Application of satellite image and geographic information system gis to build up the land cover change map for the management of taipei city

THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURAL AND FORESTRY NGUYEN HUONG GIANG APPLICATION OF SATELLITE IMAGE AND GEOGRAPHIC INFORMATION SYSTEM TO BUILD UP THE LAND COVER CHANGE

THAI NGUYEN UNIVERSITY

UNIVERSITY OF AGRICULTURAL AND FORESTRY

NGUYEN HUONG GIANG

APPLICATION OF SATELLITE IMAGE AND GEOGRAPHIC

INFORMATION SYSTEM TO BUILD UP THE LAND COVER CHANGE

MAP FOR MANAGEMENT OF TAIPEI CITY

BACHELOR THESIS

Study Mode : Full-time

Major : Environmental Science And Management

Faculty : International Training and Development Center Batch : 2010 - 2015

Thai Nguyen 30/09/2015

Thai Nguyen University of Agriculture and Forestry

Degree program Bachelor of Environmental Science and Management

Thesis Title

Application of satellite image and geographic information system (GIS) to build-up the land cover change for the management of Taipei city

2004 to 2014.The LULC from Landsat images map showed about 2883 ha (10.61%) area has been changed from vegetation to building The amount of building area increased by 4995 ha (18.39 % of the total area), while vegetation area decreased by 5075 ha (18.69 %)

In addition, the brightness temperature map (BTM) from thermal infrared image observed from satellite can be utilized for assessing the impact of urbanization on thermal environment during the study period The result shows that the land surface brightness temperature (LSBT) increased 3.690 C from 2004 to 2014, indicating the more building area the higher temperature usually is By applying the satellite visible images associated with ArcGIS and ENVI software, the results of LULC and LSBT change detection can provide the reference for land management and environmental protection more efficiently Taipei city is the biggest industrial and commercial center in Taiwan The population reached 2,618,772 people in 2010 already Compared with 2009, the total population had been increased by 11,344 people (Taipei lookbook, 2010) The population growth and socio-economic development results in rapid increasing transportation and urban expansion to suburban regions The effects of urbanization on local weather and climate change resulted in a remarkable increase in mean temperatures from 2004 to 2014 According to the assessments, the strategy can be proposed for the management and protection environment in Taipei city, Taiwan

Keywords

Taipei city, Land cover change map, ArcGIS software, ENVI software, urbanization, Land surface brightness temperature

Number of pages 64

Date of submission September, 30, 2015

ACKNOWLEDGEMENT

First and foremost, I wish to express our sincere thanks to Center for Space and Remote Sensing Research (CSRSR) of National Central University (NCU), Taiwan for providing me all necessary facilities, skills and knowledge to complete this bachelor thesis Furthermore, I want to deeply thank our principal research adviser Assoc Prof Tang-Huang Lin, Prof Chang Chung-Pai and Assoc Prof Do Anh Tai guided me wholeheartedly when I study for my project

Especially thankful to International Training Center – Thai Nguyen University

of Agriculture and Forestry has facilitated for me the chance to come here to study and get more knowledge exchange

