the land cover mapping of dong hy district, thai nguyen province using satellite images and gis

THAI NGUYEN UNIVERSITY OF AGRICULTURE AND FORESTRY PROGRAM ADVANCED EDUCATION  Student name: Nguyen Binh Minh Student ID: 1053060032 K42 - AEP THE LAND COVER MAPPING OF DONG HY D

THAI NGUYEN UNIVERSITY OF AGRICULTURE AND FORESTRY

PROGRAM ADVANCED EDUCATION



Student name: Nguyen Binh Minh

Student ID: 1053060032

K42 - AEP

THE LAND COVER MAPPING OF DONG HY DISTRICT, THAI NGUYEN

PROVINCE USING SATELLITE IMAGES AND GIS

Supervisor: Msc Nguyen Van Hieu

Thai Nguyen 15th January, 2015

ABSTRACT

Land use/cover change mapping is one of the basic tasks for environmental monitoring and management This research was conducted to analyze the land use and land cover changes in Dong Hy district, Thai Nguyen province In recent years, a variety of

change detection techniques have been developed The data sources used in this study were Landsat 5 and Landsat 8 images taken in November 2004, and December 2013, respectively

By using ArcGIS and ENVI software and remote sensing data, a supervised

classification was performed based on fusion data from a composite image of the bands Using this output, available secondary data together with field data in order to perform a Maximum Likelihood supervised classification Six classes were classified, namely forest, water, mineral, residential, traffic, rice – crops and water With overall accuracy 96.4092% and kappa = 0.9502 in 2004 , overall accuracy 96.2690% and kappa coefficient = 0.9529 in 2013

After conducted, we have:

- Land cover map of Dong Hy district in 2004 and 2013

- Land cover changes map of Dong Hy district in period 2004 – 2013

With the results achieved, we can realize the remote sensing and GIS technology is effective method for high accuracy, cost savings in the classification and analysis of land cover changes

I would like to express my special thanks of gratitude to my teacher Msc Nguyen Van Hieu who gave me the golden opportunity to do this wonderful project, which also helped me in doing a lot of Research and i came to know about so many new things

I am really thankful to them

Secondly i would also like to thank my parents and friends who helped me a lot in finishing this project within the limited time.

I would also like to thanks the officers and staffs of the Dong Hy district who

enthusiastically communicated word experience and helped me a lot in the supply of data for my research to create conditions for I can complete this research In addition, I would like to thank family, friends and relatives who were always at my side to

encourage and help me in the learning process as well as during the time I performed research

Again, I sincerely thank!

Student Nguyen Binh Minh Thai Nguyen, January 8 ,2015

LIST OF ACRONYMS

GIS Geographic information systems

NDVI Normalized Difference Vegetation Index

RS Remote sensing

SPOT System Pour l'Observation de la Terre IRS Indian Remote Sensing

Lidar Light Detection and Ranging

MOS Marine Observation Satellite

SeaWiFS Sea-viewing Wide-Field-of View Sensor FLIR Forward Looking InfraRed

RADAR RAdio Detection And Ranging

USLE Universal Soil Loss Equation

GDP Gross Domestic Product

VND Vietnamese dong

ROI Region Of Interest

LISTS OF TABLES

Table 3 Parameters of ETM Landsat ( Landsat 5) 14 Table 4 Parameters of LDCM Landsat (Landsat 8 ): 15

Table 6: Economic development in Dong Hy district period 2011 –

2013

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Table 9: Recognizing the features on images and fields 34, 35 Table 10 Results of the accuracy evaluation in 2004 40 Table 11: Results of the accuracy evaluation in 2013 41 Table 12 Statistical fluctuations of land cover in the period 2004 -

