The application of Remote Sensing and GIS fThe application of Remote Sensing and GIS for forest cover monitoring.or forest cover monitoring.

学　生：阿 玛 娜学　生：阿 玛 娜导 师： 刘耀林 教授 导 师： 刘耀林 教授

学 号：学 号： 200322050175200322050175

专　业：地图学与地理信息系统专　业：地图学与地理信息系统单　位：资源与环境科学学院单　位：资源与环境科学学院

Content of ThesisContent of Thesis

1. General introduction and study objectives 1. General introduction and study objectives

2. Study area and methodological approach 2. Study area and methodological approach

3. Experimental analysis and result 3. Experimental analysis and result

4. Future work and conclusions 4. Future work and conclusions

General introduction and study objectivesGeneral introduction and study objectives

Sustainable management of natural forests has been a worldwiSustainable management of natural forests has been a worldwide issue after the United Nation's Convention on Environment de issue after the United Nation's Convention on Environment and Development held in June 1992. Many organizations are inand Development held in June 1992. Many organizations are involved in development of such principle, criteria and indicators volved in development of such principle, criteria and indicators

at national and international levelat national and international level..

Furthermore, forest resource is the important material foundaFurthermore, forest resource is the important material foundation of national sustainable development. And it need to master tion of national sustainable development. And it need to master the status and change of forest resource timely for reasonable ethe status and change of forest resource timely for reasonable exploitation of forest and its renewal.xploitation of forest and its renewal.

Specific objective of this study Specific objective of this study

1. How to apply remote sensing and GIS data for forest cover 1. How to apply remote sensing and GIS data for forest cover monitoring monitoring

2. Using GIS tools to covert map projection from different 2. Using GIS tools to covert map projection from different datum datum

3. 3.    To deal the work with Image processing, image    To deal the work with Image processing, image classificationclassification

4.      To process image Enhancements/transformation, there are 4.      To process image Enhancements/transformation, there are the normalized difference index (NDVI) and the Principal the normalized difference index (NDVI) and the Principal components analysis (PCA)components analysis (PCA)

5. To determine the area of forest cover change5. To determine the area of forest cover change

Study areaStudy area

Laos is located in the heart of the Indochinese peninsular, in sLaos is located in the heart of the Indochinese peninsular, in southeast Asia, latitude 14° to 23 °north and longitude 100°to 1outheast Asia, latitude 14° to 23 °north and longitude 100°to 108°east, covered a total 236, 800 square kilometers, and count08°east, covered a total 236, 800 square kilometers, and country of nearly 6 million people. ry of nearly 6 million people.

Laos is in the tropical zone, and the weather is influenced by Laos is in the tropical zone, and the weather is influenced by monsoonsmonsoons

The study region is located in the southern part of Laos (106°3The study region is located in the southern part of Laos (106°30´ 00 - 107°00´ E, 14°40´ 00 - 15°00´ N) to be chosen as a simpl0´ 00 - 107°00´ E, 14°40´ 00 - 15°00´ N) to be chosen as a simple of studye of study

Study area (the left-administrative map, the riStudy area (the left-administrative map, the right-Landsat 7 ETM +image ght-Landsat 7 ETM +image

Data sources and review literatures Data sources and review literatures

In the study, we got some document data ,forest land cover maIn the study, we got some document data ,forest land cover map in 1997 and index map (administrative border), and Landsat p in 1997 and index map (administrative border), and Landsat ETM+ data from National Geographic Department, National ETM+ data from National Geographic Department, National Agriculture and Forestry Intension Service, and National AgriAgriculture and Forestry Intension Service, and National Agriculture and Forestry Research Institute. culture and Forestry Research Institute.

   Available DataAvailable Data

Forestcover map

in 1997

Image data2002

Map units meters meters

Spheroid Everest1830

WGS 84

System UTMZone 48

UTMZone 48

Datum Indian1960

WGS 84

The first part of the study, analysis forest land cover map in 1997, and type of forest classification. From original forest land cover map, we re-append the legend map to be clearly on training simple for image classification as following

1) Evergreen by 11, 12, 13 code ( high , medium, low cover density), 

2) Mixed (evergreen and deciduous)by17,18and19 code,high- medium-low cover density,

3) Deciduous and deciduous mosaic by 20 and 22 code,

4)Re-growth and inundated by 40 and 41 code

5) Non forest area which are included : wood, scrubland, evergreen and grassland ( by 61,62, 81, 82 code)

6) Agriculture land, barren land, Urban and built up over area ( by 91 and 92 code).