Finally yet importantly, I take this opportunity to record my sense of gratitude

to my families and friends who encourage and backing me unceasingly

TABLE OF CONTENTS

LIST OF FIGURES 1

LIST OF TABLES …2

LIST OF ABBREVIATIONS 3

PART I INTRODUCTION 5

1.1 Research rationale 5

1.2 Research objectives 6

1.3.The requirement 6

1.4 The significance: 6

PART II LITERATURE REVIEW 8

2.1 Theoretical basis 8

2.1.1 The land cover 8

2.1.2 The land covers change 11

2.1.3 Geographic information system (GIS) 12

2.1.4 Arcgis software 14

2.1.5 Remote Sensing(RS) 15

2.1.6 The Landsat program 17

2.1.7 ENVI software 19

2.1.8 Brightness Temperature 20

2.2 Practical basis 21

2.2.1 The research in the world 21

2.2.2 The research in Viet Nam 24

PART III.MATERIALS AND METHODS 28

3.1 Materials 28

3.1.1 The objects and scope of research 28

3.1.2 The content of research 28

3.2 Methods build-up land cover change map in period 2004-2014 28

3.2.1 Data Collection 30

3.2.2 Image Preprocessing 31

3.2.3 Image classification 35

3.2.4 Post Classification 37

3.3 Methods Build-up Brightness Temperature Map 40

3.3.1 Data collection of Landsat image thermal band 41

3.3.2 Conversion of the Digital Number (DN) to Spectral Radiance (Lλ) 42

3.3.3 Conversion to At-Satellite Brightness Temperature 43

3.3.4 Transfer the temperature value from Kelvin unit to Celsius unit 43

PART IV RESULTS 44

4.1 The natural conditions and socioeconomic in research area 44

4.1.1.Natural conditions 44

4.1.2 Socioeconomic conditions in Taipei city 46

4.2 Classification accuracy assessment of Taipei city in 2004 and 2014 48

4.3 Land Cover Maps 51

4.3.1 Landsat classification area statistics for 2004 and 2014 51

4.3.2 Land cover Map for Landsat 5 TM (2004) 52

4.3.3 Land cover Map for Landsat 8 TM (2014) 53

4.4 Land cover change map from 2004 to 2014 55

4.5 Brightness temperature map (BTM) for Taipei city 2004 and 2014 56

PART V DISCUSSION AND CONCLUSION 59

5.1 Discussion 59

5.2 Conclusion 60

REFERENCES 62

LIST OF FIGURES

Figure 2.1: Remote Sensing procedures 17

Figure 3.1(a): Satellite images cover the research area in 2004 31

Figure 3.1(b): Satellite images cover the research area in 2014 31

Figure 3.2(a): Composite bands 4, 3, 2 for LS-5 TM image; July 12th 2004 32

Figure 3.2(b): Composite bands 5, 4, 3 for LS-8 OLI/TIRS Pre-WRS2 image; August 25th, 2014 33

Figure 3.3: Landsat images shown in True and False composite colors 35

Figure 3.4: The classification image after we used the tool filtering 38

Figure 3.5: The Landsat thermal band image of Landsat 5 and Landsat 8 42

Figure 3.6: The formula calculate spectral radian of Land sat 5(2004) and Landsat 8 (2014) 42

Figure 3.7 : The formular calculate the At-Satellite Brightness Temperature 2004-2014 43

Figure 4.1: The Geographical location map of Taipei city 45

Figure 4.2: The classified images of land cover map for 2004 52

Figure 4.3: The statistics percentage of land cover type classification in 2004 52

Figure 4.4: The classified images of land cover map for 2014 53

Figure 4.5: The statistics percentage of land cover type classification in 2014 54

Figure 4.6: Land cover change of Taipei city in 2004-2014 55

Figure 4.7 : Brightness temperature map of Taipei city in 2004 57

Figure 4.8 : Surface brightness temperature (SBT) map of Taipei city in 2014 57

LIST OF TABLES

Table 2.1: Level of the classification 10

Table 2.2 : Parameters of ETM Landsat (Landsat 5) 18

Table 2.3: Parameters of LDCM Landsat (Landsat 8) 19

Table 3.1: The information of Landsat image 30

Table 3.2: Land cover classification scheme 39

Table 3.3: K1 and K2 Values in Landsat 8and Landsat5 images 41

Table 3.4: Rescaling Factor in Landsat 8 image and Landsat 5 images 41

Table 4.1: Accuracy assessment of LULC classification in 2004 and 2004 48

Table 4.2 : Accuracy assessment of classified land cover change in 2004 49

Table 4.3: Accuracy assessment of classified land cover change in 2014 50

Table 4.4 : Summary of classification area with Landsat data in 2004 and 2014 51

Table 4.5 : Statistical fluctuations of land cover change in the period 2004 - 2014 55

