2
BORANG PENGESAHAN STATUS TESIS*
LAND SURFACE TEMPERATURE DETERMINATION FOR SELANGOR AREA
SESI PENGAJIAN : _ _ _ _ UMMI KALSOM BINTI MOHAMAD HASHIM
mengaku membenarkan tesis Projek Sarjana Muda ini disimpan di Perpustakaan Fakulti Teknologi Maklumat dan Komunikasi dengan syarat-syarat kegunaan seperti berikut:
1. Tesis dan projek adalah hakmilik Universiti Teknikal Malaysia Melaka. 2. Perpustakaan Fakulti Teknologi Maklumat dan Komunikasi dibenarkan membuat
salinan untuk tujuan pengajian sahaja. 3. Perpustakaan Fakulti Teknologi Maklumat dan Komunikasi dibenarkan membuat
salinan tesis ini sebagai bahan pertukaran antara institusi pengajian tinggi. 4. ** Sila tandakan (/)
(Mengandungi maklumat yang berdarjah
keselamatan atau kepentingan Malaysia seperti
yang termaktub di dalam AKTA RAHSIA
RASMI 1972)
(Mengandungi maklumat TERHAD yang telah
ditentukan oleh organisasi/badan di mana
penyelidikan dijalankan)
TIDAK TERHAD
(TANDATANGAN PENULIS) (TANDATANGAN PENYELIA)
Alamat tetap: KG.PULAU KABONG DR. ASMALA BIN AHMAD 32700, BERUAS Nama Penyelia
PERAK
Tarikh: Tarikh:
CATATAN: * Tesis dimaksudkan sebagai Laporan Projek Sarjana Muda (PSM).
** Jika tesis ini SULIT atau atau TERHAD, sila lampirkan surat daripada pihak berkuasa.
2011 / 2012 JUDUL :
Saya
SULIT TERHAD
LAND SURFACE TEMPERATURE DETERMINATION FOR SELANGOR
AREA
UMMI KALSOM BINTI MOHAMAD HASHIM
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
LAND SURFACE TEMPERATURE DETERMINATION FOR SELANGOR
AREA
UMMI KALSOM BINTI MOHAMAD HASHIM
This report is submitted in partial fulfillment of the requirements for the Bachelor of Computer Science (Artificial Intelligence)
FACULTY OF INFORMATION AND COMMUNICATION TECHNOLOGY UNIVERSITI TEKNIKAL MALAYSIA MELAKA
2013
ii
DECLARATION
I hereby declare that this project report entitled
LAND SURFACE TEMPERATURE DETERMINATION FOR SELANGOR AREA
is written by me and is my own effort and that no part has been plagiarized
without citations.
STUDENT : ____________________________________Date: _________
UMMI KALSOM BINTI MOHAMAD HASHIM SUPERVISOR : ____________________________________Date: _________
DR. ASMALA BIN AHMAD
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DEDICATION
I dedicate my final year project report to my family and many friends. A special to
my loving parents, whose words of encouragement and push for tenacity ring in my
ears. I also dedicate this report to my close friends and family who have supported
me throughout the project development. I will always appreciate all they have done.
I dedicate this work and give special thanks to my best friends Fatehah and Amalina
for being support me throughout the entire project development. Both of you have
been my best cheerleaders.
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ACKNOWLEDGEMENTS
I would like to acknowledge the contributions of the following individuals to the
development of this final year project report: My class peer for the cooperation and
camaraderie. I am also heartily thankful to my final year project supervisor,
Dr.Asmala bin Ahmad, whose encouragement, guidance and support from the initial
to the final level enabled me to develop an understanding of the project. I would also
like to thank Dr.Zuraida and Mr.Ahmad Fadzli Nizam for their comments and
questions were very beneficial in my completion of my final year project. To my
truly great friends Fatehah Hanim Ahmad Othman and Nuramalina Mat Serah who
has made available their support in a number of ways. Lastly, I am offer my regards
and blessings to all of those who supported me in any respect during the completion
of the project.
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ABSTRACT
The study was implemented by using Landsat TM. This scene covers the areas of
Klang, Selangor. The main objective of this study is to evaluate the use of remote
sensed information. Remote sensing is a technology to obtain information about the
object or phenomenon without having to be physically connected to the object.
Remote sensed information is use to gain land surface temperature (LST) using
supervised method of Landsat images. The derivation of LST map using remote
sensing technique in this study is useful in providing information for analyzing
geophysical information over Selangor area, especially dealing with the urban heat
island phenomenon. Supervised image classification was used, in which it requires
training sites on the image that represent of each desired land temperature category.
