merged Quickbird satellite image

and SOI maps. Buffer zones of 500m, 1000m and 1500m on either side of

roads have been generated (refer to figure 7.8). The area lying 500 M distance

on both sides of the road network is ranked high for development and

consequently the area lying between 1000 M to 1500 M is ranked low for

development.


SlSlSlSl No No No No Road BufferRoad BufferRoad BufferRoad Buffer Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 500 M 5172

2 1000 M 996

3 1500 M 0

TotalTotalTotalTotal 6169616961696169 Table 7.6: Areas of Proximity to Road Classes in the study areaTable 7.6: Areas of Proximity to Road Classes in the study areaTable 7.6: Areas of Proximity to Road Classes in the study areaTable 7.6: Areas of Proximity to Road Classes in the study area

---- Proximity to Built Proximity to Built Proximity to Built Proximity to Built----up area:up area:up area:up area:

It is considered due to the reason that the cost of future urban

development depends on proximity to the built-up area. A buffer of 500 M is

normally considered in urban development studies. Therefore, areas within 500

M buffer radius are more suitable for urban development than areas outside

500 M buffer limit (refer to Figure 7.9).

SlSlSlSl No No No No Built BufferBuilt BufferBuilt BufferBuilt Buffer Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 <500M 5419

2 >500M 750


Table 7.7: Areas of Proximity to BuiltTable 7.7: Areas of Proximity to BuiltTable 7.7: Areas of Proximity to BuiltTable 7.7: Areas of Proximity to Built----up Classes in the study areaup Classes in the study areaup Classes in the study areaup Classes in the study area

• Constraints:Constraints:Constraints:Constraints:

These are the layers which restrict the growth or urban development. Master

plan, water body and urban area are identified as important constraints for

urban development.

---- Master plan: Master plan: Master plan: Master plan:

Bangalore Development Authority (BDA) is an agency constituted in 1976

for regulating and monitoring the orderly growth of the Bangalore city. Recently

BDA released the Draft Master Plan - 2015 covering a Local Planning Area (LPA)

of 1306 sqkm. The BDA planning area consists of 387 villages, 7 City Municipal

Councils (CMC) and 1 Town Municipal Council (TMC). The areas classified under

‘Constraint Areas Category’ (CAC) within the LPA are restricted from

development. These Non-buildable areas have been extracted from the

Bangalore Master Plan (2005 – 2015) as shown in Figure 7.10. These areas are

mainly the low lying or valley areas prone to submerging during heavy rains.

The total non-buildable area in the study area is 89.24 hectares.


SlSlSlSl No No No No Master Plan ConstraintMaster Plan ConstraintMaster Plan ConstraintMaster Plan Constraint Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Buildable 4543

2 Non-Buildable 1626

TotalTotalTotalTotal 6169616961696169 Table 7.8: Areas of Constraint (Master Plan) Classes in the study areaTable 7.8: Areas of Constraint (Master Plan) Classes in the study areaTable 7.8: Areas of Constraint (Master Plan) Classes in the study areaTable 7.8: Areas of Constraint (Master Plan) Classes in the study area

---- Built Built Built Built----up area:up area:up area:up area:

Obviously urban development can not take place in the developed area

(refer to Figure 7.11). Total built-up area is about 1331.03 hectares.

SlSlSlSl No No No No ClassClassClassClass Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Built Up 1355

2 Others 4814

TotalTotalTotalTotal 6169616961696169 Table 7.9: Areas of Constraint (BuiltTable 7.9: Areas of Constraint (BuiltTable 7.9: Areas of Constraint (BuiltTable 7.9: Areas of Constraint (Built----up) Classes in the study areaup) Classes in the study areaup) Classes in the study areaup) Classes in the study area

---- Water body: Water body: Water body: Water body:

Conservation of Water bodies has been the motto of the planning

authority due to various reasons such as ecology, recreation and providing lung

spaces for healthy society. The Figure 7.12 shows the distribution of water

bodies in the study area. The total area covered by Water bodies is 492.32

hectares.

SlSlSlSl No No No No CLASSCLASSCLASSCLASS Area(Ha)Area(Ha)Area(Ha)Area(Ha)

1 Others 5737

2 Water body 432

TotalTotalTotalTotal 6169616961696169 Table 7.10:Table 7.10:Table 7.10:Table 7.10: Area Area Area Area of of of of Water BodyWater BodyWater BodyWater Body Constraint Classes Constraint Classes Constraint Classes Constraint Classes

- Cadastral Layer:Cadastral Layer:Cadastral Layer:Cadastral Layer:

The cadastral maps which are procured from the Department of

Survey and Land Records by the KSRSAC are being used. The

methodology of creation of cadastral layer is shown in Figure 7.13. These

maps are prepared in the scales of 1:7920 and 1:4000. The hardcopies

are scanned in 1:1 ratio at a resolution of 150 DPI to 300 DPI depending

on the quality of cadastral map. These scanned cadastral maps are geo-

referenced with the merged Quick bird (0.6m PAN and 2.4m MSS) satellite

image in ERDAS IMAGINE 8.5. The geo-referenced cadastral layer is then

kept over the Quickbird image (refer to Figure 7.14) in pseudo colour


PROCUREMENT OF CADASTRAL MAPS

(1:7,920 & 1:4,000 SCALE) (Source: Dept. of Survey Settlement & Land Records)

SCANNING IN 1:1 SCALE

(150 DPI if it is dark & good condition)

(200-300 DPI if it light & bad condition)

GEO-REFERENCING WITH QUICK BIRD

VHR (PAN+MSS) MERGED IMAGE

ONSCREEN DIGITIZATION OVER PSUEDO

CADASTRAL LAYER BEING OVERLAID ON

QUICK BIRD MERGED IMAGE

VECTOR CADASTRAL LAYER

mode and digitized manually. The final cadastral layer of all the 29

villages is prepared as shown in the Figure 7.15. Also an overlay of the

cadastral layer is shown in Figure 7.16.

**** Methodology:Methodology:Methodology:Methodology:

Figure 7.13: Methodology for Generation oFigure 7.13: Methodology for Generation oFigure 7.13: Methodology for Generation oFigure 7.13: Methodology for Generation of Cadastral Vector Layerf Cadastral Vector Layerf Cadastral Vector Layerf Cadastral Vector Layer


CHAPTER 8: DATA ANALYSISCHAPTER 8: DATA ANALYSISCHAPTER 8: DATA ANALYSISCHAPTER 8: DATA ANALYSIS


Identification of suitable land for urban development is of the critical

issues of planning. The suitability of the land for urban development is not only

based on a set of physical parameters but also very much on the economic

factors. The cumulative effect of these factors determine the degree of

suitability and also helps in further categorizing of the land into different

orders of development. The assessment of the physical parameters of the land

is possible by analyzing the land use, soil parameters, terrain parameters,

geology, flood hazard, physiography, and distance from road, distance from the

existing development etc. and which is much amenable to GIS analysis. As

against this, the economic pressures on urban land are very much difficult to be

specified and used for analysis. However the assessment of physical parameters

gives an identification of the limitations of the land for urban development. The

concept of limitation is derived from the quality of land. For example, if the

slope is high the limitation it offers is more than a land which has gentle slopes

or a flat terrain. Practically, this would mean that the development of the high

slope land would require considerable inputs (finance, manpower, materials,

time etc.) and thus may be less suitable as against the flat land where the

inputs required are considerably less. The constraints with respect to the

terrain characteristics (landform) and their urban suitability are to be assessed.

In this particular study seven such important parameters which are most

relevant for the area under study and accepted by urban planners, are

considered. The parameters are existing lanuse/landcover, ground water

prospects, soil depth, soil texture, land value, proximity to built-up and

proximity to road. Also three constraints such as built-up area, water bodies

and master plan are also considered in grading the suitable parcels for urban

development. These seven factors are analyzed in GIS environment using a

Weighted Index Model.

• Weighted Index Model:Weighted Index Model:Weighted Index Model:Weighted Index Model:

One of the classic problems in decision theory or multi-parameter

analysis is the determination of the relative importance or weights of each

parameter with respect to the other. This is a problem which requires human

judgment supplemented by mathematical tools. As all factors of the land can


not be weighted equal for the suitability assessment, it is essential that a

weighted method needs to be employed where the relative importance of the

parameters defines the weightage. A number of methods are available to deal

with such problems.

Saaty’s Analytic hierarchy process is a most widely accepted method for

scaling the weights of parameter by constructing a pairwise comparison matrix

of parameters whose entries indicate the strength with which one element

dominates over another. The pairwise comparison matrix of parameters results

into importance matrix which is based on a scale of important intensities and is

generated by a group of experts. The Saaty’s scale of importance is presented

in Table 8.1.

The importance matrices for seven factors were generated based of

Saaty’s guidelines mentioned in the above Table 8.1. By varying the importance

for a set of two factors at a time, three models have been generated.

Correspondingly three performance matrices for the three models have been

generated as given in Table 8.2 to Table 8.5. The fourth model has been

generated by assigning equal importance and weightage for all seven factors.

Thus a comparative study can be done among the first three models and also

against the fourth model so that sensitive factors can be identified.

Assigned Assigned Assigned Assigned

ValueValueValueValue DefinitionDefinitionDefinitionDefinition ExplanationExplanationExplanationExplanation

1 Parameters of equal importance Two parameters contribute equally to the

objective

3

Parameter ‘j’ is of weak

importance compared to

parameter ‘i’

Experience and judgement slightly favour

to parameter ‘i’ over ‘j’

5 Essential or strong importance

of parameter ‘i’ compared to ‘j’

Experience and judgement strongly favour

to parameter ‘i’ over ‘j’

7 Demonstrated importance

Criteria ‘i’ is very strongly favoured over ‘j’

and its dominance is demonstrated in

practice

9 Absolute importance

The evidence favouring parameters ‘i’ over

‘j’ to the highest possible order of

affirmation

2,4

6,8

Intermediate values between two

adjacent judgement

Judgement is not precise enough to assign

values of 1,3,5,7 and 9

Table 8.Table 8.Table 8.Table 8.1111: Saaty’s scale of importance: Saaty’s scale of importance: Saaty’s scale of importance: Saaty’s scale of importance


lu gwp sd st lv bb t

lu 1.000 7.000 5.000 5.000 1.000 2.000 3.000

gwp 0.143 1.000 0.500 0.500 0.200 0.333 0.333

sd 0.200 2.000 1.000 2.000 0.200 0.333 0.500

st 0.200 2.000 0.500 1.000 0.500 0.500 0.200

lv 1.000 5.000 5.000 2.000 1.000 1.000 3.000

bb 0.500 3.000 3.003 2.000 1.000 1.000 2.000

t 0.333 3.000 2.000 5.000 0.333 0.500 1.000

Table Table Table Table 8.28.28.28.2: Imp: Imp: Imp: Importance matrix for Modelortance matrix for Modelortance matrix for Modelortance matrix for Model----1111


lu 1.000 5.000 3.000 3.000 1.000 0.500 0.500

gwp 0.200 1.000 0.500 0.500 0.200 0.333 0.333

sd 0.333 2.000 1.000 2.000 0.200 0.333 0.500

st 0.333 2.000 0.500 1.000 0.500 0.500 0.200

lv 1.000 5.000 5.000 2.000 1.000 0.500 0.500

bb 2.000 3.000 3.000 2.000 2.000 1.000 1.000

t 2.000 3.000 2.000 5.000 2.000 1.000 1.000

Table 8.Table 8.Table 8.Table 8.3333: Importance matrix for Model: Importance matrix for Model: Importance matrix for Model: Importance matrix for Model----2222