LIST OF ABBREVIATIONS

BTM Brightness temperature map

CASI Compact Airborne Spectrographic Imager

DN Digital Number

ENVI The Environment for Visualizing Images

EROS Earth Resources Observation and Science

ESRI Environmental Systems Research Institute

FLIR Forward Looking InfraRed

GDB Create Geodatabases

GIS Geographic Information System

IDL Interactive Data Language

IRS Indian Remote Sensing

LCC Land-Cover Changes

LCCS Land Cover Classification System

Lidar Light Detection and Ranging

LULCC Land-use and land-cover change

MEIS-II Multispectral Electro-optical Imaging Scanner

MOS Marine Observation Satellite

NIR Near Infrared

NDVI The normalized vegetation index

NASA National Aeronautics and Space Administration

OLI The Operational Land Imager

ROI Region of Interest

RSS Remote Sensing Systems

RS Remote Sensing

SPOT System Pour l'Observation de la Terre

RADAR Radio Detection And Ranging

SeaWiFS Sea-viewing Wide-Field-of View Sensor

TRRI Total reflected radiance index

TIRS Thermal Infrared Sensor

TRRI Total Reflected Radiance Index

USGS United States Geological Survey

PART I INTRODUCTION

1.1 Research rationale

Land is one of the most important natural resource, serving as a support for life and other developmental activities Nowadays, population increased dramatically released to population density rise and industry zone, the private industry park and raise strength economic society All of things which affected strong to land, specific with a city have speed development quickly such Taipei city Overtime, with these pressures of urbanization speed, which became main reason related to LCC Additionally, land cover impacted under strong natural disasters, human, economic and social development activities Study building the LCC map by satellite image and GIS technology helps to shorten the time compared to other technology mapping ago and it is factor important contributions in the management and assessing the current state of the environment

Therefore, the monitoring, research, management and the use of urban environment is an effective and reasonable speed urbanization developing such as Taipei city in Taiwan Remote sensing and GIS technology are well developed and widely applied to the related fields such as: atmosphere, hydrology, geology, environmental and agriculture, forestry observations Therefore, it’s an efficient way

to monitor the changes of land cover by means of remote sensing technology with high accuracy for the environmental management of research area Thus, having this project

conducted “Application of satellite image and geographic information system (GIS) to

build up the land cover change map for the management of Taipei city, Taiwan”

1.2 Research objectives

This study is generally aimed at producing land cover maps of the Taipei city in order

to monitor LCC with a focus on urban expansion during the period 2004 – 2014

The specific objectives of this study are:

- To produce land cover maps for 2004 and 2014

- To detect and analyze the LCC of the Taipei city in the period 2004-2014

- To build-up land surface temperature maps for 2004 and 2014 to assess the environment status

1.3.The requirement

- To acquire adequate data of natural condition, socio economic and spatial data

- To classify land cover type and process data

- To accuracy assesement of land cover map in 2004-2014

- To evaluate the impact of distribution land cover change on transformation of land surface temperature

- To be fluent ENVI and Arcgis software to mapping data and analyzing data

1.4 The significance:

- For learning and researching purpose: to understand about two software ENVI and Arcgis and to find ways to evaluate the changes in land cover changes and analyze the impact of land cover change to the land surface temperature This study is intended to use remotely sensed data to map and detect changes in land cover of the land cover type in the last 10 years (2004 – 2014)

- The practical significance: Applying the knowledge on reality combine with collecting and analyzing data, assessing the impact of land cover changed, providing information to local community about land cover change effect how with temperature and its effects in the research area and then suggest several reasonable strategies and measures from predictions in the thesis