Forest land cover map in Forest land cover map in 19971997

Software application Software application

Soft package 

Used for

ArcView3.2, ArcMap 

View data, make legend, check attribute data

Arc/Infor 

Check Info. Data, Split map, map projection and conversion

ERDAS 8.6 

Image processing and image analysis

Microsoft Word 

Word processing

Methodology ApproachMethodology Approach

Image classification

Definition of classification classes

Selection of features

Sampling of training data

Classification

Accuracy assessment

Statistic data

Documentation

Experience of specialist

Forest land cover map (1: 250 000)

Supervised classification

Maximum likelihood classifier

Experimental analysis and resultExperimental analysis and result

Simple of training data using AOI tools for signature editor, Simple of training data using AOI tools for signature editor, plus with experience of specialist, and analysis forest plus with experience of specialist, and analysis forest land cover map in 1997, then we classified image of land cover map in 1997, then we classified image of 2002 into 3 classes : 2002 into 3 classes :

1) Forest, 1) Forest,

2) Non Forest 2) Non Forest

3) Water body. 3) Water body.

Image before classification and After Image before classification and After classification classification

Estimating area variation by remote sensing Estimating area variation by remote sensing

Forest cover change period 1997 to 2002

Type Changepercent %

Area/km2 Ratio % Area/km2 Ratio %

Forest 1102.61 54.92 770.53 38.13 -16.79

Nonforest 882.52 43.96 1233.8 61.05 17.09

Water 22.46 1.12 16.54 0.82 -0.3

Map in 1997 Image in Jan. 2002

Accuracy assessment Accuracy assessment

Kappa analysis (Cohen, 1960; R. G. Congalton & Mead, 1983; Kappa analysis (Cohen, 1960; R. G. Congalton & Mead, 1983; Stehman, 1996) is a currently popular multi-variable technique Stehman, 1996) is a currently popular multi-variable technique for accuracy assessment. The estimate of kappa is called KHAfor accuracy assessment. The estimate of kappa is called KHAT statistic, and gives a measure indicates if the error matrix is sT statistic, and gives a measure indicates if the error matrix is significantly different from a random result. ignificantly different from a random result.

r

ii

r

i

r

iiii

ixxN

ixxxK

1

2

1 1

Accuracy assessment Accuracy assessment Error matric and accuracy totals of layered classification with MLC

Class Reference

Classification

Number Producers

Name Totals Totals Correct AccuracyWater 14 14 12 85.71%

Forest 54 56 45 83.33%

Nonforest 111 109 99 92.16%

Reference data

Classes water Forest nonforest Total

Water 12 0 2 14Forest 1 45 10 56Nonforest 1 9 99 109Total 14 54 111

Users

Accuracy85.71%

Total : 179 179 156Overall accuracy 87.15%Kappa 0.75

80.36%

91.26%

Image analysisImage analysis

The normalized different vegetation index (NDVI) has been mThe normalized different vegetation index (NDVI) has been m

ost widely used index in global vegetation studiesost widely used index in global vegetation studies . .

The transformation of the raw satellite images using principal The transformation of the raw satellite images using principal components analysis (PCA) can result in new principal compocomponents analysis (PCA) can result in new principal component images that may be more interpretable than the original dnent images that may be more interpretable than the original data ata

4 3

4 3

NIR redNDVI

NIR red

Relation between TM bands and pixel data.Relation between TM bands and pixel data.

Flow chart of spectral enhancement on ETM imageFlow chart of spectral enhancement on ETM image

ETMB 1 2

ETMB 5 7

ETMB 3 4

PCA PCA NDVI

Final EnhancedETM Image

Data merge

PC1 PC2

Next classification for image analysis, we selected bands 1,2,5,7 Next classification for image analysis, we selected bands 1,2,5,7 from normalization stretches with NDVI ,using supervised from normalization stretches with NDVI ,using supervised

(maximum likelihood classification)(maximum likelihood classification) Distribution of Land cover image base on bands 1,2,5,7 and NDVIDistribution of Land cover image base on bands 1,2,5,7 and NDVI

