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

iii

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

xv

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

3

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.

4

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

7

Figure 1.0 : Focus Area of Study