PART II LITERATURE REVIEW

of land (Gordon, 1980; Milington et al., 1986; Franchek & Biggam, 1992) The

different types of land cover can be manage or use differently Land cover could be determined by analyzing satellite and aerial imagery Land cover maps provide information to help managers best understand the status landscape To see change over time, the land cover maps for several different years are needed With this information, managers can evaluate past management decisions as well as gain insight into the

possible effects of their current decisions before they are implemented

Coastal managers use land cover data and maps to better understanding the impacts of natural phenomena and human use of the landscape Maps can help managers assess urban growth, model water quality issues, predict and assess impacts from floods and storm surges, track wetland losses and potential impacts from sea level rise, prioritize areas for conservation efforts, and compare land cover changes

with effects in the environment or to connections in socioeconomic changes such as

increasing population

b) Land Cover Classification System (LCCS):

The Land Cover Classification System is a comprehensive, standardized a

priori classification system, designed to meet specific user requirements, and

created for mapping exercises, independent of the scale or means used to map

Any land cover identified anywhere in the world can be readily accommodated

( Pal Nikolli et al., 2010)

Classification is an abstract representation of the situation in the field using

well-defined diagnostic criteria: the classifiers Sokal (1974) well-defined it as: "the ordering

orarrangement of objects into groups or sets on the basis of their relationships."

Classification is one of the most important steps in handling remote sensing

imagery and represents important input data for geographic information systems

(GIS) (Oštir etal., 2006)

At each level the defined classes are mutually exclusive At the higher levels of

the classification system few diagnostic criteria are used, whereas at the lower levels

the number of diagnostic criteria increases Criteria used at one level of the

classification should not be repeated at another, i.e., lower, level (Table 2.1)

(Pal Nikolli et al.,2010)

Table 2.1: Level of the classification

1.1.2 Discontinuous urban fabric 1.2 Industrial commercial 1.2.1 Industrial or commercial units and transport units

1.2.2 Road and rail networks and associated land 1.2.3 Port areas

1.2.4 Airports

1.3.2 Dump sites 1.3.3 Construction sites 1.4.Artificial

1.4.2 Sport and leisure facilities

2.1.2 Permanently irrigated land 2.1.3 Rice fields

2.2.2 Fruit trees and berry plantations 2.2.3 Olive groves

agricultural areas 2.4.2 Complex cultivation 2.4.3 Land principally occupied by agriculture, with significant areas of natural vegetation

3.2 Shrub and/or

3.2.2 Moors and heathland 3 3.2.3 Sclerophyllous vegetation

3.2.4 Transitional woodland shrub 3.3 Open spaces with little 3.3.1 Beaches, dunes, and sand plains or no vegetation

3.3.2 Bare rock 3.3.3 Sparsely vegetated areas 3.3.4 Burnt areas

3.3.5 Glaciers and perpetual snow

4.1.2.Peatbogs 4.2 Coastal wetlands 4.2.1 Salt marshes

4.2.2 Salines 4.2.3 Intertidal flats

5.1.2 Water bodies

5.2.2 Estuaries 5.2.3 Sea and ocean

2.1.2 The land covers change

Land-cover change (LCC) therefore is the human modification of earth's terrestrial surface Humans have been modifying land to obtain food and other essentials for thousands of years; current rates, extents and intensities of LCC are far greater than ever in history, driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales These changes encompass the greatest environmental concerns of human populations today, including climate change, biodiversity loss and the pollution of water, soils and air (Ellis et al., 2013)

Monitoring and assessing the negative consequences of LCC while sustaining the production of essential resources, which became a major priority of researchers and policymakers around the world Besides that, The LCC is thus very important for the sustainable development of urbanization

2.1.3 Geographic information system (GIS)

a Definition:

A geographic information system (GIS) is a computer system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data

b Basic elements of GIS

− 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: GIS software provides the functions and tools needed to store, analyze, and display geographic information It also provides query tools , performs analysis and displays geographic information in the form of maps or reports

− Data: Perhaps the most important component of a GIS is the data, and often most expensive Geographic data which is comprised geographic feature and their corresponding attribute information Geographic data is entered in to a GIS using a technique called digitizing This process releated to digitally encoding geographic features , such as buildings, roads or county boundaries