2013

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LIST OF FIGURES

I INTRODUCTION 1

1.1 Background 1

1.1 Purposes 2

1.2 Requirements 2

1.3 Signification 2

II LITERATURE REVIEW 3

2.1 Theoretical basis 3

2.1.1 Definitions of land cover 3

2.1.1.1 Land cover 3

2.1.1.2 Normalized Difference Vegetation Index 5

2.1.2 Geographic information system (GIS) 6

2.1.2.1 Geographic information system (GIS) 6

2.1.2.2 ArcGIS software 8

2.1.3 Remote sensing (RS) 9

2.1.3.1 Remote sensing (RS) 9

2.1.3.2 The Land sat program 12

2.2 Practical basis 16

2.2.1 The research in the world 16

2.2.2 The research in Viet Nam 18

3.1 Object, scope and time of research 21

3.1.1 The objects of research 21

3.1.2 The scope 21

3.2 Content of research 21

3.3 Methodology 21

3.3.1 Data collection 21

3.3.2 Field trips method 22

3.3.3 Building the land cover changes map 23

3.3.4 Normalized difference vegetation index (NDVI) 24

3.3.5 Accuracy assessment and image processing after classified 24

3.3.6 Building map 25

IV RESULT AND DISCUSSION 26

4.1 Evaluating the natural conditions and socioeconomic in research area 26

4.1.1 Natural conditions 26

4.1.1.1 The geographic location 26

4.1.1.2 The topography and geomorphology 28

4.1.1.3 The climate and hydrology 28

4.1.1.4 Natural resources 28

4.1.2 Socioeconomic conditions 29

4.1.2.1 Economic development status 29

4.1.2.2 The Population, labor and employment 30

4.1.2.3 The culture and society 31

4.1.2.4 The infrastructure status 31

4.2 The process of current status land cover mapping 32

4.2.1 Data preparation 32

4.2.1.1 Data collection 32

4.2.1.2 Data description 33

4.3 Analyze remote sensing image, determine land cover in Dong Hy district 33

4.3.1 Image interpretation 33

4.3.2 The process of calculate NDVI 36

4.3.2.3 Remote sensing image classification 38

4.3.2.4 Evaluating the accuracy after classification 40

4.3 ArcGIS application, editting current status land cover 42

4.3.1 Building current status land cover map 42

4.3.2 Building fluctuations map 43

4.3.3.3 Analysis of fluctuation 45

V CONCLUSION AND RECOMMENDATION 47

5.1 Conclusion 47

5.2 Recommendation 48

VI REFERENCES 49

Over time, land cover is continuous change under strong impact of disasters, human – That is the economic – Social development activities Research mapping land cover using remote sensing and GIS technology helps to shorten the time compared to the built maps technologies previously and it is important contributions in the management of natural resources, assess the current state of vegetation

With these pressures, land and land cover are constantly fluctuating with the development of society This is a special resource can exploitation and use but can not increase in quantity Therefore, the monitoring, research, management and the use of natural resources is an effective and reasonable

Remote sensing technology is increasingly widely used in many sectors, fields of meteorology - hydrology, geology, from environment to agriculture - forestry - fisheries, including monitoring changes in the types of land cover with high accuracy, which can help managers have more resources to monitor land-use change This is considered as one of the solutions for the posed problems On the other hand, this method has not been tested application in the area of Dong Hy

district So, research " The land cover mapping of Dong Hy district, Thai Nguyen

province using satellite images and GIS " is performed

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1.1 Purposes

- Research overview of the land cover map, satellite images and geographic information systems (GIS)

- Research on remote sensing and GIS technology in mapping land cover work

- Research on the spectral properties of natural objects

- Develop process mapping land cover by remote sensing and GIS technology

- Assess the current state of the land cover in Dong Hy District, Thai Nguyen Province

1.2 Requirements

- Adequate the data of natural condition, socioeconomic and spatial data

- Classify and handling the data collected

- The result of evaluating the current state of the land cover

- Proficiency in using GIS software to mapping data and analyze the data

Management

b) Land cover classification:

Digital image classification is the process of assigning pixels to class Usually each pixel is treated as an individual unit composed of values in several spectral bands Unsupervised classification method is used to identify natural groups, or structure, within multispectral data Only afterwards information labels are

assigned to the resulting groups The disadvantage and limitation of these methods primarily arise from a reliance upon “natural” grouping and difficulties in

matching these groups to the informational categories that are of interest to the interpreter In addition, the interpreter limit controls over the menu of classes and their specific identities