lu 1.000 2.000 0.500 0.500 1.000 1.000 1.000

gwp 0.500 1.000 0.500 0.500 0.500 0.500 0.500

sd 2.000 2.000 1.000 1.000 2.000 2.000 2.000

st 2.000 2.000 1.000 1.000 0.500 2.000 2.000

lv 1.000 2.000 0.500 2.000 1.000 2.000 2.000

bb 1.000 2.000 0.500 0.500 0.500 1.000 1.000

t 1.000 2.000 0.500 0.500 0.500 1.000 1.000




lu 1.000 1.000 1.000 1.000 1.000 1.000 1.000

gwp 1.000 1.000 1.000 1.000 1.000 1.000 1.000

sd 1.000 1.000 1.000 1.000 1.000 1.000 1.000

st 1.000 1.000 1.000 1.000 1.000 1.000 1.000

lv 1.000 1.000 1.000 1.000 1.000 1.000 1.000

bb 1.000 1.000 1.000 1.000 1.000 1.000 1.000

t 1.000 1.000 1.000 1.000 1.000 1.000 1.000


IndexIndexIndexIndex::::

lu lu lu lu → Landuse / Landcover→ Landuse / Landcover→ Landuse / Landcover→ Landuse / Landcover

sd sd sd sd → Soil Depth→ Soil Depth→ Soil Depth→ Soil Depth

gwpgwpgwpgwp → Ground Water Prospects→ Ground Water Prospects→ Ground Water Prospects→ Ground Water Prospects

st st st st → Soil Texture→ Soil Texture→ Soil Texture→ Soil Texture

lv lv lv lv → Land Value→ Land Value→ Land Value→ Land Value

bb bb bb bb → Proximity to Built→ Proximity to Built→ Proximity to Built→ Proximity to Built----up Areaup Areaup Areaup Area

t t t t → Proximity to Road Network→ Proximity to Road Network→ Proximity to Road Network→ Proximity to Road Network

• Derivation Of The Weightages: Derivation Of The Weightages: Derivation Of The Weightages: Derivation Of The Weightages:

Saaty’s Analytical Hierarchy process is a most widely accepted method for

scaling the weights of parameters by constructing a pair-wise comparison

matrix of factors whose entries indicate the strength with which one element

dominates over another parameter. The importance matrix can be analyzed by

various methods like “Eigen Vector” method as proposed by Saaty. Table 8.6

shows the weightages for various factors generated for all the four models.

---- Eigen Vector Method: Eigen Vector Method: Eigen Vector Method: Eigen Vector Method:

In this method the basic input is the pair wise comparison matrix of ‘n’

parameters constructed based on the Saaty’s scaling ratios, which could be of

the order (n x n), and is in the form of:

A = [aij], where i,j = 1,2,3,…….,n (1)

Where aij = wi / wj for all i and j

The matrix A has generally the property of reciprocality and also the

consistency. This is mathematically,


aij = 1 / aji and (2)

aij = aik / ajk for all i, j and k. (3)

Thus, multiplying equation(1) with the weighting vector - W of (n x 1) size

yields

( A – nI) W = 0 (4)

where I is an identity matrix of (n x n). According to the matrix theory if the

comparison matrix A has the property of consistency, the system of equations

has a trivial solution. The matrix A is, however, a judgement matrix and it may

not be possible to determine the elements of A accuracy to satisfy the property

of consistency. Therefore, it is estimated by a set of linear homogeneous

equations:

A* W* = max W* (5)

Where A* is the estimate of A and W* is the corresponding priority vector and

is the largest Eigen value for the matrix A. the equation(5) yields the weightages

W which are normalized to unity for further purposes.

Model-1 Model-2 Model-3 Model-4

Landuse / Landcover 0.295 0.167 0.120 0.143

Ground Water Prospects 0.040 0.048 0.074 0.143

Soil Depth 0.068 0.082 0.219 0.143

Soil Texture 0.072 0.087 0.219 0.143

Land Value 0.233 0.176 0.186 0.143

Proximity to Built-up Area 0.169 0.220 0.109 0.143

Proximity to Road Network 0.128 0.238 0.109 0.143

Table 8.6 : Weightages derived from the Saaty’s AHP method:Table 8.6 : Weightages derived from the Saaty’s AHP method:Table 8.6 : Weightages derived from the Saaty’s AHP method:Table 8.6 : Weightages derived from the Saaty’s AHP method:

Note:

1. In Model-1 more importance has been given to Landuse / Landcover and

Land Value

2. In Model-2 more importance has been given to Proximities to Built-up

Area and Road Network

3. In Model-3 more importance has been given to Soil Depth and Soil

Texture

4. In Model-4 equal importance has been given to all the factors


• Ranking of thematic layers:Ranking of thematic layers:Ranking of thematic layers:Ranking of thematic layers:

The different features in each thematic layer are ranked keeping in mind the

constraints for development as given in the Table 5.1. The most favorable

feature in each thematic or factor layer is ranked highest with 4 and the least

favorable feature is assigned with 1. The intermediate features are ranked

suitably with values of 2 and 3 appropriately. The following Table 8.7 shows the

ranking system adopted in the present study.

Range values definingRange values definingRange values definingRange values defining

RRRRankankankank----4444 RankRankRankRank----3333 RankRankRankRank----2222 RankRankRankRank----1111 Sl Sl Sl Sl

NoNoNoNo FactorsFactorsFactorsFactors

Min Min Min Min Limitation Limitation Limitation Limitation Max Max Max Max

1 Landuse

/landcover Wasteland Agriculture Plantation Forest

2 Ground Water

Prospects Poor

Moderate To

Poor Moderate Good

3 Soil Depth Very Deep Deep Moderate Shallow

Soil Texture Sandy Loamy Clayey Rocky/Stony

5 Land Value Low Moderate High Very High

6 Proximity to

Built-up <500m 500 – 1000m 1000–2500m > 2500 M

7 Proximity to

Road <500 M 500 – 1000 M 1000–2500m > 2500 M

Table 8.7 : RanTable 8.7 : RanTable 8.7 : RanTable 8.7 : Rankkkking System for the Categories oing System for the Categories oing System for the Categories oing System for the Categories of Factors/Parametersf Factors/Parametersf Factors/Parametersf Factors/Parameters

• Integration of factors and constraintsIntegration of factors and constraintsIntegration of factors and constraintsIntegration of factors and constraints::::

A composite map has been generated by integrating all seven thematic

coverages. Overlaying of all the seven thematic coverages into a single

composite coverage resulted in a number of sliver polygons. These sliver

polygons have been dissolved into the adjoining polygons based upon the

extent and spatial distribution of these polygons and minimum mapping unit.

The composite coverage has composite units and each unit has the

characteristics of all the parameters considered for the suitability analysis. All

the units have been assigned weightages by creating separate fields in the

coverage. The ranks also have been assigned weightages by creating separate

fields in the coverage. The ranks also have been assigned to all the units by

creating a separate field. Thus, each unit in the composite map is associated

with both weightage and rank.


A composite Suitability Index (CSI) has been calculated for each

composite unit by multiplying weightage with rank of each parameter and

summing up the values of all the seven parameters. Finally the factor composite

layer is intersected by GIS INTERSECTION operation with the single composite

constraint layer obtained by the integration of three constraint layers by again

GIS INTERSECTION operation. Categorization of the CSI is achieved by ranging

the CSI into four classes where each range indicates the amount of limitation

acceptable for each class. Minimum, maximum, mean and standard deviation of

CSI have been used for categorization.

Class Class Class Class –––– 1 1 1 1 : Maximum : Maximum : Maximum : Maximum > CSI > > CSI > > CSI > > CSI > (Mean + 1 Std. Dev.)(Mean + 1 Std. Dev.)(Mean + 1 Std. Dev.)(Mean + 1 Std. Dev.)

Class Class Class Class –––– 2 2 2 2 : (Mean + 1 Std. Dev.) : (Mean + 1 Std. Dev.) : (Mean + 1 Std. Dev.) : (Mean + 1 Std. Dev.) > CSI > Mean > CSI > Mean > CSI > Mean > CSI > Mean

Class Class Class Class –––– 3 3 3 3 : Mean : Mean : Mean : Mean > CSI > (Mean > CSI > (Mean > CSI > (Mean > CSI > (Mean ---- 1 Std. Dev.) 1 Std. Dev.) 1 Std. Dev.) 1 Std. Dev.)

ClClClClass ass ass ass –––– 4 4 4 4 : (Mean : (Mean : (Mean : (Mean ---- 1 Std. Dev.) 1 Std. Dev.) 1 Std. Dev.) 1 Std. Dev.) > CSI > Minimum(non > CSI > Minimum(non > CSI > Minimum(non > CSI > Minimum(non----zero)zero)zero)zero)

Class Class Class Class –––– 5 5 5 5 : Zero: Zero: Zero: Zero

• Results:Results:Results:Results:

---- Overall Comparative Urban Land Suitability Analysis:Overall Comparative Urban Land Suitability Analysis:Overall Comparative Urban Land Suitability Analysis:Overall Comparative Urban Land Suitability Analysis:

The urban land suitability maps derived using four different models are

presented in Figure 8.6 to Figure 8.9. The area of each suitability class in

different models is shown in Table 8.8. From the Table 8.8 it is very clear that

about 50% of the available land is not suitable for urban development due to

existing built-up area, water bodies and non-buildable area as per the

proposed master plan of Bangalore city ( 2015 ). The non-buildable area

designated by Bangalore Development Authority is mainly low lying natural

stream network( i.e. earlier this network of natural streams which carried the

run-off during monsoon to a series of water tanks or reservoirs, was called as

‘raja nala’ ) based on contours. The first three urban land suitability models, i.e.

Model-1, Model-2and Model-3 may be compared with the Model-4 with equal

weights for all factors. It may be observed that

i) Highly suitable land for urban development as per model-1, model-

2 and model-3, is almost same, but is more as per model-4 with a

difference of 296 to 377 hectares ( refer to Figure 8.1)

ii) In Suitable class, the model-1 and model-2 are almost nearer in

suitable area and also modle-3 and model-4 are almost same. But

the difference in area between the above set of models is 552 to 677

Hectares, which is quite large enough.


iii) Considering only two classes Highly Suitable and suitable, the

model-1, model-2, model-3 and model-4 yield cumulative areas of

2672, 2477, 2005 and 2295 Hectares. Compared to Model-4, the

differences in areas are 378, 182 and 290 Hectares for Model-1,

Model-1 and Model-3 respectively. Thus, it may be inferred that the

factors landuse/landcover and land value are more sensitive and the

factors proximities to the road network and built-up area are least

sensitive.