− People:People really is main factor to create power of GIS Given this fact , the number of a GIS users has increased rapidly and no longer includes only GIS specialists Today, GIS is being used by people, in many different fields, as a tool that

enable them to perform their jobs effectively Example, biologist use GIS to protect plant and animal species, teacher use GIS to teach lesson in geography , history and engineering

− 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

c) The Functions of GIS

- Data validation and editing, eg checking and correction

- Structure conversion, eg conversion from vector to raster

- Geometric conversion, eg map registration, scale changes, projection changes, map transformations, rotation

- Generalisation and classification, eg reclassifying data, aggregation or disaggregation, co-ordinate thinning

- Integration, eg overlaying, combining map layers or edge matching

- Map enhancement, eg image enhancement, add title, scale, key, map symbolism, draping overlays

- Interpolation, eg kriging, spline functions, Thiessen polygons, plus centroid determination and extrapolation

- Buffer generation, eg calculating and defining corridors

- Data searching and retrieval, eg on points, lines or areas, on user defined themes or by using Boolean logic Also browsing, querying and windowing

- Graphical display, eg maps and graphs with symbols, labels or annotations

- Textual display, eg reports, tables

- Support and monitoring of multi-user access to the database

- Coping with systems failure

- Communication linkages with other systems

- Editing and up-dating of databases

- Organising the database for efficient storage and retrieval

- Maintenance of database security and integrity

- Provision of a “data independent” view of the database

2.1.4 Arcgis software

ArcGIS is a software program, used to create, display and analyze geospatial data, developed by Environmental Systems Research Institute (ESRI) of Redlands, California ArcGIS consists of three components: ArcCatalog, ArcMap and ArcToolbox ArcCatalog is used for browsing for maps and spatial data, exploring spatial data, viewing and creating metadata, and managing spatial data ArcMap is

used for visualizing spatial data, performing spatial analysis, and creating maps to show the results of your work ArcToolbox is an interface for accessing the data conversion and analysis function that come with ArcGIS ArcGIS comes in three variants: ArcView, ArcEditor, or ArcInfo, which are the low end, middle and fully configured versions of the software (Kristina, S., Francisco, O., and David, R.M.,2002)

Application of three components of ArcGIS will describe such as:

-ArcCatalog is the application that you will use to:

+ Create Geodatabases (GDB) and their various components

+ Establish coordinate systems and spatial extents for your GDB

+ Manage date in a variety of formats

- ArcMap is the application that you will to:

+View your spatial and tabular data

+To create map layouts for printing

+To query, subset, and aggregate your data

+ Do some spatial analysis

- ArcToolbox is the application that you will to: Data processing ;Data conversion ; Data management Especially application of the Toolbox can be accessed from inside ArcCatalog or ArcMap

2.1.5 Remote Sensing(RS)

a) Definition

Remote Sensing (RS) is an essential tool of land-change science because it facilitates observations across larger extents of Earth’s surface than is possible by

ground-based observations This is accomplished by use of cameras, multi-spectral scanners, RADAR and LiDAR sensors mounted on air borne and space borne platforms, yielding aerial photographs, satellite imagery, RADAR and LiDAR dataset (Ellis et al., 2013)

b) Principle of Operation of Remote sensing

Detection and distinguish of objects or surface features means detecting and recording of radiant energy reflected or emitted by objects or surface material (Fig 2.1) Different objects return different amount of energy in different bands of the electromagnetic spectrum, incident upon it This depends on the property of material (structural, chemical, and physical), surface roughness, angle of incidence, intensity,

and wavelength of radiant energy

*) Stages in Remote Sensing

•Emission of electromagnetic radiation, or EMR (sun/self- emission)

•Transmission of energy from the source to the surface of the earth, as wellas absorption and scattering