By contrast, supervised classification method is the process of using samples of known identity (training area or training field) and extends it to the entire image Each primitive image is characterized by n observations (the values in n data channels) The samples training are vectors in an n-dimensional space (the feature space) A supervised classifier uses the distribution of the samples training for each class to estimate density functions in the feature space and to divide the space into class regions

19 Open land and other land

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44 Bare forest

45 Forest has burnt out

52 Lakes

53 Water collection tank

54 Bays and estuaries

55 Sea water

6 Wetlands 61 Wetlands have plant created forest

62 Wetlands have plant can’t created forest

63 Wetlands haven’t plant

72 Beach

(Thach Nguyen Ngoc, 2012) [20]

2.1.1.2 Normalized Difference Vegetation Index

The Normalized Difference Vegetation Index (NDVI) is an index of plant

“Greenness” or photosynthetic activity, and is one of the most commonly used vegetation indices Vegetation indices are based on the observation that different surfaces reflect different types of light differently Photosynthetically active vegetation, in particular, absorbs most of the red light that hits it while reflecting much

of the near infrared light Vegetation that is dead or stressed reflects more red light and less near infrared light Likewise, non-vegetated surfaces have a much more even reflectance across the light spectrum

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By taking the ratio of red and near infrared bands from a remotely-sensed image, an index of vegetation “greenness” can be defined The Normalized Difference Vegetation Index (NDVI) is probably the most common of these ratio indices for vegetation NDVI is calculated on a per-pixel basis as the normalized difference between the red and near infrared bands from an image:

NDVI = (NIR-RED)/ (NIR+RED)

By which NIR is the near infrared band value for a cell and RED is the red band value for the cell NDVI can be calculated for any image that has a red and a near infrared band The biophysical interpretation of NDVI is the fraction of absorbed photosynthetically active radiation

2.1.2 Geographic information system (GIS)

2.1.2.1 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 The acronym GIS is sometimes used for geographical information science or geospatial information studies to refer to the academic discipline or career of working with geographic information systems and is a large domain within the broader academic discipline of Geo-informatics What goes beyond a GIS is a spatial data infrastructure, a concept that has no such restrictive boundaries

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

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 Software: GIS software provides the functions and tools needed to store, analyze, and display geographic information A review of the key GIS software subsystems is provided above

 Data: Perhaps the most important component of a GIS is the data Geographic data and related tabular data can be collected in-house, compiled to custom specifications and requirements, or occasionally purchased from a commercial data provider A GIS can integrate spatial data with other existing data resources, often stored in a corporate DBMS The integration of spatial data (often proprietary to the GIS software), and tabular data stored in a DBMS is a key functionality afforded by GIS

 People: GIS technology is of limited value without the people who manage the system and develop plans for applying it to real world problems GIS users range from technical specialists who design and maintain the system to those who use it to help them perform their everyday work The identification of GIS specialists versus end users is often critical to the proper implementation of GIS technology

 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 Pre-processing and Manipulation

 Data editing, checking and correcting

 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

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 Interpolation, e.g 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

Database Management

 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

2.1.2.2 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

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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 Any of these versions can be used for this exercise

- ArcGIS allows:

 Create and edit data integration (integrating spatial data with attribute data)

 Query spatial data from different sources and different ways

 Display, query and analyze spatial data

 Create thematic maps and prints with high quality

- The structure and organization of data in ArcGIS

Data in ArcGIS is divided into 3 parts:

+ Vector: is a set of characteristic classes have the same system of reference

+ Raster: is a simple data file or a compressed data set from the wavelength bands of the individual spectrum or a list of values

+ TIN: contains a set of continuous triangle precisely of an area have Z values for each node

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from aircraft or satellites) or passive (e.g sunlight) when information is merely recorded

b) Principle of Operation of Remote sensing

In remote sensing, its operating principle link between electromagnetic waves

from the source and the object of interest

1 Sources of energy (A) - the first requirement for remote sensing is emitting energy sources to supply electric energy to the object of interest