Model Model Model Model ---- 1 1 1 1 Model Model Model Model ---- 2 2 2 2 Model Model Model Model ---- 3 3 3 3 Model Model Model Model ---- 4 4 4 4

Suit ClassSuit ClassSuit ClassSuit Class Area in

Hect. Percent

Area in

Hect. Percent

Area in

Hect. Percent

Area in

Hect. Percent

Not

Suitable(1) 2954.66 47.90 2954.66 47.90 2954.66 47.90 2954.66 47.90

Less

Suitable(2) 41.02 0.66 103.15 1.67 191.68 3.11 95.59 1.55

Moderately

Suitable(3) 501.20 8.13 633.89 10.28 1017.73 16.50 822.91 13.34

Suitable(4) 1829.90 29.67 1717.02 27.84 1164.76 18.88 1158.29 18.78

Highly

Suitable(5) 841.76 13.65 759.81 12.32 839.70 13.61 1137.10 18.43

Total 6168.54 100.00 6168.54 100.00 6168.54 100.00 6168.54 100.00

Table 8.8: Comparative Gross Areas of Suitability Classes of Table 8.8: Comparative Gross Areas of Suitability Classes of Table 8.8: Comparative Gross Areas of Suitability Classes of Table 8.8: Comparative Gross Areas of Suitability Classes of allallallall

ModelsModelsModelsModels

0

500

1000

1500

2000

2500

3000

3500

Not

Sui

tabl

e

Les

sS

uita

ble

Mode

rate

lyS

uita

ble

Sui

tabl

e

Hig

hly

Sui

tabl

e

Suitablity Class

Are

a in

Hec

tare

s

Model-1

Model-2

Model-3

Model-4

Figure 8.1: Graph showing the comparison of areas of Urban Land Suitability

classes of different models for the whole study area.


---- VillageVillageVillageVillage----wise Urban Land Suitability Classes:wise Urban Land Suitability Classes:wise Urban Land Suitability Classes:wise Urban Land Suitability Classes:

Table 8.9 shows village-wise comparative areas of different land

suitability classes and models. Thus land available for each village under

different suitability classes can be obtained and particular villages which are

more feasible for urban development can be assessed. It can be inferred from

the table 8.9 that villages Baligeri, DoddaKannalli, Gunjur, Hagaduru, Kacha-

Maranalli, mullur, nallur, pantur, Sorhunse, Sulakunte and Varthur are major

villages where each village can contribute more tha 100 to 500 Hectares of

‘Suitable’ and ‘Highly Suitable land’ for urban development.

Area Of Each Suitability Area Of Each Suitability Area Of Each Suitability Area Of Each Suitability

Class(Hect)Class(Hect)Class(Hect)Class(Hect) Sl Sl Sl Sl

NoNoNoNo

Village Village Village Village

NameNameNameName

Suit.Suit.Suit.Suit.

ClassClassClassClass Model1Model1Model1Model1 Model2Model2Model2Model2 Model3Model3Model3Model3 Model4Model4Model4Model4

Total Total Total Total

AreaAreaAreaArea

1 30.54 30.54 30.54 30.54

2 0.00 0.00 2.73 1.52

3 2.73 2.73 0.00 1.21

4 0.53 0.53 0.53 0.53

1 Amblipura

5 0.00 0.00 0.00 0.00

33.80

1 48.78 48.78 48.78 48.78

2 0.00 0.00 0.17 0.17

3 0.00 0.38 0.21 0.21

4 27.32 47.42 31.82 10.81

2 Baligeri

5 104.41 83.92 99.52 120.53

180.50

1 79.06 79.06 79.06 79.06

2 0.00 0.97 0.97 0.00

3 0.00 0.00 0.00 0.97

4 2.26 1.29 1.29 0.00

3 Bellandur

5 0.00 0.00 0.00 1.29

81.32

1 485.20 485.20 485.20 485.20

2 0.88 0.62 1.44 0.62

3 3.73 4.00 0.63 4.01

4 2.84 2.84 5.28 2.72 4

Bellanduru

Amanikere

5

1.51

1.51

1.51

1.51

494.06




NoNoNoNo

VillaVillaVillaVillage ge ge ge

NameNameNameName




AreaAreaAreaArea

1 47.69 47.69 47.69 47.69

2 0.02 3.39 2.11 2.11

3 27.78 18.76 3.37 7.09

4 162.02 171.51 188.17 86.37

5 Boganalli

5 12.31 8.48 8.48 106.33

249.59

1 53.64 53.64 53.64 53.64

2 0.00 1.82 1.98 1.57

3 23.58 34.05 57.48 45.49

4 68.61 56.59 9.11 21.22

6 Chikka

-Bellandur

5 29.64 29.36 53.27 53.55

175.47

1 84.48 84.48 84.48 84.48

2 1.66 1.66 13.43 6.35

3 9.68 11.87 14.92 17.42

4 17.01 14.82 0.00 4.59

7 Chinna

-ppanalli

5 0.00 0.00 0.00 0.00

112.84

1 52.55 52.55 52.56 52.55

2 10.85 11.40 26.87 10.85

3 40.62 55.90 9.60 25.62

4 45.93 30.10 60.94 45.36

8

Devar

-

Bisanahalli

5 0.00 0.00 0.00 15.57

149.95

1 107.33 107.33 107.33 107.33

2 4.90 11.09 9.69 8.37

3 117.52 114.06 37.92 45.09

4 105.83 103.09 180.74 136.97

9 Dodda

-Kannalli

5 0.00 0.00 0.00 37.82

335.58

1 230.51 230.51 230.51 230.51

2 0.00 4.35 8.92 4.75

3 53.68 77.10 274.58 182.08

4 402.36 387.70 114.03 189.17

10 Gunjur

5 99.05 88.70 160.33 181.88

788.39




NoNoNoNo


NameNameNameName




AreaAreaAreaArea

1 149.82 149.82 149.82 149.82

2 0.00 3.00 3.02 0.00

3 31.87 40.40 65.75 69.18

4 108.45 96.91 72.64 71.13

11 Hagaduru

5 1.30 1.30 0.21 1.30

291.43

1 60.28 60.28 60.27 60.28

2 0.00 0.00 0.00 0.09

3 26.80 63.50 75.94 72.11

4 77.16 61.11 83.34 86.54

12 Kacha

-Maranalli

5 95.65 75.01 40.33 40.87

259.89

1 22.57 22.57 22.57 22.57

2 0.00 0.00 8.49 0.00

3 6.80 14.79 0.00 8.50

4 45.16 37.18 43.47 11.99

13 Kada

-Bisanalli

5 0.00 0.00 0.00 31.48

74.54

1 63.25 63.25 63.26 63.25

2 0.07 2.46 0.29 0.29

3 3.00 0.61 8.38 8.38

4 5.61 5.61 0.01 0.01

14 Kaikonda

-Nahalli

5 0.00 0.00 0.00 0.00

71.93

1 32.74 32.74 32.74 32.74

2 1.21 1.21 1.30 1.21

3 0.09 0.09 0.10 0.10

4 0.34 0.34 0.24 0.33

15 Khane

Khandya

5 0.46 0.46 0.46 0.46

34.84

1 9.04 9.04 9.04 9.04

2 0.00 0.00 0.00 0.00

3 0.00 0.00 8.24 8.24

4 9.41 9.41 1.17 1.17

16 Kodathi

5 1.31 1.31 1.31 1.31

19.76




NoNoNoNo


NameNameNameName




AreaAreaAreaArea

1 206.46 206.46 206.46 206.46

2 1.64 4.63 10.26 0.80

3 17.59 23.92 18.30 23.13

4 21.03 11.69 11.69 16.31

17 Kundalalli

5 0.00 0.00 0.00 0.00

246.70

1 92.09 92.09 92.10 92.09

2 0.02 2.61 3.11 1.92

3 7.79 17.03 84.48 56.75

4 93.42 81.58 7.60 36.52

18 Mullur

5 15.46 15.46 21.49 21.50

208.78

1 279.29 279.29 279.29 279.29

2 0.00 1.20 5.71 1.19

3 8.16 7.18 6.33 11.58

4 10.93 10.73 7.07 6.33

19 Munne-

Kollala

5 23.71 23.70 23.70 23.71

322.10

1 96.94 96.94 96.94 96.94

2 1.77 0.31 1.56 0.00

3 12.39 13.85 12.59 14.15

4 82.62 82.62 82.62 82.18

20 Nalluralli

5 0.00 0.00 0.00 0.43

193.70

1 74.79 74.79 74.79 74.79

2 0.01 0.29 1.99 1.99

3 4.77 4.50 0.92 1.35

4 122.65 122.87 53.85 53.20

21 Panatur

5 105.28 105.05 175.96 176.18

307.51

1 119.48 119.48 119.48 119.48

2 0.40 0.40 6.18 0.40

3 9.71 9.71 1.71 7.50

4 6.33 6.33 8.54 8.51

22 Rama-

Gondanalli

5 0.00 0.00 0.00 0.20

136.09



ClasClasClasClass(Hect)s(Hect)s(Hect)s(Hect) Sl Sl Sl Sl

NoNoNoNo


NameNameNameName




AreaAreaAreaArea

1 19.57 19.57 19.57 19.57

2 0.00 0.00 0.00 0.00

3 0.01 5.86 10.92 10.92

4 13.01 7.15 2.10 0.54

23

R-

Narayana

-pura

5 2.22 2.22 2.22 3.77

34.80

1 103.14 103.14 103.14 103.14

2 0.00 0.00 0.00 0.00

3 0.02 2.44 31.37 13.14

4 31.35 28.93 3.16 18.23

24 Siddapura

5 3.16 3.16 0.00 3.16

137.67

1 51.99 51.99 51.99 51.99

2 0.00 0.00 0.05 0.05

3 0.00 0.71 4.62 4.16

4 21.17 27.60 36.09 32.73

25 Sorhunse

5 150.94 144.45 131.35 135.17

224.10

1 14.92 14.92 14.92 14.92

2 0.00 1.23 6.99 6.99

3 4.35 16.25 25.70 15.88

4 37.29 40.86 40.44 47.97

26 Sulakunte

5 90.26 73.57 58.78 61.06

146.82

1 86.57 86.57 86.57 86.57

2 9.55 20.85 35.52 11.98

3 38.05 34.04 11.21 34.74

4 25.30 18.01 26.18 24.26

27 Tubaralli

5 0.00 0.00 0.00 1.92

159.47

1 112.73 112.73 112.73 112.73

2 0.00 0.00 1.75 1.75

3 11.26 33.28 233.64 111.25

4 271.88 252.79 92.09 158.99

28 Varthur

5 104.80 101.87 60.46 115.95

500.67




NoNoNoNo


NameNameNameName




AreaAreaAreaArea

1 139.21 139.21 139.21 139.21

2 8.05 30.63 37.17 30.63

3 39.24 22.65 18.92 22.65

4 9.43 3.43 0.58 3.38

29 White Field

5 0.29 0.29 0.34 0.34

196.21

Table 8.9 : Village-wise Comparative Areas of Different Land Suitability Classes

and models

---- ParcelParcelParcelParcel----wise Urban Land Suitability Classes:wise Urban Land Suitability Classes:wise Urban Land Suitability Classes:wise Urban Land Suitability Classes:

The parcel level information such as its suitability class or classes,

suitability class or classes of adjoining parcels, its own area and extent of area

available in a particular continuous stretch of land etc. can be obtained by

intersecting the Urban Land Suitability coverage with the Parcel coverage

prepared over a mosaic of village or cadastral maps (refer to Figures 8.10 to

8.13).

- Gunjur Village Parcel Level Urban Land Suitability Analysis:Gunjur Village Parcel Level Urban Land Suitability Analysis:Gunjur Village Parcel Level Urban Land Suitability Analysis:Gunjur Village Parcel Level Urban Land Suitability Analysis:

In order to limit the size of the project report only one prominent village,

Gunjur village has been analyzed for various types of information. Figures 8.14

to 8.17 show the parcel level suitability maps of all the four urban land

suitability models. The graph as shown in Figure 8.2 shows the comparison of

land suitability classes of all the four models. It can be inferred that model-1

and model-2 yield more than 60% of land out of 785.6 hectares of total extent

of the village for urban development under suitable and most suitable classes.

But model-3 and model-4 give only 34% and 47% of total area of village for

urban development under same classes.