•Interaction of EMR with the earth’s surface: reflection and emission

•Transmission of energy from the surface to the remote sensor

•Sensor data output

•Data transmission, processing and analysis

Figure 2.1: Remote Sensing procedures c) Some Land Observation Satellites/Sensors

− The LandSat;

− System Pour l'Observation de la Terre (SPOT);

− Indian Remote Sensing (IRS);

− Multispectral Electro-optical Imaging Scanner(MEIS-II);

− Compact Airborne Spectrographic Imager(CASI);

− Light Detection and Ranging (Lidar);

− Marine Observation Satellite (MOS);

− Sea-viewing Wide-Field-of View Sensor (SeaWiFS);

− Forward Looking InfraRed (FLIR);

− Radio Detection And Ranging (RADAR)

2.1.6 The Landsat program

a) Landsat 5

Landsat 5 was a low Earth orbit satellite launched on March 1, 1984 to collect imagery of the surface of Earth A continuation of the Landsat Program, Landsat 5 was

jointly managed by the U.S Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) Data from Landsat 5 was collected and distributed from the USGS's Center for Earth Resources Observation and Science (EROS) Landsat 5 had a maximum transmission bandwidth of 85 Mbit/s It was deployed at an altitude of 705.3 km (438.3 mi), and it took about 16 days to scan the entire Earth

Table 2.2 : Parameters of ETM Landsat (Landsat 5)

provides two thermal bands

Table2.3: Parameters of LDCM Landsat (Landsat 8)

Bands Wavelength

Cirrus Thermal Infrared (TIR) Thermal Infrared (TIR)

it is built upon the IDL (Interactive Data Language) platform which enables ENVI functionality to be available in IDL and IDL functionality in ENVI It also makes development, customization and extension of ENVI easier than other software packages While this class uses ENVI, proficiency in using one image processing package should make it easy to translate this skills to another image processing package.(Hongxing Liu et al., 2010)

2.1.8 Brightness Temperature

2.1.8.1 The concept and the role of brightness temperature

The brightness temperature is a measurement of the radiance of the microwave radiation traveling upward from the top of the atmosphere to the satellite, expressed in units of the temperature of an equivalent black body

The brightness temperature is the fundamental parameter measured by passive microwave radiometers The brightness temperatures, measured at different microwave frequencies, are used at Remote Sensing Systems(RSS) to derive wind, vapor, cloud, rain, and SST products Despite differences in sensor frequencies, channel resolutions, instrument operation and other radiometer characteristics, RSS produces high-quality, carefully intercalibrated data, using uniform processing techniques, with a brightness temperature data record spanning multiple instruments over several decades At the bottom of this page, we include information on access to our brightness temperature data and links to more detailed information

2.1.8.2 The situation of brightness temperature in research area

Taipei city is the capital city and a special municipality of Taiwan Situated at the northern tip of Taiwan, Taipei City is an enclave of the municipality of New Taipei It

is city with developing rapidly with high industrialization and urbanization rates If status of Taipei city continuous increase urbanization and population then brightness

temperature of city will remarkably increasing

2.1.8.3 The causes of increased brightness temperature

The main cause of increase temperature is processing urbanization and industrialization Besides that, residential area expansion is one of the factors that

cause more heat reflect and raise the brightness temperature in urban area The roof and asphalt make the reflectivity occurred and cause brightness temperature and overall ambient air temperature in urban area to rise The last, the deforestation with high rate relate to the land cover changes

2.2 Practical basis

2.2.1 The research in the world

Researching Land Use/ Land Cover Analysis Using Remote Sensing and Gis, a

Case Study on Pulivendula Taluk, Kadapa District, Andhra Pradesh, India This

article has been written by T Lakshmi Prasad and G Sreenivasulu In this paper an attempt has been used Remote Sensing and GIS to study land use land cover changes, and Drainage pattern of Pulivendula Taluk, Kadapa district, Andhra Pradesh, India By using satellite images IRS- P6, LISS-III data of the study area four thematic maps such

as location, Land use/ Land cover and drainage maps were prepared It is observed that the important land use features like crop lands, barren lands or uncultivated lands, forest, built-up, soil and drainage pattern.From results obtained about proportion area

of forest , built-up, barren land in order to develop dendritic drainage pattern is there in the study area The spatial information of the surface will help in the optimal land use planning at the macro and micro level.( Lakshmi, P & Sreenivasulu et al.,2014)