2 Electromagnetic waves and atmospheric (B) - the transfer of energy from the source to the object, it will go on and interact with the atmosphere it passes

through This interaction can occur when the 2nd energy transmit from the object

to the sensor

3 The interaction with the object (C) - when energy meet the object after through the atmosphere, it interacts with objects Depending on the characteristics of the object and the electromagnetic that energy of reflection or radiation by the

different subjects

Figure 1:Remote sensing system

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4 The record the energy of sensor (D) - after the energy is scattered or emitted

from objects, a sensor to collect and record the electromagnetic wave

5 The transmission, receiving and processing (E) - the energy recorded by the sensor must be transmitted to a receiving station and processing Energy is

transmitted often in the form of electricity Receiving station will handle this energy to create images in the form of hardcopy or a number

6 The interpretation and analysis (F) - Image is processed in the receiving station will be interpreted visually or by machine classification to extract information about the object

7 Applications (G) - this is the last component in the treatment process of remote sensing technology The information is extracted from the image can be used to understand better the subject, exploring some new information or support for solving a particular problem (Tran Thong Nhat, Nguyen Kim Loi, 2009)

c) Some Land Observation Satellites/Sensors

The LandSat;

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

Indian Remote Sensing (IRS);

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)

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Geology: Structural Mapping and Terrain Analysis; Geologic Unit Mapping Hydrology: Flood delineation and Mapping; Soil Moisture

SeaIce: Ice type and Concentration; Ice Motion

Land Cover – Biomass Mapping: Land Cover and Land Use; Land Use

Change (Rural / Urban)

Mapping: Planimetry; Digital Elevation Model; Topographic and Baseline

Thematic Mapping

Oceans and Coastal Monitoring: Ocean Features: Ocean Colour and

Phytoplankton Concentration; Oil Spill Detection

2.1.3.2 The Land sat program

a) The Land sat program

The Land sat program is the longest running enterprise for acquisition of

satellite imagery of Earth On Jul y 23, 1972 the Earth Resources Technology Satellite was launched This was eventually renamed to Landsat The most recent , Landsat 8, was launched on February 11, 2013 The instruments on the Landsat satellites have acquired millions of images The images, archived in the United States an data Landsat receiving stations around the world, are a unique resource for global change research and applications in agriculture, cartography, geology, forestry, regional planning, surveillance and education, and can be viewed through the USGS 'Earth Explorer' website Landsat 7 data has eight spectral bands with spatial resolutions ranging from

15 to 60 meters; the temporal resolution is 16 days

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- Satellite chronology

Table 2:Landsat satellite system

Landsat 1 July 23,

1972

January 6, 1978 2 years, 11

months and 15 days

Originally named Earth Resources Technology Satellite 1

Nearly identical copy of Landsat 1

1982

December 14,

1993

11 years, 4 months and 28 days

Nearly identical copy of Landsat 4 Longest Earth-observing satellite

Failed to reach orbit

Landsat 6 October 5,

1993

October 5, 1993 0 days Operating with scan line

corrector disabled since May 2003

Landsat 7 April 15,

1999

Still active 15 years, 8

months and 22 days

Originally named Landsat Data Continuity Mission from launch until May

30, 2013, when NASA operations were turned over to USGS

Originally named Earth Resources Technology Satellite 1

b) Land Sat 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 3 : Parameters of ETM Landsat ( Landsat 5)

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b) The Landsat 8

The Landsat 8 satellite images the entire Earth every 16 days in an 8-day offset from Landsat 7 Data collected by the instruments onboard the satellite are available to download at no charge from GloVis, EarthExplorer, or via the LandsatLook Viewer within 24 hours of reception Landsat 8 carries two instruments: The Operational Land Imager (OLI) sensor includes refined heritage bands, along with three new bands: a deep blue band for coastal/aerosol studies, a shortwave infrared band for cirrus detection*, and a Quality Assessment band The Thermal Infrared Sensor (TIRS) sensor provides two thermal bands