Sensitivity of a single parcel to changes in priorities among the suitability

factors or parameters as per different urban suitability models developed is

done for a single survey number 303 or parcel ID 0820020164303 with an area

of is shown in the figures 8.18 to 8.21 and also the pie charts as shown in

figures 8.3 to 8.5. Also the extent and affiliation of different fragments of

single parcel are shown in tables 8.11 to 8.14. It can be inferred from the Table

8.15 that over 82% of the parcel area is suitable for urban development as per

model-1 where more importance has been given for landuse/land value and


model-2 where more importance has been given for proximity to built-up area

and road network. But only 37% and 49% of parcel area is suitable for urban

development respectively as per model-3 where more importance has been

given for soil depth and soil texture and model-4 where all parameters are

given same importance.

Comparision of Urban Land Suitability Classes of all Models

0

100

200

300

400

500

Suitable Class

Are

a in

Hec

tare

s

MODEL-1MODEL-2MODEL-3

MODEL-4

MODEL-1 230.51 0 53.68 402.36 99.05

MODEL-2 230.51 4.35 77.1 387.7 88.7

MODEL-3 230.51 8.92 274.58 114.03 160.33

MODEL-4 230.51 4.75 182.08 189.17 181.88

Not Suitable

Less Suitable

Moderately Suitable

SuitableHighly

Suitable

Figure 8.Figure 8.Figure 8.Figure 8.2222: : : : Graph sGraph sGraph sGraph showing the comparison of areas of different Urban landhowing the comparison of areas of different Urban landhowing the comparison of areas of different Urban landhowing the comparison of areas of different Urban land

suisuisuisuitability classes of all modelstability classes of all modelstability classes of all modelstability classes of all models for Gunjur village for Gunjur village for Gunjur village for Gunjur village

%AGE OF AREAS OF URBAN LAND SUITABILITY CLASSES-MOD1&2

17%

72%

11%

NOT SUITABLE

SUIITABLE

HIGHLY SUITABLE

Figure 8.3 : Pie Chart Showing the Percentage of Areas of Different Urban Land

Suitability Classes of Parcel No 303 – Models 1&2


%AGE OF AREAS OF URBAN LAND SUITABILITY CLASSESS - MOD3

17%

46%

37%

NOT SUITABLE

MOD. SUITABLE

HIGHLY SUITABLE

Figure 8.Figure 8.Figure 8.Figure 8.4444 : Pie Chart Showing the Percentage of Areas of Different Urb : Pie Chart Showing the Percentage of Areas of Different Urb : Pie Chart Showing the Percentage of Areas of Different Urb : Pie Chart Showing the Percentage of Areas of Different Urban Land an Land an Land an Land

Suitability Classes of Parcel No 303 Suitability Classes of Parcel No 303 Suitability Classes of Parcel No 303 Suitability Classes of Parcel No 303 –––– Model Model Model Model ----3333

%AGE OF AREAS OF URBAN LAND SUITABILITY CLASSESS - MOD4

17%

33%

13%

37%

NOT SUITABLE

MOD. SUITABLE

SUITABLE

HIGHLY SUITABLE

Figure 8.5 : Pie Chart Showing the Percentage of Areas of Different Urban Land

Suitability Classes of Parcel No 303 – Model-4


VILLAGEVILLAGEVILLAGEVILLAGE

CODECODECODECODE

PARCELPARCELPARCELPARCEL

NONONONO


IDIDIDID

AREA IN AREA IN AREA IN AREA IN

HECTARESHECTARESHECTARESHECTARES LULCLULCLULCLULC CLASSCLASSCLASSCLASS CONSTCONSTCONSTCONSTRAINTRAINTRAINTRAINT

0820020164 303 0820020164303 0.034 AGRICULTURE 1 NB

0820020164 303 0820020164303 0.486 AGRICULTURE 4 B

0820020164 303 0820020164303 0.015 AGRICULTURE 1 NB

0820020164 303 0820020164303 1.259 PLANTATION 4 B

0820020164 303 0820020164303 0.016 PLANTATION 1 NB

0820020164 303 0820020164303 0.593 PLANTATION 1 NB

0820020164 303 0820020164303 0.008 AGRICULTURE 1 NB

0820020164 303 0820020164303 0.327 AGRICULTURE 5 B

0820020164 303 0820020164303 1.010 PLANTATION 4 B

0820020164 303 0820020164303 0.094 AGRICULTURE 5 B

Table 8.11 :Table 8.11 :Table 8.11 :Table 8.11 : Example showing Gunjur Village MultiExample showing Gunjur Village MultiExample showing Gunjur Village MultiExample showing Gunjur Village Multi----Suitability ClassSuitability ClassSuitability ClassSuitability Class Affiliation Affiliation Affiliation Affiliation

of Same Parcel To different classes : Model of Same Parcel To different classes : Model of Same Parcel To different classes : Model of Same Parcel To different classes : Model –––– 1 1 1 1


CODECODECODECODE


NONONONO


IDIDIDID HECTARESHECTARESHECTARESHECTARES LULCLULCLULCLULC CLASSCLASSCLASSCLASS CONSTRAINTCONSTRAINTCONSTRAINTCONSTRAINT

0820020164 303 820020164303 0.034 AGRICULTURE 1 NB

0820020164 303 820020164303 0.486 AGRICULTURE 4 B

0820020164 303 820020164303 0.015 AGRICULTURE 1 NB

0820020164 303 820020164303 1.259 PLANTATION 4 B

0820020164 303 820020164303 0.016 PLANTATION 1 NB

0820020164 303 820020164303 0.593 PLANTATION 1 NB

0820020164 303 820020164303 0.008 AGRICULTURE 1 NB

0820020164 303 820020164303 0.327 AGRICULTURE 5 B

0820020164 303 820020164303 1.010 PLANTATION 4 B

0820020164 303 820020164303 0.094 AGRICULTURE 5 B

Table 8.12 :Table 8.12 :Table 8.12 :Table 8.12 : Example showing Gunjur VillageExample showing Gunjur VillageExample showing Gunjur VillageExample showing Gunjur Village Multi Multi Multi Multi----Suitability ClassSuitability ClassSuitability ClassSuitability Class

Affiliation of Same Parcel To different classes : Model Affiliation of Same Parcel To different classes : Model Affiliation of Same Parcel To different classes : Model Affiliation of Same Parcel To different classes : Model –––– 2 2 2 2

VILLAGE VILLAGE VILLAGE VILLAGE

CODE CODE CODE CODE


NONONONO


IDIDIDID HECTARESHECTARESHECTARESHECTARES LULCLULCLULCLULC CLASSCLASSCLASSCLASS CONSTRAINTCONSTRAINTCONSTRAINTCONSTRAINT

0820020164 303 0820020164303 0.666 PLANTATION 1 NB

0820020164 303 0820020164303 1.745 AGRICULTURE 3 B

0820020164 303 0820020164303 1.431 AGRICULTURE 5 B

Table 8.13:Table 8.13:Table 8.13:Table 8.13: Example showing Gunjur Village MultiExample showing Gunjur Village MultiExample showing Gunjur Village MultiExample showing Gunjur Village Multi----Suitability ClassSuitability ClassSuitability ClassSuitability Class




CODECODECODECODE

PARCEL PARCEL PARCEL PARCEL

NONONONO


IDIDIDID HECTARESHECTARESHECTARESHECTARES LULCLULCLULCLULC CLASSCLASSCLASSCLASS CONSCONSCONSCONSTRAINTTRAINTTRAINTTRAINT

0820020164 303 0820020164303 0.666 PLANTATION 1 NB

0820020164 303 0820020164303 1.259 PLANTATION 3 B

0820020164 303 0820020164303 0.486 AGRICULTURE 4 B

0820020164 303 0820020164303 1.431 AGRICULTURE 5 B

Table 8.14:Table 8.14:Table 8.14:Table 8.14: Example showing Gunjur VillaExample showing Gunjur VillaExample showing Gunjur VillaExample showing Gunjur Village Multige Multige Multige Multi----Suitability ClassSuitability ClassSuitability ClassSuitability Class


AREAS IN HECTARES OF PARCEL 303 IN GUNJUR AREAS IN HECTARES OF PARCEL 303 IN GUNJUR AREAS IN HECTARES OF PARCEL 303 IN GUNJUR AREAS IN HECTARES OF PARCEL 303 IN GUNJUR


SUITABILITY CLASS MODEL-1 MODEL-2 MODEL-3 MODEL-4

NOT SUITABLE 0.666 0.666 0.666 0.666

LESS SUITABLE 0.000 0.000 0.000 0.000

MOD. SUITABLE 0.000 0.000 1.745 1.259

SUITABLE 2.755 2.755 0.000 0.486

HIGHLY SUITABLE 0.421 0.421 1.431 1.431

TOTAL 3.842 3.842 3.842 3.842

Table 8.Table 8.Table 8.Table 8.11115: Showing Comparative Areas in Hectares of all the four Models 5: Showing Comparative Areas in Hectares of all the four Models 5: Showing Comparative Areas in Hectares of all the four Models 5: Showing Comparative Areas in Hectares of all the four Models

pertaining to Parcel No. pertaining to Parcel No. pertaining to Parcel No. pertaining to Parcel No. 303 of Gunjur Village303 of Gunjur Village303 of Gunjur Village303 of Gunjur Village


Chapter 9: Summary and ConclusionsChapter 9: Summary and ConclusionsChapter 9: Summary and ConclusionsChapter 9: Summary and Conclusions

The present study indicates the uses of Remote Sensing and Geographic

Information System for spatial planning. The study also demonstrates the

potential of very high spatial resolution and multi-spectral data from Quick Bird

satellite for effectively mapping the landuse/landcover details rapidly. GIS

technology has provided a wide range of very easy to use analysis and

visualization tools.

The case study addresses a typical problem faced by the local

government urban planning authorities, namely identification of lands suitable

for the urban development at cadastral level using the remote sensing and GIS

as powerful tools of data generation, analysis and map generation. The area of

this study was Bangalore, the state capital of southern state, Karnataka.

The present study area is a cosmopolitan and complex heterogeneous

city with varied types of human races from different parts of India. The urban

land suitability analysis does not demand micro level classification and

therefore, a broader classification of landuse and landcover has been adopted.

It was possible to create a cadastral layer or parcel boundary layer because of

very high spatial resolution satellite image.

The present case study has been compartmented into seven chapters.

In the first chapter: Introduction, more focus has been laid on the

problem definition, objectives, data sources, the study area and software used.

In the second chapter, the cadastral maps, the purpose, history, uses,

advantages and disadvantages of cadastral maps have been dealt with.

In the third chapter : Introduction to RS and GIS, the science and

technology and remote sensing and GIS, their purpose, applications with special

reference to urban planning, various Indian and other operational satellites with

technical specifications are highlighted.

In the fourth chapter: Multi-Criteria Decision Analysis (MCDA), elements

of MCDA, the Saaty’s AHP (Analytical Hierarchy Process) are briefly overviewed.

In the fifth chapter: the landuse / land suitability evaluation guide lines

are briefly discussed.

In the sixth chapter : Study area – Bangalore , history, transformation of

Bangalore into various functional phases such as administrative, industrial,

commercial, educational, Silicon valley of India etc. in due course and

tremendous growth due to migration of both urban and rural masses from the


rest of the state as well as the whole country. Also, location, rainfall,

demographics, soil information etc. are dealt with briefly. Migrants from all over

the country are attracted towards Bangalore due to well behaved and

considerate local kannadigas, opportunities in public and private sector,

optimal climate with moderate temperature. Thus Bangalore has rightly earned

the tag of ‘Garden City’ and ‘Naturally Air-Conditioned City’.