The delineation of training areas that represent the land temperature is most effective
when an image analyst has knowledge of the geography of a region and experience
with the spectral properties of the suitable classes. Envi software is used to identified
spectral values or signatures associated with the training sites. After the signatures
for each land temperature category have been identified, the software then uses all
the signatures to classify the remaining pixels. The methodology consist 4 phases;
analysis, design, implementation and testing.
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ABSTRAK Kajian ini dilaksanakan menggunakan Landsat TM. Kejadian ini meliputi kawasan
Klang, Selangor. Objektif utama kajian ini adalah untuk menilai penggunaan
maklumat ‘Remote Sensing’. ‘Remote Sensing’ adalah perolehan maklumat tentang
objek atau fenomena tanpa membuat hubungan fizikal dengan objek. Maklumat
‘remote sensing’ adalah digunakan untuk mendapatkan suhu permukaan tanah (LST)
menggunakan kaedah ‘supervised’ dan imej satelit. Penghasilan peta LST
menggunakan teknik ‘remote sensing’ dalam kajian ini amat berguna dalam
menyediakan maklumat untuk menganalisis parameter geofizik di kawasan Selangor,
terutamanya berurusan dengan fenomena haba pulau bandar. ‘Supervised image
classification’ adalah satu kaedah di mana penganalisis mentakrifkan kawasan kecil,
yang dikenali sebagai tapak latihan, pada imej yang mewakili setiap kategori suhu
tanah yang dikehendaki. Penggarisan wakil kawasan latihan suhu tanah adalah paling
berkesan apabila seorang penganalisis imej mempunyai pengetahuan geografi rantau
dan pengalaman dengan ciri-ciri spektrum kelas perlindungan. Penganalisis imej
kemudian menggunakan perisian untuk mengenali nilai spektrum atau petanda yang
berkaitan dengan tapak latihan. Selepas petanda bagi setiap kategori suhu tanah telah
ditentukan, perisian kemudian menggunakan petanda untuk mengklasifikasikan baki
piksel. Kaedah ini terdiri 4 fasa iaitu analisis, reka bentuk, pelaksanaan dan ujian.
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TABLE OF CONTENTS DECLARATION ......................................................................................................... ii
DEDICATION ............................................................................................................ iii
ACKNOWLEDGEMENTS ........................................................................................ iv
ABSTRACT................................................................................................................. v
ABSTRAK .................................................................................................................. vi
TABLE OF CONTENTS ....................................................................................... vii-x
LIST OF TABLES ................................................................................................ xi-xii
LIST OF FIGURES ........................................................................................... xiii-xiv
LIST OF ABBREVIATIONS .................................................................................... xv
CHAPTER 1: INTRODUCTION ................................................................................ 1
1.1 Project Background ............................................................................................ 1-2
1.2 Problem Statements ............................................................................................ 2-3
1.3 Objectives .............................................................................................................. 3
1.4 Scopes .................................................................................................................... 3
1.4.1 Software Scope .............................................................................................. 4
1.4.2 Area of Study Scope ................................................................................... 5-7
1.4.3 User Scope ..................................................................................................... 8
1.5 Project Significance ............................................................................................... 9
1.6 Expected Output .................................................................................................... 9
1.7 Conclusion ........................................................................................................... 10
CHAPTER 2: LITERATURE REVIEW AND PROJECT METHODOLOGY ....... 11
2.1 Introduction.......................................................................................................... 11
2.2 LST and remote sensing interest.................................................................... 12-13
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2.3 Supervised Method .............................................................................................. 14
2.3.1 Maximum Likelihood ............................................................................. 14-15
2.3.2 Neural Network ...................................................................................... 16-17
2.4 Project Methodology ...................................................................................... 17-19
2.5 Project Schedule and Milestone........................................................................... 20
2.6 Conclusion ........................................................................................................... 21
CHAPTER 3: ANALYSIS ........................................................................................ 22
3.1 Introduction..................................................................................................... 22-23
3.2 Problem Analysis ................................................................................................. 23
3.3 Requirement Analysis.......................................................................................... 24
3.3.1 Data Requirement ................................................................................... 24-30
3.3.2 Software and Hardware Requirement ..................................................... 30-34
3.3.3 Ancillary Data Requirement ............................................................................. 34