Researching Using satellite data to monitor land-use land-cover change in

North-eastern Latvia has been written by Simon Foteck Fonji & Gregory N Taff This study

has been conducted about Land-use and land-cover change (LULCC) For this purpose of study shows how socio-demographic data can be integrated with satellite image data and cartographic data to analyze drivers of LULCC at multiple spatial

scales In this study the effects of geographic and demographic factors on LULCC are analyzed in northeastern Latvia using official estimates from census and vital statistics data, and using remotely sensed satellite imagery (Landsat Thematic Mapper) acquired from 1992 and 2007 The remote sensing images, elevation data, in-situ ground truth and ground control data (using GPS), census and vital statistics data were processed, integrated, and analyzed in a geographic information system (GIS) Changes in six categories of land-use and land-cover (wetland, water, agriculture, forest, bare field and urban/suburban) were studied to determine their relationship to demographic and geographic factors between 1992 and 2007 Supervised classifications were performed

on the Landsat images.( Simon, F F., & Gregory, N T et al.,2013)

Reasearching Analyzing Land Use/Land Cover Changes Using Remote

Sensing and GIS in Rize, North-East Turkey by Selçuk Reis In this study, LULCC

are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000 Image Classification of six reflective bands of twoLandsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002 The second part focused on land use land cover changes by using change detection comparison (pixel by pixel) In third part of the study, the land cover changes are analyzed according to the topographic

structure (slope and altitude) by using GIS functions.( Selçuk ,R et al.,2008)

A Land Use And Land Cover Classification System For Use With Remote Sensor Data has been written by James R Anderson, Ernest E Hardy, John T Roach,

and Richard E Witmer This study has been conducted to research for land use and land cover classification system is presented for use with remote sensor data The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country

on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order

to meet their particular needs and at the same time remain compatible with each other and the national system (James, R A., Ernest, E.H., John, T R.,, and Richard, E.W et al.,1976)

Researching Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas

Using Moderate Resolution Satellite Data: A Case Study in Central Africa by Bikash

Basnet and Anthony Vodacek Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing of the Lake Kivu region in Central Africa For this purpose developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data.Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive A

systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa These multi-temporal datasets will be a valuable baseline for land use managers in the region interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts

2.2.2 The research in Viet Nam

Applications remote sensing monitoring land urban change at Vinh City, Nghe

An Province This reaseach has been conducted at Vinh City, Nghe An Province by

Nguyen Ngoc Phi ( 2009) This study used to classification closets method to divide five class category and perform combination of remote sensing image types as Landsat (1992, 2000) and SPOT (2005) order to give results interpretation and the

simultaneous comparison of accuracy to detail between photos Applications of remote

sensing data in monitoring changes in urban land has shown very clearly the changing

in current use of urban land at Vinh city in the periods 1992-2000, 2000-2005 and

1992-2005 (Phi, N.N.et al., 2009)

Assessing Land Use and Land Cover Change: A Case of Tien Yen District, Quang Ninh Province from 2000 to 2010 The study was conducted to assess land use

and land cover change in Tien Yen district in the 2000 - 2010 period by author Nguyen Thi Thu Hien, (2013) Project has method to use SPOT images which were acquired in years 2000, 2005 and 2010 and ArcGIS (version 10.0) software to produce land use and land cover map and land use/land cover change map, with purpose to assess the land use change of the study area The object oriented approach which was applied in this research included two main steps: firstly, SPOT image was fragmented

to vary objects by eCognition software, and, secondly, the segmentation images were classified by Maximum Likelihood approach using training dataset The samples consisting of nine land use types were collected by handheld GPS device ( Hien, N.T.T et al.,2013)