It can be inferred from the Bangalore City Corporation (BCC) or Bangalore

Mahanagara Palike (BMP) population density map that the population density is

more towards south, south-west and north of Bangalore city. The Bangalore

Development Authority (BDA) is the authorized local authority to take care of

the planned development of the Bangalore city. Therefore, BDA prepares the

master plan or development plan every 10 years.

In the seventh chapter: Methodology and database creation, four models

have been created by giving more importance to two factors at a time and also

by giving equal importance to all factors. The primary concern in selection of

factors and constraints was keeping in mind the geographical location and the

trends in Bangalore urban growth. The south east part of Bangalore was chosen

due to less growth and probable demand for development due its affinity to the

IT-corridor. Bangalore is located almost on a plain ground without many

undulations. Therefore, more emphasis is given for factors such as

landuse/landcover, land value, proximity to road network, proximity to urban

areas, ground water prospects, soil depth and soil texture. Also, three basic

constraints, the water bodies, urban area and master plan of Bangalore

Development Authority (BDA) are considered to mask the non-buildable areas

in the study area. BDA has identified in its master plan, the buildable and non-

buildable areas by using DEM generated by making use of contours.

It can be finally concluded that


- Present study demonstrates that Satellite Remote Sensing is a valuable tool

to generate information about Urban Area Mapping even at Parcel Level. Quick

Bird (PAN-0.6m+MSS-2.4m) Merged image was good enough to generate

landuse/landcover in detail up to level-4

- Very high resolution satellite data can be used generate a moasic of village

maps or cadastral layer with edge matching of geo-referenced village maps

- GIS is a powerful tool which can be efficiently used for planning of urban

development. It has been effectively used for Multi-Criteria evaluation of urban

land suitability analysis using Saaty’s AHP method. Comparison of different

models has been done to evaluate the sensitivity of the factors not only at

village level but also at parcel level.

- By integrating RS and GIS techniques, it was possible to scientifically

identify the Lands Suitable for Urban Development.


APPENDIX APPENDIX APPENDIX APPENDIX –––– A: A: A: A:

Table 8.10:Table 8.10:Table 8.10:Table 8.10: Example Example Example Example SSSShowing Gunjur Village Pahowing Gunjur Village Pahowing Gunjur Village Pahowing Gunjur Village Parcelrcelrcelrcel----wise wise wise wise

Suitability Class Assignment: Model Suitability Class Assignment: Model Suitability Class Assignment: Model Suitability Class Assignment: Model –––– 4 4 4 4

SLNOSLNOSLNOSLNO VILLAGEVILLAGEVILLAGEVILLAGE CODECODECODECODE PARCELPARCELPARCELPARCEL NONONONO PARCEL_IDPARCEL_IDPARCEL_IDPARCEL_ID HECTARESHECTARESHECTARESHECTARES SUIT CLASSSUIT CLASSSUIT CLASSSUIT CLASS