3.4 Conclusion ........................................................................................................... 35
CHAPTER 4: DESIGN.............................................................................................. 36
4.1 Introduction.......................................................................................................... 36
4.2 LST process .................................................................................................... 37-38
4.2.1 Input .................................................................................................................. 39
4.2.2 Geometric correction......................................................................................... 40
4.2.3 Subset/Resize .................................................................................................... 40
4.2.4 Ocean Masking ............................................................................................ 40-41
4.2.5 Cloud Masking .................................................................................................. 41
4.2.6 Classification using Maximum Likelihood and Neural Network ................ 41-44
4.2.7 LST modeling .............................................................................................. 44-45
4.2.8 LST derivation .................................................................................................. 45
4.2.9 LST segmentation ........................................................................................ 45-46
4.2.10 Output ............................................................................................................. 46
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4.2.11 Accuracy assessment/Testing ......................................................................... 46
4.3 Conclusion ........................................................................................................... 47
CHAPTER 5: IMPLEMENTATION......................................................................... 48
5.1 Introduction....................................................................................................... 48
5.2 Data preprocessing............................................................................................... 49
5.2.1 Input data process ........................................................................................ 49-51
5.2.2 Geometric correction process....................................................................... 52-55
5.2.3 Subset/resize process ................................................................................... 55-60
5.2.4 Ocean masking process ................................................................................ 61-62
5.2.5 Cloud masking process ................................................................................ 62-64
5.3 Classification using Maximum Likelihood and Neural Network ................... 64-66
5.4 LST modeling process .................................................................................... 66-67
5.4.1 Conversion from Digital Number to Radiance ................................................. 67
5.4.2 Conversion from Radiance to Brightness Temperature .................................... 68
5.5 LST derivation ..................................................................................................... 69
5.6 LST segmentation ................................................................................................ 70
5.7 Output ............................................................................................................ 71-78
5.8 Accuracy Assessment/Testing ............................................................................. 79
5.9 Conclusion ........................................................................................................... 79
CHAPTER 6: TESTING............................................................................................ 80
6.1 Introduction.......................................................................................................... 80
6.2 Maximum Likelihood Classification ................................................................... 81
6.3 Neural Network Classification ....................................................................... 81-82
6.4 Comparison Between Region of Interest (ROI) and Maximum Likelihood Classification......................................................................................................... 83-89
6.5 Comparison Between Region of Interest(ROI) and Neural Network
Classification ........................................................................................................ 90-94
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6.6 Comparison Between Maximum Likelihood and Neural Network
Classification......................................................................................................... 95-99
6.7 Comparison Between Maximum Likelihood Map and Temperature Map. 100-101
6.8 Conclusion .................................................................................................. 101-102
CHAPTER 7: APPLICATION OF LAND SURFACE TEMPERATURE DETERMINATION USING ANDROID ................................................................ 103
7.1 Introduction........................................................................................................ 103
7.2 Requirement....................................................................................................... 104
7.2.1 Sofware ........................................................................................................ 104
7.2.2 Hardware ............................................................................................... 104-105
7.3 Graphical User Interface ............................................................................. 106-109
7.4 Codes ................................................................................................................. 110
7.4.1 XML Codes ..................................................................................................... 110
7.4.2 Java Codes ............................................................................................... 111-113
