Land Surface Temperature Determination For Selangor Area.

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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 projekadalah 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

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UMMI KALSOM BINTI MOHAMAD HASHIM

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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

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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: _________

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iii

DEDICATION

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ACKNOWLEDGEMENTS

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v

ABSTRACT

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ABSTRAK

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vii

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

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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

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

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

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4.2.11 Accuracy assessment/Testing ... 46

CHAPTER 5: IMPLEMENTATION... 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

CHAPTER 6: TESTING... 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

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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

7.2 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

8.2 Strengths ... 116

8.3 Weakness ... 117

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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

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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

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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

Figure 6.2 ROI and (b) Neural Network 82

Figure 6.3 Maximum Likelihood and (b) Neural Network 86

Figure 6.4 Maximum Likelihood and (b) Neural Network 90

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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

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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

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CHAPTER 1

INTRODUCTION

1.1 Project Background

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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.

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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

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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.)

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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 cityrapid 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.

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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

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