1 0820020142 000 0820020142000 3.059 1

2 0820020142 000 0820020142000 1.126 3

3 0820020142 000 0820020142000 0.039 3

4 0820020142 000 0820020142000 1.890 4

5 0820020142 000 0820020142000 0.000 4

6 0820020142 000 0820020142000 0.000 5

7 0820020142 000 0820020142000 0.144 5

8 0820020164 000 0820020164000 0.200 1

9 0820020164 000 0820020164000 0.124 1

10 0820020164 000 0820020164000 0.475 1

11 0820020164 000 0820020164000 9.368 1

12 0820020164 000 0820020164000 1.792 1

13 0820020164 000 0820020164000 0.113 1

14 0820020164 000 0820020164000 0.444 1

15 0820020164 000 0820020164000 0.337 1

16 0820020164 000 0820020164000 0.029 2

17 0820020164 000 0820020164000 0.354 3

18 0820020164 000 0820020164000 0.092 3

19 0820020164 000 0820020164000 0.012 3

20 0820020164 000 0820020164000 0.912 3

21 0820020164 000 0820020164000 0.947 3

22 0820020164 000 0820020164000 0.229 3

23 0820020164 000 0820020164000 0.034 3

24 0820020164 000 0820020164000 0.254 4

25 0820020164 000 0820020164000 0.285 4

26 0820020164 000 0820020164000 0.109 4

27 0820020164 000 0820020164000 0.716 4

28 0820020164 000 0820020164000 1.364 4

29 0820020164 000 0820020164000 0.279 4

30 0820020164 000 0820020164000 0.136 5

31 0820020164 000 0820020164000 0.186 5

32 0820020164 000 0820020164000 0.235 5

33 0820020164 001 0820020164001 0.217 1

34 0820020164 002 0820020164002 1.518 1

35 0820020164 002 0820020164002 0.003 3



36 0820020164 002 0820020164002 1.643 4

37 0820020164 003 0820020164003 0.101 1

38 0820020164 004 0820020164004 0.260 1

39 0820020164 005 0820020164005 0.384 1

40 0820020164 005 0820020164005 0.068 3

41 0820020164 005 0820020164005 3.499 4

42 0820020164 006 0820020164006 0.045 3

43 0820020164 006 0820020164006 0.733 4

44 0820020164 008 0820020164008 0.002 3

45 0820020164 008 0820020164008 0.013 3

46 0820020164 008 0820020164008 1.019 4

47 0820020164 008 0820020164008 1.117 4

48 0820020164 010 0820020164010 0.106 3

49 0820020164 010 0820020164010 0.574 4

50 0820020164 010 0820020164010 0.076 5

51 0820020164 011 0820020164011 0.717 3

52 0820020164 011 0820020164011 2.864 4

53 0820020164 011 0820020164011 1.864 5

54 0820020164 012 0820020164012 0.507 3

55 0820020164 012 0820020164012 0.715 4

56 0820020164 012 0820020164012 0.150 5

57 0820020164 013 0820020164013 0.004 1

58 0820020164 013 0820020164013 0.070 3

59 0820020164 013 0820020164013 0.133 4

60 0820020164 013 0820020164013 1.151 5

61 0820020164 014 0820020164014 0.044 1

62 0820020164 014 0820020164014 0.186 3

63 0820020164 014 0820020164014 0.552 4

64 0820020164 014 0820020164014 0.743 5

65 0820020164 015 0820020164015 0.494 1

66 0820020164 015 0820020164015 0.036 4

67 0820020164 015 0820020164015 0.737 5

68 0820020164 016 0820020164016 0.666 1

69 0820020164 016 0820020164016 1.094 1

70 0820020164 016 0820020164016 0.007 4

71 0820020164 016 0820020164016 0.018 5

72 0820020164 017 0820020164017 1.095 1

73 0820020164 017 0820020164017 0.141 4

74 0820020164 017 0820020164017 0.052 5

75 0820020164 018 0820020164018 1.341 1



76 0820020164 019 0820020164019 0.854 1

77 0820020164 020 0820020164020 0.849 1

78 0820020164 021 0820020164021 0.255 1

79 0820020164 022 0820020164022 0.749 1

80 0820020164 023 0820020164023 0.854 1

81 0820020164 024 0820020164024 0.769 1

82 0820020164 025 0820020164025 0.454 1

83 0820020164 025 0820020164025 0.002 4

84 0820020164 026 0820020164026 0.156 1

85 0820020164 026 0820020164026 0.026 3

86 0820020164 026 0820020164026 0.324 4

87 0820020164 027 0820020164027 0.438 1

88 0820020164 028 0820020164028 0.552 1

89 0820020164 029 0820020164029 2.689 1

90 0820020164 029 0820020164029 0.005 5

91 0820020164 030 0820020164030 3.561 1

92 0820020164 030 0820020164030 0.662 5

93 0820020164 031 0820020164031 1.750 1

94 0820020164 031 0820020164031 0.001 2

95 0820020164 031 0820020164031 0.008 3

96 0820020164 031 0820020164031 0.000 4

97 0820020164 033 0820020164033 1.136 1

98 0820020164 033 0820020164033 0.019 2

99 0820020164 033 0820020164033 0.679 4

100 0820020164 034 0820020164034 0.008 1

101 0820020164 034 0820020164034 0.993 4

102 0820020164 034 0820020164034 0.027 5

103 0820020164 035 0820020164035 0.191 4

104 0820020164 035 0820020164035 0.430 5

105 0820020164 036 0820020164036 0.148 3

106 0820020164 036 0820020164036 0.132 4

107 0820020164 036 0820020164036 0.701 5

108 0820020164 037 0820020164037 1.739 3

109 0820020164 037 0820020164037 0.003 4

110 0820020164 038 0820020164038 0.577 3

111 0820020164 038 0820020164038 1.097 4

112 0820020164 039 0820020164039 0.004 1

113 0820020164 039 0820020164039 0.016 3

114 0820020164 039 0820020164039 0.767 4

115 0820020164 040 0820020164040 0.271 1



116 0820020164 040 0820020164040 0.109 3

117 0820020164 040 0820020164040 0.048 4

118 0820020164 041 0820020164041 0.566 1

119 0820020164 041 0820020164041 0.283 3

120 0820020164 042 0820020164042 0.555 3

121 0820020164 043 0820020164043 1.959 3

122 0820020164 044 0820020164044 1.617 3

123 0820020164 044 0820020164044 0.069 4

124 0820020164 045 0820020164045 3.149 3

125 0820020164 045 0820020164045 1.253 4

126 0820020164 046 0820020164046 0.223 4

127 0820020164 047 0820020164047 0.000 3

128 0820020164 047 0820020164047 0.152 4

129 0820020164 048 0820020164048 1.469 3

130 0820020164 048 0820020164048 0.674 4

131 0820020164 049 0820020164049 0.001 1

132 0820020164 049 0820020164049 0.028 3

133 0820020164 049 0820020164049 1.027 4

134 0820020164 050 0820020164050 0.165 1

135 0820020164 050 0820020164050 0.007 4

136 0820020164 051 0820020164051 0.012 3

137 0820020164 051 0820020164051 3.375 4

138 0820020164 052 0820020164052 1.126 3

139 0820020164 052 0820020164052 1.538 4

140 0820020164 053 0820020164053 2.252 3

141 0820020164 053 0820020164053 0.002 4

142 0820020164 054 0820020164054 0.762 3

143 0820020164 054 0820020164054 0.087 4

144 0820020164 055 0820020164055 0.303 3

145 0820020164 055 0820020164055 3.207 4

146 0820020164 056 0820020164056 2.280 3

147 0820020164 056 0820020164056 0.988 4

148 0820020164 057 0820020164057 0.666 1

149 0820020164 057 0820020164057 1.259 3

150 0820020164 057 0820020164057 0.768 3

151 0820020164 057 0820020164057 0.486 4

152 0820020164 057 0820020164057 0.729 4

153 0820020164 057 0820020164057 1.431 5

154 0820020164 057 0820020164057 2.867 5

155 0820020164 058 0820020164058 3.390 1



156 0820020164 058 0820020164058 0.634 5

157 0820020164 059 0820020164059 0.043 1

158 0820020164 059 0820020164059 0.871 5

159 0820020164 060 0820020164060 0.053 1

160 0820020164 060 0820020164060 0.038 3

161 0820020164 060 0820020164060 0.798 4

162 0820020164 060 0820020164060 0.582 5

163 0820020164 061 0820020164061 0.480 3

164 0820020164 061 0820020164061 0.244 4

165 0820020164 062 0820020164062 1.757 3

166 0820020164 062 0820020164062 0.864 4

167 0820020164 063 0820020164063 4.509 3

168 0820020164 063 0820020164063 0.013 4

169 0820020164 064 0820020164064 1.753 3

170 0820020164 065 0820020164065 4.057 3

171 0820020164 065 0820020164065 0.020 4

172 0820020164 066 0820020164066 1.044 3

173 0820020164 066 0820020164066 1.082 4

174 0820020164 067 0820020164067 1.237 3

175 0820020164 067 0820020164067 0.048 4

176 0820020164 067 0820020164067 0.006 5

177 0820020164 068 0820020164068 1.778 3

178 0820020164 068 0820020164068 1.905 4

179 0820020164 068 0820020164068 0.044 5

180 0820020164 069 0820020164069 0.023 3

181 0820020164 069 0820020164069 0.986 4

182 0820020164 069 0820020164069 0.085 5

183 0820020164 070 0820020164070 0.255 4

184 0820020164 070 0820020164070 0.007 5

185 0820020164 071 0820020164071 0.176 1

186 0820020164 071 0820020164071 0.600 2

187 0820020164 071 0820020164071 0.121 3

188 0820020164 071 0820020164071 0.579 4

189 0820020164 071 0820020164071 0.635 5

190 0820020164 072 0820020164072 0.669 3

191 0820020164 072 0820020164072 0.249 4

192 0820020164 072 0820020164072 0.559 5

193 0820020164 073 0820020164073 3.411 3

194 0820020164 073 0820020164073 0.172 5

195 0820020164 074 0820020164074 0.103 5


SLNOSLNOSLNOSLNO VILLVILLVILLVILLAGEAGEAGEAGE CODECODECODECODE PARCELPARCELPARCELPARCEL NONONONO PARCEL_IDPARCEL_IDPARCEL_IDPARCEL_ID HECTARESHECTARESHECTARESHECTARES SUIT CLASSSUIT CLASSSUIT CLASSSUIT CLASS

196 0820020164 075 0820020164075 0.555 5

197 0820020164 076 0820020164076 0.216 2

198 0820020164 076 0820020164076 0.124 3

199 0820020164 076 0820020164076 1.958 4

200 0820020164 076 0820020164076 2.614 5

201 0820020164 077 0820020164077 0.014 1

202 0820020164 077 0820020164077 0.023 2

203 0820020164 077 0820020164077 0.096 3

204 0820020164 077 0820020164077 2.387 4

205 0820020164 077 0820020164077 1.044 4

206 0820020164 077 0820020164077 0.768 5

207 0820020164 078 0820020164078 3.556 1

208 0820020164 078 0820020164078 0.004 2

209 0820020164 078 0820020164078 0.001 3

210 0820020164 078 0820020164078 0.694 4

211 0820020164 078 0820020164078 0.006 5

212 0820020164 079 0820020164079 0.061 1

213 0820020164 079 0820020164079 0.004 2

214 0820020164 079 0820020164079 0.042 3

215 0820020164 079 0820020164079 0.017 4

216 0820020164 079 0820020164079 0.014 5

217 0820020164 080 0820020164080 0.068 1

218 0820020164 080 0820020164080 0.615 2

219 0820020164 080 0820020164080 0.361 3

220 0820020164 080 0820020164080 0.041 4

221 0820020164 080 0820020164080 0.005 5

222 0820020164 081 0820020164081 0.030 2

223 0820020164 081 0820020164081 0.182 3

224 0820020164 082 0820020164082 0.174 2

225 0820020164 083 0820020164083 14.927 1

226 0820020164 083 0820020164083 0.076 2

227 0820020164 083 0820020164083 0.081 3

228 0820020164 084 0820020164084 0.684 1

229 0820020164 085 0820020164085 0.832 1

230 0820020164 086 0820020164086 0.841 1

231 0820020164 087 0820020164087 0.888 1

232 0820020164 088 0820020164088 0.687 1

233 0820020164 089 0820020164089 0.889 1

234 0820020164 090 0820020164090 0.640 1

235 0820020164 090 0820020164090 0.861 1



236 0820020164 091 0820020164091 3.371 1

237 0820020164 091 0820020164091 0.189 5

238 0820020164 092 0820020164092 1.562 1

239 0820020164 092 0820020164092 2.774 5

240 0820020164 093 0820020164093 5.786 1

241 0820020164 094 0820020164094 3.120 1

242 0820020164 094 0820020164094 0.011 5

243 0820020164 095 0820020164095 3.439 1

244 0820020164 095 0820020164095 0.076 2

245 0820020164 095 0820020164095 0.234 5

246 0820020164 096 0820020164096 2.245 1

247 0820020164 096 0820020164096 0.300 2

248 0820020164 096 0820020164096 0.467 5

249 0820020164 097 0820020164097 1.030 1

250 0820020164 097 0820020164097 7.593 5

251 0820020164 098 0820020164098 0.948 1

252 0820020164 098 0820020164098 11.346 5

253 0820020164 099 0820020164099 11.797 5

254 0820020164 100 0820020164100 1.799 5

255 0820020164 101 0820020164101 2.146 1

256 0820020164 101 0820020164101 0.122 5

257 0820020164 102 0820020164102 0.445 1

258 0820020164 102 0820020164102 0.477 4

259 0820020164 102 0820020164102 1.702 5

260 0820020164 103 0820020164103 0.002 1

261 0820020164 103 0820020164103 0.984 4

262 0820020164 103 0820020164103 8.398 5

263 0820020164 104 0820020164104 5.973 4

264 0820020164 104 0820020164104 5.310 5

265 0820020164 105 0820020164105 0.191 1

266 0820020164 105 0820020164105 3.743 5

267 0820020164 106 0820020164106 0.927 1

268 0820020164 106 0820020164106 3.984 5

269 0820020164 107 0820020164107 0.000 1

270 0820020164 107 0820020164107 3.714 5

271 0820020164 108 0820020164108 2.416 1

272 0820020164 108 0820020164108 0.012 4

273 0820020164 108 0820020164108 6.098 5

274 0820020164 109 0820020164109 1.574 4

275 0820020164 109 0820020164109 10.925 5



276 0820020164 110 0820020164110 0.365 1

277 0820020164 110 0820020164110 0.009 4

278 0820020164 110 0820020164110 5.363 5

279 0820020164 111 0820020164111 6.278 1

280 0820020164 111 0820020164111 0.616 4

281 0820020164 111 0820020164111 0.985 5

282 0820020164 112 0820020164112 5.920 1

283 0820020164 112 0820020164112 3.417 5

284 0820020164 113 0820020164113 3.586 1

285 0820020164 113 0820020164113 0.460 5

286 0820020164 114 0820020164114 3.487 1

287 0820020164 114 0820020164114 0.005 5

288 0820020164 115 0820020164115 0.606 1

289 0820020164 115 0820020164115 0.455 4

290 0820020164 115 0820020164115 3.796 5

291 0820020164 116 0820020164116 0.229 1

292 0820020164 116 0820020164116 1.651 4

293 0820020164 116 0820020164116 8.694 5

294 0820020164 117 0820020164117 0.318 4

295 0820020164 117 0820020164117 4.186 5

296 0820020164 118 0820020164118 0.076 3

297 0820020164 118 0820020164118 1.280 4

298 0820020164 118 0820020164118 2.363 5

299 0820020164 119 0820020164119 0.973 3

300 0820020164 119 0820020164119 2.442 4

301 0820020164 119 0820020164119 0.744 5

302 0820020164 120 0820020164120 2.525 4

303 0820020164 121 0820020164121 4.075 4

304 0820020164 122 0820020164122 4.210 4

305 0820020164 122 0820020164122 0.732 5

306 0820020164 123 0820020164123 1.400 4

307 0820020164 123 0820020164123 3.040 5

308 0820020164 124 0820020164124 0.322 4

309 0820020164 124 0820020164124 0.057 5

310 0820020164 125 0820020164125 0.248 1

311 0820020164 125 0820020164125 1.426 4

312 0820020164 125 0820020164125 2.897 5

313 0820020164 126 0820020164126 2.568 1

314 0820020164 126 0820020164126 0.708 5

315 0820020164 127 0820020164127 1.717 1



316 0820020164 127 0820020164127 1.081 4

317 0820020164 127 0820020164127 0.645 5

318 0820020164 128 0820020164128 1.158 1

319 0820020164 128 0820020164128 1.380 4

320 0820020164 128 0820020164128 1.743 5

321 0820020164 129 0820020164129 3.754 4

322 0820020164 129 0820020164129 0.908 5

323 0820020164 130 0820020164130 0.477 4

324 0820020164 130 0820020164130 1.713 5

325 0820020164 131 0820020164131 2.805 4

326 0820020164 131 0820020164131 2.110 5

327 0820020164 132 0820020164132 0.320 1

328 0820020164 132 0820020164132 0.175 4

329 0820020164 132 0820020164132 1.811 5

330 0820020164 133 0820020164133 0.049 1

331 0820020164 133 0820020164133 0.024 4

332 0820020164 133 0820020164133 2.466 5

333 0820020164 134 0820020164134 0.710 4

334 0820020164 134 0820020164134 0.474 5

335 0820020164 135 0820020164135 1.773 1

336 0820020164 135 0820020164135 0.264 4

337 0820020164 135 0820020164135 1.955 5

338 0820020164 136 0820020164136 3.940 1

339 0820020164 137 0820020164137 2.774 1

340 0820020164 137 0820020164137 0.001 5

341 0820020164 138 0820020164138 3.374 1

342 0820020164 138 0820020164138 0.077 5

343 0820020164 139 0820020164139 1.253 1

344 0820020164 140 0820020164140 0.567 1

345 0820020164 141 0820020164141 0.676 1

346 0820020164 142 0820020164142 0.456 1

347 0820020164 143 0820020164143 3.883 1

348 0820020164 144 0820020164144 1.208 1

349 0820020164 144 0820020164144 0.040 4

350 0820020164 144 0820020164144 0.140 5

351 0820020164 145 0820020164145 0.291 1

352 0820020164 145 0820020164145 0.792 4

353 0820020164 145 0820020164145 0.449 4

354 0820020164 145 0820020164145 0.074 5

355 0820020164 145 0820020164145 0.003 5



356 0820020164 147 0820020164147 0.367 4

357 0820020164 147 0820020164147 0.163 5

358 0820020164 148 0820020164148 1.230 4

359 0820020164 148 0820020164148 0.174 5

360 0820020164 150 0820020164150 0.629 1

361 0820020164 150 0820020164150 0.004 4

362 0820020164 150 0820020164150 0.146 5

363 0820020164 151 0820020164151 0.542 1

364 0820020164 151 0820020164151 0.209 4

365 0820020164 151 0820020164151 0.072 5

366 0820020164 152 0820020164152 0.603 4

367 0820020164 152 0820020164152 0.186 5

368 0820020164 153 0820020164153 0.553 1

369 0820020164 153 0820020164153 0.461 4

370 0820020164 154 0820020164154 0.514 1

371 0820020164 154 0820020164154 0.559 4

372 0820020164 154 0820020164154 0.464 5

373 0820020164 155 0820020164155 0.115 4

374 0820020164 155 0820020164155 0.814 5

375 0820020164 156 0820020164156 0.006 3

376 0820020164 156 0820020164156 0.580 5

377 0820020164 157 0820020164157 0.103 3

378 0820020164 157 0820020164157 0.106 4

379 0820020164 157 0820020164157 0.530 5

380 0820020164 158 0820020164158 0.099 1

381 0820020164 158 0820020164158 0.051 4

382 0820020164 158 0820020164158 0.856 5

383 0820020164 159 0820020164159 0.183 1

384 0820020164 159 0820020164159 0.109 3

385 0820020164 159 0820020164159 0.153 4

386 0820020164 159 0820020164159 1.414 5

387 0820020164 160 0820020164160 0.272 5

388 0820020164 161 0820020164161 0.191 3

389 0820020164 161 0820020164161 0.685 4

390 0820020164 161 0820020164161 0.035 5

391 0820020164 162 0820020164162 0.871 3

392 0820020164 162 0820020164162 0.106 4

393 0820020164 163 0820020164163 0.731 3

394 0820020164 163 0820020164163 2.372 4

395 0820020164 163 0820020164163 0.064 5


SLNOSLNOSLNOSLNO VILLAGEVILLAGEVILLAGEVILLAGE CCCCODEODEODEODE PARCELPARCELPARCELPARCEL NONONONO PARCEL_IDPARCEL_IDPARCEL_IDPARCEL_ID HECTARESHECTARESHECTARESHECTARES SUIT CLASSSUIT CLASSSUIT CLASSSUIT CLASS