7.5 Conclusion ......................................................................................................... 114
CHAPTER 8: CONCLUSION ................................................................................ 115
8.1 Introduction........................................................................................................ 115
8.2 Strengths ............................................................................................................ 116
8.3 Weakness ........................................................................................................... 117
8.4 Proposition for Improvement ...................................................................... 117-118
8.5 Conclusion ....................................................................................................... 118
REFERENCES ................................................................................................. xvi-xvii
APPENDICES .................................................................................................. xviii-xx
xi
LIST OF TABLES
TABLE TITLE PAGE
Table 1.0 List of software required 4
Spectral range of bands and spatial resolution for the Table 1.1 Landsat 5 TM sensor 6 Table 2.0 LandSat TM spectral consideration 11 Table 2.1 FYP schedule and milestone 19
Spectral range of bands and spatial resolution for the Table 3.0 Landsat 5 TM sensor 26 Table 3.1 Envi 4.5 Specifications 29 Table 3.2 Mathlab R2010a Specifications 31 Table 3.3 List of Hardware Requirement 31 Table 5.0 Sources of Geometric Error 49 Table 5.1 Image value before and after subset 55 Table 5.2 List of pixel changes at different temperatures 71 Table 5.3 Total of pixel at different temperatures 71
Confusion Matrix in Pixels; Region of Interest vs Table 6.0 Maximum Likelihood 79
Confusion Matrix in Percent; Region of Interest vs Table 6.1 Maximum Likelihood 79
Confusion Matrix in Commission and Ommission; Region Table 6.2 of Interest vs Maximum Likelihood 80
Confusion Matrix in Prod. Acc and User Acc; Region of Table 6.3 Interest vs Maximum Likelihood 80
Confusion Matrix in Pixels; Region of Interest vs Neural Table 6.4 Network 83
Confusion Matrix in Percent; Region of Interest vs Neural Table 6.5 Network 83
Confusion Matrix in Commission and Ommission; Region Table 6.6 of Interest vs Neural Network 84
xii
Confusion Matrix in Prod. Acc and User Acc; Region of Table 6.7 Interest vs Neural Network 84
Confusion Matrix in Pixels; Maximum Likelihood vs Table 6.8 Neural Network 87
Confusion Matrix in Percent; Maximum Likelihood vs Table 6.9 Neural Network 87
Confusion Matrix in Commission and Ommission; Table 6.11 Maximum Likelihood vs Neural Network 88
Confusion Matrix in Prod. Acc and User Acc; Maximum Table 6.12 Likelihood vs Neural Network 88 Table 7.0 Specification of laptop 94 Table 7.1 Specifications of tablet 95 Table 8.0 Result of Classification Error Between ML and NN 103
xiii
LIST OF FIGURES FIGURE TITLE PAGE
Figure 1.0 Focus Area of Study 6 Figure 2.0 System Development Cycle of Waterfall Model 17 Figure 3.0 Process capture satellite image 24 Figure 3.1 Envi Interface 29 Figure 4.0 LST process 35
Classification process using maximum likelihood Figure 4.1 method 40
Overview of how remotely sensed data are turned Figure 5.0 into useful information 47 Figure 5.1 Spatial Subset (Subsetting by Images) 52 Figure 5.2 (a) Image before subset, (b) Image after subset 52
Histogram of the difference image value: (a)Image Figure 5.3 before subset, (b)Image after subset 55
Ocean masking: (a)Masking into 0 and 1 data value, Figure 5.4 (b) Masking into 0 data value only 56
Cloud masking: (a) Image before mask, (b) Image Figure 5.5 after mask 58 Figure 5.6 Classification using Maximum Likelihood method 60 Figure 5.7 Temperature value of land cover histogram 62
Temperature value range for different land cover Figure 5.8 type 64
Comparison Landcover Classification Using (a) Figure 6.0 ROI and (b) Maximum Likelihood 76
Regions of interest (ROIs) Using by Maximum Figure 6.1 likelihood and Neural Network 77
Comparison Landcover Classification Using (a) Figure 6.2 ROI and (b) Neural Network 82
Comparison Landcover Classification Using (a) Figure 6.3 Maximum Likelihood and (b) Neural Network 86
Comparison Landcover Classification Using (a) Figure 6.4 Maximum Likelihood and (b) Neural Network 90 Figure 7.0 Main Page of Land Surface Temperature 96
xiv
Determination Application Second Page of Land Surface Temperature
Figure 7.1 Determination Application 96 Third Page of Land Surface Temperature
Figure 7.2 Determination Application 97 Fourth Page of Land Surface Temperature
Figure 7.3 Determination Application 98
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LIST OF ABBREVIATIONS LST - Land-surface temperature
TM - Thematic Mapper
OS - Operating Windows
UHI - Urban Heat Island
IFOV - Instantaneous Field Of View
ML - Maximum Likelihood
ENVI - ENvironment for Visualizing Images
MATLAB - Matrix Laboratory
ROI - Regions of Interest
ARSM - Malaysian Remote Sensing Agency
MACRES - Malaysian Centre for Remote Sensing
MOSTI - Ministry of Science, Technology and
Innovation
TDRS - Tracking Data and Relay Satellites
GCP - Ground Control Point
IR - Infra-Red
PSM2 - Projek Sarjana Muda 2
NN - Neural Network
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CHAPTER 1
INTRODUCTION 1.1 Project Background
Image processing is a technology that provides numerous benefits for the
betterment of life. Remote sensing is one of technology used in image processing. The
use of digital image processing for land temperature survey and mapping was initiated
with the establishment of the National Remote Sensing Agency and Regional Remote
Sensing Service Centres. Land-surface temperature (LST) is the thermal emission
from the earth surface, including the top of the canopy for vegetated surfaces as well as
other surfaces (such as residential landscape). LST plays an important role in the field
of atmospheric sciences as it describes surface-atmosphere interaction between the
atmosphere and the ground. Land surface temperature is an important parameter in
determining the heat and moisture flow between the surface and the atmosphere. LST
is also important in hydrologic, vegetation and decomposition processes. LST helps to
control water exchange and surface heat with atmosphere.