Using Satellite Data for Mapping Land Cover Factor (C) in Soil Erosion

Research in Tam Nong District Phu Tho Province by Tran Quoc Vinh This research

has been conducted in Tam Nong district, Phu Tho province in 2009 Universal Soil Loss Equation (USLE) is used for identifying land cover factor (C) The C factor is derived from satellite images of Spot 5 by using two different methods The first one is conducted by combining an interpolation of land cover factor and the result of C factor from other researches The second one is based on Normalized Difference Vegetation Index (NDVI) The paper showed the advantages and disadvantage of each method and suggests that method selection should depend on both specific characteristics of research area and the study objectives (Vinh, T.Q et al.,2009)

Study land cover change in Vietnam in period 2001-2003 using MODIS 32

days composite by Nguyen Dinh Duong This study has been conducted at Viet Nam

in 2006 In this research, the author presents result on land cover mapping of Vietnam

based on MODIS 500m 32-day global composite developed by the University of Maryland Land cover classification was carried out by the GASC algorithm which was developed by the author for multitemporal remote sensing data analysis Classification scheme is kept following IGBP standards As result, a land cover map of Vietnam for the year 2001, 2002 and 2003 were established The classification result was validated using ground GPS photo database The paper has pointed out usefulness

of usage of high temporal and medium spatial resolution remote sensing data for natural resource inventory and environment monitoring in country-wide, regional and global scale The time seriers of land cover of Vietnam for the years 2001, 2002 and

2003 was used for change analysis Though it is relatively short time data series some trend of land cover change reflecting both positive and negative impact of development to the environment have been addressed in the paper.( Duong, N D et al.,2006)

Researching : Monitoring of forest cover change in Tanh Linh district, Binh

Thuan province, Vietnam by multi-temporal Landsat TM data by authors Nguyen Dinh

Duong, Kim Thoa và Nguyen Thanh Hoan (2005) Forest cover is an important indicator for environmental assessment of a territory Land cover maps that also shows forest cover can be established by different methods, with different levels of accuracy

In this paper the authors report on the application of the normalized vegetation index (NDVI) and the total reflected radiance index (TRRI) for land cover mapping with an emphasis on forest cover in the area of Tanh Linh District, Binh Thuan province, Vietnam Recently drastic clear cutting of natural forest has occurred in this area for different purposes: logging, agricultural cultivation etc The research has been

performed based on multi-temporal LANDSAT TM data (1989, 1992, 1996 and 1998) and topographical maps compiled in 1965 Change analysis has been executed based

on digital interpretation results The authors further report on a land cover classification algorithm with NDVI and TRRI indices, which enables the removal of some inaccuracies of vegetation classification by the NDVI method in the case of high resolution data.( Duong, N.D., Thoa, K.& Hoan, N.T et al.,2005)

PART III.MATERIALS AND METHODS

3.1 Materials

3.1.1 The objects and scope of research

a The objects

The land cover change map from 2004 to 2014 of Taipei city

Land surface temperature of 2004 and 2014 in Taipei city

b The scope

- The time scope: The time for research was three months, from March,20th, 2015

to June, 29th, 2015

- The spatial extent: The research areas are the Taipei city, Taiwan

3.1.2 The content of research

−Identifying the natural conditions and socio economic conditions in research area

−Determining the land cover change map between 2004 and 2014 in research area from satellite images

− Determining change temperature in period 2004 and 2014

−Analyzing the relation between land surface temperature and land cover types

in research area

−Mapping land cover and surface temperature by GIS technology

3.2 Methods build-up land cover change map in period 2004-2014

In this work, various steps were involved, ranging from data acquisition and preprocessing, image classification to the derivation of the statistical tables and the land cover change mapping The entire workflow is summarized below as follows;