396 0820020164 164 0820020164164 0.028 1

397 0820020164 164 0820020164164 1.355 3

398 0820020164 164 0820020164164 2.168 4

399 0820020164 165 0820020164165 0.002 1

400 0820020164 165 0820020164165 0.023 3

401 0820020164 165 0820020164165 2.473 4

402 0820020164 166 0820020164166 2.456 1

403 0820020164 166 0820020164166 0.000 3

404 0820020164 166 0820020164166 0.731 4

405 0820020164 167 0820020164167 3.373 3

406 0820020164 167 0820020164167 0.023 4

407 0820020164 168 0820020164168 1.144 1

408 0820020164 168 0820020164168 0.059 3

409 0820020164 168 0820020164168 0.150 4

410 0820020164 169 0820020164169 0.619 1

411 0820020164 170 0820020164170 0.306 1

412 0820020164 170 0820020164170 0.321 3

413 0820020164 171 0820020164171 0.961 1

414 0820020164 171 0820020164171 0.119 3

415 0820020164 172 0820020164172 0.298 1

416 0820020164 172 0820020164172 0.262 3

417 0820020164 173 0820020164173 0.018 1

418 0820020164 173 0820020164173 1.177 3

419 0820020164 174 0820020164174 1.206 3

420 0820020164 175 0820020164175 0.717 3

421 0820020164 175 0820020164175 0.220 4

422 0820020164 176 0820020164176 0.062 3

423 0820020164 176 0820020164176 0.892 3

424 0820020164 176 0820020164176 0.093 3

425 0820020164 176 0820020164176 0.017 4

426 0820020164 176 0820020164176 0.241 4

427 0820020164 177 0820020164177 1.096 3

428 0820020164 178 0820020164178 0.706 3

429 0820020164 178 0820020164178 0.004 4

430 0820020164 178 0820020164178 0.171 5

431 0820020164 179 0820020164179 0.366 3

432 0820020164 179 0820020164179 0.489 4

433 0820020164 179 0820020164179 0.793 5

434 0820020164 180 0820020164180 0.016 4

435 0820020164 180 0820020164180 0.490 5



436 0820020164 181 0820020164181 0.707 4

437 0820020164 181 0820020164181 0.040 5

438 0820020164 182 0820020164182 0.358 4

439 0820020164 182 0820020164182 0.067 4

440 0820020164 182 0820020164182 0.004 5

441 0820020164 182 0820020164182 0.209 5

442 0820020164 183 0820020164183 0.209 3

443 0820020164 183 0820020164183 0.639 5

444 0820020164 184 0820020164184 0.504 3

445 0820020164 184 0820020164184 0.000 4

446 0820020164 184 0820020164184 0.204 5

447 0820020164 185 0820020164185 0.252 3

448 0820020164 186 0820020164186 1.281 3

449 0820020164 187 0820020164187 1.497 3

450 0820020164 188 0820020164188 0.025 1

451 0820020164 188 0820020164188 3.618 3

452 0820020164 188 0820020164188 0.519 4

453 0820020164 189 0820020164189 0.329 3

454 0820020164 189 0820020164189 0.533 4

455 0820020164 189 0820020164189 0.110 5

456 0820020164 190 0820020164190 0.514 3

457 0820020164 190 0820020164190 0.569 4

458 0820020164 190 0820020164190 1.873 5

459 0820020164 191 0820020164191 1.040 4

460 0820020164 191 0820020164191 0.501 5

461 0820020164 192 0820020164192 2.556 4

462 0820020164 192 0820020164192 0.767 5

463 0820020164 193 0820020164193 0.000 1

464 0820020164 193 0820020164193 0.675 4

465 0820020164 193 0820020164193 0.017 5

466 0820020164 194 0820020164194 0.621 1

467 0820020164 194 0820020164194 1.094 4

468 0820020164 194 0820020164194 0.752 5

469 0820020164 195 0820020164195 3.382 1

470 0820020164 195 0820020164195 0.031 4

471 0820020164 195 0820020164195 0.061 5

472 0820020164 196 0820020164196 0.659 1

473 0820020164 196 0820020164196 0.229 5

474 0820020164 197 0820020164197 0.144 1

475 0820020164 197 0820020164197 1.671 4



476 0820020164 197 0820020164197 0.483 5

477 0820020164 198 0820020164198 0.027 4

478 0820020164 198 0820020164198 0.145 5

479 0820020164 199 0820020164199 0.024 1

480 0820020164 199 0820020164199 1.633 4

481 0820020164 199 0820020164199 0.698 5

482 0820020164 200 0820020164200 0.722 4

483 0820020164 200 0820020164200 1.804 5

484 0820020164 201 0820020164201 0.819 3

485 0820020164 201 0820020164201 1.685 4

486 0820020164 201 0820020164201 0.111 5

487 0820020164 202 0820020164202 1.622 3

488 0820020164 202 0820020164202 0.138 4

489 0820020164 203 0820020164203 0.148 3

490 0820020164 203 0820020164203 0.923 4

491 0820020164 203 0820020164203 0.137 5

492 0820020164 204 0820020164204 1.354 3

493 0820020164 204 0820020164204 3.285 4

494 0820020164 204 0820020164204 0.538 5

495 0820020164 205 0820020164205 3.638 3

496 0820020164 205 0820020164205 0.715 4

497 0820020164 206 0820020164206 2.733 3

498 0820020164 206 0820020164206 1.035 4

499 0820020164 207 0820020164207 0.632 3

500 0820020164 207 0820020164207 4.441 4

501 0820020164 208 0820020164208 0.024 3

502 0820020164 208 0820020164208 0.447 4

503 0820020164 209 0820020164209 0.358 3

504 0820020164 209 0820020164209 0.143 4

505 0820020164 210 0820020164210 1.989 3

506 0820020164 210 0820020164210 2.658 4

507 0820020164 211 0820020164211 0.040 1

508 0820020164 211 0820020164211 3.014 3

509 0820020164 211 0820020164211 1.266 4

510 0820020164 212 0820020164212 0.500 1

511 0820020164 212 0820020164212 0.030 3

512 0820020164 212 0820020164212 0.011 4

513 0820020164 213 0820020164213 0.587 1

514 0820020164 213 0820020164213 0.006 3

515 0820020164 213 0820020164213 0.080 4



516 0820020164 214 0820020164214 0.636 1

517 0820020164 214 0820020164214 0.826 3

518 0820020164 214 0820020164214 0.841 4

519 0820020164 215 0820020164215 5.015 1

520 0820020164 215 0820020164215 0.055 3

521 0820020164 215 0820020164215 0.076 4

522 0820020164 217 0820020164217 1.678 1

523 0820020164 217 0820020164217 0.243 3

524 0820020164 217 0820020164217 1.148 4

525 0820020164 218 0820020164218 0.676 1

526 0820020164 219 0820020164219 3.363 1

527 0820020164 219 0820020164219 0.427 3

528 0820020164 219 0820020164219 0.422 4

529 0820020164 220 0820020164220 3.647 3

530 0820020164 220 0820020164220 1.600 4

531 0820020164 221 0820020164221 0.016 3

532 0820020164 221 0820020164221 1.063 4

533 0820020164 222 0820020164222 0.502 3

534 0820020164 222 0820020164222 1.296 4

535 0820020164 223 0820020164223 0.254 4

536 0820020164 224 0820020164224 1.892 1

537 0820020164 224 0820020164224 1.109 3

538 0820020164 224 0820020164224 0.324 4

539 0820020164 225 0820020164225 0.448 1

540 0820020164 225 0820020164225 0.533 3

541 0820020164 225 0820020164225 0.003 4

542 0820020164 226 0820020164226 0.777 1

543 0820020164 226 0820020164226 0.019 3

544 0820020164 226 0820020164226 0.256 4

545 0820020164 227 0820020164227 1.835 1

546 0820020164 227 0820020164227 0.692 3

547 0820020164 227 0820020164227 0.615 4

548 0820020164 228 0820020164228 0.903 1

549 0820020164 228 0820020164228 2.711 3

550 0820020164 228 0820020164228 0.060 4

551 0820020164 229 0820020164229 6.201 3

552 0820020164 229 0820020164229 0.035 4

553 0820020164 230 0820020164230 2.265 3

554 0820020164 230 0820020164230 2.127 4

555 0820020164 231 0820020164231 1.115 3



556 0820020164 231 0820020164231 0.664 4

557 0820020164 232 0820020164232 1.752 3

558 0820020164 232 0820020164232 2.696 4

559 0820020164 233 0820020164233 0.210 1

560 0820020164 233 0820020164233 1.246 3

561 0820020164 233 0820020164233 0.006 4

562 0820020164 234 0820020164234 4.353 3

563 0820020164 234 0820020164234 0.020 4

564 0820020164 235 0820020164235 2.564 1

565 0820020164 235 0820020164235 0.114 3

566 0820020164 236 0820020164236 0.085 1

567 0820020164 236 0820020164236 0.960 3

568 0820020164 236 0820020164236 0.494 4

569 0820020164 237 0820020164237 1.550 1

570 0820020164 237 0820020164237 0.034 3

571 0820020164 241 0820020164241 1.222 1

572 0820020164 242 0820020164242 0.231 1

573 0820020164 243 0820020164243 3.085 1

574 0820020164 244 0820020164244 0.323 1

575 0820020164 245 0820020164245 3.496 1

576 0820020164 246 0820020164246 0.844 1

577 0820020164 246 0820020164246 0.426 3

578 0820020164 247 0820020164247 1.077 3

579 0820020164 248 0820020164248 0.604 1

580 0820020164 248 0820020164248 0.541 3

581 0820020164 249 0820020164249 0.670 1

582 0820020164 249 0820020164249 1.296 3

583 0820020164 250 0820020164250 0.178 3

584 0820020164 251 0820020164251 0.393 1

585 0820020164 251 0820020164251 0.790 3

586 0820020164 252 0820020164252 0.983 1

587 0820020164 252 0820020164252 0.000 3

588 0820020164 253 0820020164253 1.085 1

589 0820020164 253 0820020164253 0.139 3

590 0820020164 254 0820020164254 0.738 1

591 0820020164 254 0820020164254 0.656 3

592 0820020164 256 0820020164256 1.000 3

593 0820020164 256 0820020164256 0.015 4

594 0820020164 257 0820020164257 1.327 3

595 0820020164 257 0820020164257 0.212 4


SLNOSLNOSLNOSLNO VILLAGEVILLAGEVILLAGEVILLAGE CODECODECODECODE PARCEPARCEPARCEPARCELLLL NONONONO PARCEL_IDPARCEL_IDPARCEL_IDPARCEL_ID HECTARESHECTARESHECTARESHECTARES SUIT CLASSSUIT CLASSSUIT CLASSSUIT CLASS