Table 4 : Parameters of LDCM Landsat (Landsat 8 ):

30 Band 3 0.525 – 0.600

Cirrus Thermal Infrared (TIR) Thermal Infrared (TIR)

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multispectral channels 2-5, using Landsat 8 materials have been widely used in the

world of mapping the current state of land use and vegetation maps

2.2 Practical basis

2.2.1 The research in the world

Land Use / Land Cover Change of Delhi: A Study using Remote Sensing and GIS Techniques by Singh Bijender The change analysis was performed by post classification comparison method, comparing the data of two different sensors (Lands

at TM and LISSIII IRS P-6), at different time periods (years 1992 and 2004) The growth of Delhi measured between two time periods was based on the above data set The results showed that there was rapid change in land cover/land use It was found that there was a phenomenal change in the built-up area in watersheds, loss of forest cover and change in agriculture land Singh Bijender, 2014, Land Use / Land Cover Change of Delhi: A Study using Remote Sensing and GIS Techniques, International Research Journal of Earth Sciences [10]

Land use and land cover changes detection using remote sensing and GIS in parts of Coibatore and Tiruppur districts, Tamil Nadu, India by Pandian M In this paper an attempt has been made to study the changes in land use and land cover parts of Coimbatore and Tiruppur districts The study was carried out through Remote Sensing and GIS approach using SOI toposheets, LANDSAT imagery of

2000 and IRS-P6-LISS-III 2009 The land use/land cover classification was performed based on the Survey of India toposheets and Satellite imageries GIS software is used to prepare the thematic maps and ground truth observations were also performed to check the accuracy of the classification Pandian M,

2014, Land use and land cover changes detection using remote sensing and GIS in parts of Coibatore and Tiruppur districts, Tamil Nadu, India, Land use and land cover changes detection using remote sensing and GIS in parts of Coibatore and Tiruppur districts, Tamil Nadu, India [7]

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Researching Land Use Mapping Using Remote Sensing & GIS Techniques in Naina - Gorma Basin, Part of Rewa District, M.P, India of Vimla Singh For this study the ERDAS Imagine 8.6 computer software used to develop a land use classification using IRS 1-C, LISS III, image Unsupervised classification, ISODATA clustering method is use to classify the image and visual image interpretation approach used to delineate the land use classes The present study is focus on demarcating boundaries of different land use / land cover units from an analysis, with the help of SOI to posheet, and Satellite images on 1:50,000 scale, divides the study area into forest, open scrub, dense scrub, Agriculture (agr crop area), Rocky/Stony waste land, Sandy soil / land / patches, Settlement, River, and other water bodies Vimla Singh, 2012, Land Use Mapping Using Remote Sensing & GIS Techniques in Naina - Gorma Basin, Part of Rewa District, M.P, India, ISSN 2250-2459 [14]

An Analysis on Land Use/Land Cover Using Remote Sensing and GIS – A Case Study In and Around Vempalli, Kadapa District, Andhra Pradesh, India by G Sreenivasulu This study three thematic maps such as location map, drainage map and land use / land cover maps were prepared The land use and land cover analysis on the study area has been attempted based on thematic mapping of the area consisting of built-up land, cultivated land, water bodies, forest and uncultivated land using the satellite image The research concludes that there is a rapid expansion of built-up area Land use and land cover information, when used along with information on other natural resources, like water, soil, hydro-geomorphology, etc will help in the optimal land use planning at the macro and micro level G Sreenivasulu, 2013, Analysis on Land Use/Land Cover Using Remote Sensing and GIS – A Case Study In and Around Vempalli, Kadapa District, Andhra Pradesh, India, International Journal of Scientific and Research Publications [1]

Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey by Selçuk Reis In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems

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(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 Reis, 2008, Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey, Sensors ISSN 1424-8220 [9]