Error matrix and accuracy totals of layered classification with bands 1, 2,5,7 Error matrix and accuracy totals of layered classification with bands 1, 2,5,7 and NDVIand NDVI

Class Reference

Classification

Number Producers

Name Totals Totals Correct Accuracy

Water 13 14 13 100.00%Forest 64 62 55 85.94%Nonforest 102 103 94 92.16%

Reference dataClasses water Forest nonforest TotalWater 13 0 1 14Forest 0 55 7 62Nonforest 1 9 94 104Total 14 64 102

Users

Accuracy

92.86%88.71%91.26%

Total : 179 179 162Overall accuracy 90.50%Kappa 0.83

Distribution of Land cover image base on PCA and NDVIDistribution of Land cover image base on PCA and NDVI

Error matrix and accuracy totals for Land cover classes base on PCA and Error matrix and accuracy totals for Land cover classes base on PCA and NDVINDVI

Class Reference Classification

Number Producers

Name Totals Totals Correct Accuracy

Water 13 13 13 100.00%

Forest 70 60 59 84.29%

Nonforest 96 106 95 98.96%

Nonforest 0 11 95 96Total 13 70 96

Reference data

Classes water Forest nonforest Total

Water 13 0 0 13Forest 0 59 1 60

Users

Accuracy

100.00%

98.33%

89.62%

Kappa 0.877

Total : 179 179 177Overall accuracy 93.30%

result of classification image base on PCA and NDVI and Overlaid with forest result of classification image base on PCA and NDVI and Overlaid with forest mapmap

  

Result image data after classification ( Landsat 7 ETM+ in 2Result image data after classification ( Landsat 7 ETM+ in 2002)002)

The classification of forest cover is an important The classification of forest cover is an important element in both forest resources management element in both forest resources management and scientific research issues. and scientific research issues.

By power tools of Remote sensing and GIS, the By power tools of Remote sensing and GIS, the final work we classify forest into 3 classes:final work we classify forest into 3 classes:

(1) forest high cover density, (1) forest high cover density,   (2) Forest medium, low cover, (2) Forest medium, low cover,   (3) Forest mosaic.(3) Forest mosaic.

Forest mask to classify forest into 3 classesForest mask to classify forest into 3 classes

Result of forest classification image data.Result of forest classification image data.

Future work and conclusions Future work and conclusions Forest cover types information in case of our study is extracted fForest cover types information in case of our study is extracted f

rom the Landsat 7 ETM+ image, and base on classification of irom the Landsat 7 ETM+ image, and base on classification of image, using maximum likelihood of supervised method,mage, using maximum likelihood of supervised method,

we can determine the area of forest cover change during period twe can determine the area of forest cover change during period time 5 years, the forest cover have been decrease about 17% to cime 5 years, the forest cover have been decrease about 17% to compare with the forest cover map in 1997 in that area .ompare with the forest cover map in 1997 in that area .

Forest cover were changed a lot during period 5 year. To be seeForest cover were changed a lot during period 5 year. To be seen clearly , we overlaid and compare between forest map cover in n clearly , we overlaid and compare between forest map cover in 1997 and Landsat image in 2002, mostly forest over medium, lo1997 and Landsat image in 2002, mostly forest over medium, low cover density, deciduous and deciduous mosaic have been decw cover density, deciduous and deciduous mosaic have been decreased reased

As result accuracy assessment, for the first classification, the rAs result accuracy assessment, for the first classification, the result showed overall accuracy and kappa statistics 87.15% anesult showed overall accuracy and kappa statistics 87.15% and 0.75 . For the second and third, we classified data base one d 0.75 . For the second and third, we classified data base one PCA and NDVI, the result of accuracy assessment are better thPCA and NDVI, the result of accuracy assessment are better than the first one with overall accuracy (90.50% , 0.83 ) and (93.an the first one with overall accuracy (90.50% , 0.83 ) and (93.30% , 0.87). 30% , 0.87).

  Image analysis have been effected in quality of classification Image analysis have been effected in quality of classification data. In case of our study, Water body showed clearly after prdata. In case of our study, Water body showed clearly after preferment classification data with PCA and NDVI. eferment classification data with PCA and NDVI.

谢谢各位老师！谢谢各位老师！

欢迎提出宝贵意见和建议！欢迎提出宝贵意见和建议！