596 0820020164 258 0820020164258 0.347 1

597 0820020164 258 0820020164258 0.946 3

598 0820020164 259 0820020164259 0.255 1

599 0820020164 259 0820020164259 0.878 3

600 0820020164 260 0820020164260 0.054 1

601 0820020164 260 0820020164260 0.293 3

602 0820020164 261 0820020164261 0.089 1

603 0820020164 261 0820020164261 0.106 1

604 0820020164 261 0820020164261 0.228 3

605 0820020164 262 0820020164262 0.007 1

606 0820020164 262 0820020164262 0.274 3

607 0820020164 262 0820020164262 0.003 4

608 0820020164 264 0820020164264 0.013 1

609 0820020164 264 0820020164264 0.640 4

610 0820020164 265 0820020164265 0.982 3

611 0820020164 265 0820020164265 0.813 4

612 0820020164 266 0820020164266 0.688 3

613 0820020164 266 0820020164266 2.748 4

614 0820020164 267 0820020164267 2.575 3

615 0820020164 267 0820020164267 1.460 4

616 0820020164 268 0820020164268 0.663 3

617 0820020164 268 0820020164268 3.021 4

618 0820020164 269 0820020164269 4.233 3

619 0820020164 269 0820020164269 0.466 4

620 0820020164 270 0820020164270 4.597 3

621 0820020164 270 0820020164270 1.318 4

622 0820020164 271 0820020164271 5.359 3

623 0820020164 271 0820020164271 0.019 4

624 0820020164 272 0820020164272 3.913 3

625 0820020164 272 0820020164272 0.376 4

626 0820020164 273 0820020164273 2.103 3

627 0820020164 273 0820020164273 0.004 4

628 0820020164 274 0820020164274 2.373 3

629 0820020164 274 0820020164274 0.039 4

630 0820020164 275 0820020164275 3.815 3

631 0820020164 275 0820020164275 0.002 4

632 0820020164 276 0820020164276 0.001 1

633 0820020164 276 0820020164276 2.192 3

634 0820020164 276 0820020164276 1.739 4

635 0820020164 276 0820020164276 0.149 5



636 0820020164 277 0820020164277 0.895 3

637 0820020164 277 0820020164277 2.098 4

638 0820020164 277 0820020164277 0.909 5

639 0820020164 278 0820020164278 1.807 3

640 0820020164 278 0820020164278 0.016 4

641 0820020164 279 0820020164279 1.101 3

642 0820020164 279 0820020164279 2.919 4

643 0820020164 279 0820020164279 0.016 5

644 0820020164 280 0820020164280 0.693 3

645 0820020164 280 0820020164280 2.531 4

646 0820020164 280 0820020164280 0.434 5

647 0820020164 281 0820020164281 2.771 3

648 0820020164 281 0820020164281 2.354 4

649 0820020164 281 0820020164281 0.095 5

650 0820020164 282 0820020164282 0.915 3

651 0820020164 282 0820020164282 2.031 4

652 0820020164 283 0820020164283 1.063 3

653 0820020164 283 0820020164283 0.637 4

654 0820020164 284 0820020164284 3.406 3

655 0820020164 284 0820020164284 0.892 4

656 0820020164 285 0820020164285 0.588 3

657 0820020164 285 0820020164285 2.094 4

658 0820020164 286 0820020164286 0.136 3

659 0820020164 286 0820020164286 0.229 4

660 0820020164 287 0820020164287 1.830 3

661 0820020164 287 0820020164287 1.330 4

662 0820020164 288 0820020164288 0.681 3

663 0820020164 288 0820020164288 1.093 4

664 0820020164 289 0820020164289 0.439 1

665 0820020164 289 0820020164289 0.488 3

666 0820020164 289 0820020164289 3.503 4

667 0820020164 290 0820020164290 0.474 1

668 0820020164 290 0820020164290 0.019 4

669 0820020164 291 0820020164291 0.099 1

670 0820020164 291 0820020164291 0.000 3

671 0820020164 292 0820020164292 0.077 1

672 0820020164 292 0820020164292 1.261 3

673 0820020164 293 0820020164293 0.305 1

674 0820020164 293 0820020164293 0.022 2

675 0820020164 293 0820020164293 4.560 3



676 0820020164 293 0820020164293 0.258 4

677 0820020164 294 0820020164294 0.040 1

678 0820020164 294 0820020164294 0.214 2

679 0820020164 294 0820020164294 0.512 3

680 0820020164 294 0820020164294 0.654 4

681 0820020164 294 0820020164294 1.305 5

682 0820020164 295 0820020164295 0.132 1

683 0820020164 295 0820020164295 0.761 2

684 0820020164 295 0820020164295 1.283 4

685 0820020164 295 0820020164295 0.038 5

686 0820020164 296 0820020164296 0.015 1

687 0820020164 296 0820020164296 0.511 2

688 0820020164 296 0820020164296 0.049 3

689 0820020164 296 0820020164296 1.654 4

690 0820020164 296 0820020164296 0.695 5

691 0820020164 297 0820020164297 0.390 4

692 0820020164 297 0820020164297 1.208 5

693 0820020164 298 0820020164298 0.944 5

694 0820020164 299 0820020164299 0.173 4

695 0820020164 299 0820020164299 1.622 5

696 0820020164 300 0820020164300 0.797 2

697 0820020164 300 0820020164300 0.063 3

698 0820020164 300 0820020164300 1.039 4

699 0820020164 300 0820020164300 0.117 5

700 0820020164 301 0820020164301 24.230 1

701 0820020164 301 0820020164301 0.275 2

702 0820020164 301 0820020164301 0.589 3

703 0820020164 301 0820020164301 0.000 4

704 0820020164 302 0820020164302 4.602 1

705 0820020164 302 0820020164302 0.455 5

706 0820020164 303 0820020164303 0.006 1

707 0820020164 303 0820020164303 2.010 5

708 0820020164 304 0820020164304 2.814 1

709 0820020164 304 0820020164304 0.989 4

710 0820020164 305 0820020164305 1.036 1

711 0820020164 305 0820020164305 0.275 4

712 0820020164 306 0820020164306 2.277 1

713 0820020164 306 0820020164306 0.342 3

714 0820020164 306 0820020164306 0.116 4

715 0820020164 307 0820020164307 0.175 1



716 0820020164 308 0820020164308 0.258 1

717 0820020164 308 0820020164308 0.849 5

718 0820020164 309 0820020164309 0.216 1

719 0820020164 309 0820020164309 2.093 4

720 0820020164 309 0820020164309 0.143 5

721 0820020164 310 0820020164310 0.946 1

722 0820020164 310 0820020164310 1.149 5

723 0820020164 310 0820020164310 1.455 5

724 0820020164 312 0820020164312 2.077 1

725 0820020164 312 0820020164312 0.064 4

726 0820020164 312 0820020164312 0.398 5


References:References:References:References:

• BDA Master Plan - 2021 (Draft): 2005 : Published by Bangalore

Development Authority

• Burrogh P A and Mc Donneu R: Principles of Geographical Information

System, Oxford University Press, London

• Campbell Jhon, B: 1996 : Introduction to Remote Sensing, Taylor &

Francis.

• Census: 2001 : Bangalore Urban and Rural Districts

• ERDAS IMAGINE field guide

• ERDAS IMAGINE tour guide

• FAO: (1976) : A framework for land evaluation. Soil Bulletin 32, Food and

Agricultural Organization of United Nations, Rome

• Indore Development Plan 2011 (Draft) : 2003 : published by Directorate

of Town and Country Planning and ISRO, Ahmedabad

• Jacek Malczewski : 1999 : GIS and Multi-Criteria Decision Analysis, Jhon

Wiley & Sons

• James Heitzman: 2004: Network City Planning the Information Society in

Bangalore : Published by Oxford University Press, New Delhi

• Jenson Jhon ,R: 1996: Introcution to Digital Image Processing: A Remote

Sensing Perspective, II Edition, Prentice Hall

• Karnataka State Gazetteer: Bangalore District, 1990; Government of

Karnataka Publication

• K. S. Gopalan, Director, SAC, ISRO, Ahmedabad: High resolution imagery

for developmental planning with special reference to developing

economies

• Lillisand Thomas, M & Keifer, Ralph : 1993 : Remote Sensing Image

Interpretation, Third Edition, John Wiley

• Paul A Longley : 2001 : Geographic Information Systems and Science,

Jhon Wiley & Sons

‘A Land Use And Land Cover Classification System For Use With

Remote Sensor Data’ By James R. Anderson, Ernest E. Hardy, Jhon T.

Roach, and Richard E. Witmer


• Revised Development Plan of Ahmedabad 2011; Vol.1:Remote Sensing

and GIS Approach: Published by Ahmedabad Urban Development

Authority and ISRO, Ahmedabad.

• Soil Survey Manual : 1971 : Published by All India Soil and Landuse Survey

Organization, IARI, New Delhi

• Understanding GIS: the ARC/INFO Method

• Urban Developments Plan Formulation and Implementation (UDPFI)

Guidelines: 1996 : published by Ministry of Urban affairs and

Employment, Govt. of India.

• Using ArcVIEW: Tutorials

Internet Internet Internet Internet ReReReResources:sources:sources:sources:

• http://www.censusindia.net

• http://www.gisdevelopment.com

• http://www.kar.nic.in

• http://www.bangaloreit.com

• http://www.bdabangalore.org

• http://www.fao.org

http://www.censusindia.net/

http://www.gisdevelopment.com/

http://www.kar.nic.in/

http://www.bangaloreit.com/

http://www.bdabangalore.org/

http://www.fao.org/

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Figur

e 6.3: Gro

wth

o B

anga

lore

city fr

om 1537 to 2001 (cen

sus 2001)

Figur

e 6.2: Stu

dy ar

ea ove

r to

pogr

aphic she

et m

osaic of

Ban

galore

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Figur

e 6.3: Lo

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of Stu

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ea ove

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( B

DA)

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Figur

e 6.4: Lo

cation

of st

udy ar

ea ove

r BDA A

dministr

ative Are

a

App

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RS

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Figur

e 7.2: Sub

set Stu

dy Are

a of

Quick

Bird M

erge

d Imag

e

App

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Figur

e 7.3: Existing

Man

ually

Digitized

Lan

duse

/lan

dco

ver M

ap

App

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Figur

e 7.4: Gro

und W

ater

Pro

spec

ts M

ap

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Figur

e 7.5: Soil Dep

th M

ap

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Figur

e 7.6: Soil Tex

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Map

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Figur

e 7.7: La

nd V

alue

(Gov

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Figur

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Built-u

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Figur

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Roa

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Figur

e 7.10: M

aste

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Figur

e 7.11: B

uilt-u

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a Con

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Figur

e 7.12: W

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Bod

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Figur

e 7.14: Ove

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Figur

e 7.16: Ove

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Figure 7.15: Village wise Parcel Vector Layer

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Figure 8.6 : Urban land Suitability Map For Urban Suitability Model 1

App

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Figure 8.10 : Parcel Level Urban Land Suitability Map for Model – 1

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Figure 8.18: Gunjur Village Parcel Level urban land suitability map for Parcel No: 303 : model-1

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