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This study used data recorded from a remote sensing satellite. Landsat platform use
TM (Thematic Mapper) sensor. It is a multispectral scanning Earth surface. Images
captured from such sensor usually has sharper spectral separation and greater image
resolution. Other than that, it also has greater geometric accuracy. The TM data are
scanned simultaneously by the TM sensor that is equipped with different spectral
bands. Before the advent of this technology, land mapping information is done by
using a plane that would cost. By using this technology mapping method can be done
in a better way and save costs. Nature of technology requires data from satellite images
to identify and use the land surface through the spectral response.
1.2 Problem Statements
The study of LST mapping using remote sensing technology offers a new
alternative over the convensional methods practiced elsewhere. This technology has
not been widely used in Malaysia as compared to western countries.
One of the main problems encountered in the conventional methods is that
the LST data may contain variety of disturbance such as a noise. The disturbance of
images will affect the accuracy of the temperature of the images. The traditional way
to mapping process are use a plane and Sling Psychrometer at certain point on the
ground. The ambient temperature variation only cover within a limited space. It needs
a longer time to get the final result because the data need to be combined manually
before getting the result. Involvement of plane required a big amount of cost. The
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technique used does not give a good result because they could not prevent the images
from environmental interference such as noise. Moreover, ground truth information of
earth surfaces is hard to obtain due to the factors such as time, logistic and cost. The
aim of the study is to derive derive LST information using remote sensing technology. 1.3 Objectives
In order to achieve aim of the study, the specific objectives are: 1) To design a method to derive LST information.
2) To develop LST information.
3) To assess the accuracy of the LST map.
1.4 Scopes
The Scopes of the study is divided into three part: software scope, area of
study scope and user scope.
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1.4.1 Software Scope
All of the software in Table 1.0 is required in LST mapping process from
start to end of the process of mapping.
Software Requirement
Operating Windows (0S) Microsoft Windows 7 Ultimate
Software Processed Envi 4.5(Used to process and analyze
geospatial imagery.)
Software Support Matlab 2009a
Database Support Microsoft Excel 2010
( Use to represent the data.)
Table 1.0: List of software required
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1.4.2 Area of Study Scope
Klang was chosen as the focus area of the study. Klang, Selangor is located
at latitude: 3.043061 and Longitude: 101.440657, 573KM2). Klang is the royal town
and former capital of Selangor. The Klang River was separates Klang town into
north and south sections. Klang is also the main port of Malaysia. On the east side,
Klang borders with Shah Alam, on the north side borders with Kuala Selangor, on the
west side borders with the Straits of Melaka and borders with Banting (Kuala Langat)
to the south. Klang is booming and becoming the focus city rapid development and
high growth of urbanisation and industrialisation. If unplanned development is practiced in Klang, various problems tend to exist. These
urban transformations have altered the physical and natural environment. The urban
surfaces absorb heat and will increase the temperature. This leads to Urban Heat Island
(UHI) phenomenon. Therefore, a systematic urban planning is needed in order to
avoid this phenomenon to occur. By using an appropriate Land Surface Temperature
Determination the urban areas can be planned and developed systematically. Figure
1.0 show the location of Klang as the focus area of study. Spectral used in this area is band 1 until band 7. Landsat 5 TM was use as a platform
in data processed and recorded. Landsat 5 is the fifth satellite of the Landsat program.
It has a maximum transmission bandwidth of 85 Mbit/s. It was used at an altitude of
438.3 mi (705.3 km). It takes some 16 days to scan the entire Earth. The Thematic
Mapper (TM) is an advanced satellite compare to others. With the multispectral
scanning capabilities, Earth resources sensor are capable to achieve higher image
6
resolution, sharper spectral separation, improved geometric fidelity and greater
radiometric accuracy and resolution.
TM data are processed in seven spectral bands simultaneously (Table 1.1).
Band 6 senses thermal (heat) infrared radiation. Landsat can get night scenes in band 6.
A TM scene has an Instantaneous Field Of View (IFOV) that capable to view image of
30m x 30m in bands 1-5 and 7 while band 6 has an IFOV of 120m x 120m on the
ground.
Landsat 5 (TM sensor) Wavelength(micrometers) Resolution (meters) Band 1 0.45 - 0.52 30 Band 2 0.52 - 0.60 30 Band 3 0.63 - 0.69 30 Band 4 0.76 - 0.90 30 Band 5 1.55 - 1.75 30 Band 6 10.40 - 12.50 120 Band 7 2.08 - 2.35 30
Table 1.1 : Spectral range of bands and spatial resolution for the Landsat 5 TM
sensor
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Figure 1.0 : Focus Area of Study