2.2.2 The research in Viet Nam

Researching Using Satellite Data for Mapping Land Cover Factor (C) in Soil Erosion Research in Tam Nong District Phu Tho Province of 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 Tran Quoc Vinh, 2010, Using Satellite Data for Mapping Land Cover Factor (C) in Soil Erosion Research in Tam Nong District Phu Tho Province [15]

Use of multi data Remote sensing data to evaluate vegetation indices changes of the land cover and some analysis of crop and rice state in red river and Cuu Long river delta by Dr Kham Duong Van Together with land cover observation data, integration

of multi date remote sensing data with several temporal and spatial resolution to

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evaluate vegetation indices, has complete capability to observate and supervise state and yield of crops The MODIS data with medium resolution provided by NASA is available in global coverage, enables to research fluctuation of land cover with multi temporal and multispectral data In this paper, we use MODIS 32 day composite of all month from 2001 to 2005 for initially calculating and assessing fluctuation of vegetation indices, such as, NDVI and VCI and analysis of crop and paddy state in Red River delta and Cuu Long River delta Kham Duong Van, 2007, Use of multi data Remote sensing data to evaluate vegetation indices changes of the land cover and some analysis of crop and rice state in red river and Cuu Long river delta [4]

Researching: Application of Thermal remote sensing on increasing of urban surface temperature with distribution of land cover types in Ho Chi Minh city by Van Tran Thi.Thermal remote sensing proved its capacity in monitoring temperature field The purpose of this study is to evaluate the use of Landsat ETM+ data for indicating temperature differences in urban areas and compare the relationships between urban surface temperature and land cover types The urban temperature distribution map and the analysis of thermal land cover relationships can be used as the reference for urban planning and the solution to head island effect reduction Van Tran Thi, 2006, Application of Thermal remote sensing on increasing of urban surface temperature with distribution of land cover types in Ho Chi Minh city; Science & Technology Development, Enviroment &Resources, Vol 9 – 2006 [13]

Estimating Biomass of the canopy of leaves by using Satellite data ALOS AVNIR – 2 by Bui Nguyen Lam Ha This paper presents the recent results in estimating biomass of the canopy of leaves in Cat Tien National Park, Lam Dong Province using ALOS AVNIR 2 image Three vegetation index were shown, including The Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI) and the fraction of Absorbed Photosynthetiaclly Active Radiation (fAPAR) Hence, estimating biomass of the canopy of leaves was calculated through LAI Experimental analyses show that there is the strong relationship between NDVI, LAI and fAPAR, in

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which NDVI plays an important role for estimating biomass of the canopy of leaves

Ha Bui Nguyen Lam, 2011, Estimating Biomass of the canopy of leaves by using Satellite data ALOS AVNIR [2]

Using MODIS satellite images to research seasonal crops, building current status and land cover changes map in Hong river delta, workshop nationwide GIS application by Long Vu Huu This research focuses on exploring the change detection

of the vegetation cover within- and between years based on time series of NDVI computed from MODIS data As a result, agricultural crops’ seasonal changes are monitored and land use and agricultural land changes are mapped for the 2008-2010 period Further, the combination with MODIS multi-temporal data in other spectral bands in order to monitor surface temperature and moisture availability is expected in order to improve the rice monitoring in the Red-River Delta and other rice-growing areas in Vietnam Long Vu Huu, 2011, Using MODIS satellite images to research seasonal crops, building current status and land cover changes map in Hong river delta, workshopnationwide GIS application [5]

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III CONTENT AND METHODOLOGY

3.1 Object, scope and time of research

3.1.1 The objects of research

Land cover and land cover change in Dong Hy district, Thai Nguyen province

3.1.2 The scope

 The spatial extent:

The research area is Dong Hy district, Thai Nguyen province

- Evaluating, checking the accuracy of the classification results

- Building NDVI map of Dong Hy district, Thai Nguyen province each year

- Building land cover map of Dong Hy district, Thai Nguyen province scale

 Collect landsat images of research area in 2004 and 2013

 Investigate the basic situation about socio –economic development, current status of land use and land changes over time in the study area:

+ Socio-economic situation

+ Documents of hydrology and climate