The application of Remote Sensing and GIS for forest cover monitoring. 学 生：阿 玛 娜 导 师： 刘耀林 教授

1. The application of Remote Sensing and GIS for forest cover monitoring. 学　生：阿 玛 娜 导 师： 刘耀林 教授 学 号：200322050175 专　业：地图学与地理信息系统 单　位：资源与环境科学学院

2. Content of Thesis 1. General introduction and study objectives 2. Study area and methodological approach 3. Experimental analysis and result 4. Future work and conclusions

3. General introduction and study objectives • Sustainable management of natural forests has been a worldwide issue after the United Nation's Convention on Environment and Development held in June 1992. Many organizations are involved in development of such principle, criteria and indicators at national and international level. • Furthermore, forest resource is the important material foundation of national sustainable development. And it need to master the status and change of forest resource timely for reasonable exploitation of forest and its renewal.

4. Specific objective of this study 1. How to apply remote sensing and GIS data for forest cover monitoring 2. Using GIS tools to covert map projection from different datum 3.    To deal the work with Image processing, image classification 4.      To process image Enhancements/transformation, there are the normalized difference index (NDVI) and the Principal components analysis (PCA) 5. To determine the area of forest cover change

5. Study area • Laos is located in the heart of the Indochinese peninsular, in southeast Asia, latitude 14° to 23 °north and longitude 100°to 108°east, covered a total 236, 800 square kilometers, and country of nearly 6 million people. • Laos is in the tropical zone, and the weather is influenced by monsoons • The 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 simple of study

6. Study area (the left-administrative map, the right-Landsat 7 ETM +image

7. Data sources and review literatures • In the study, we got some document data ,forest land cover map in 1997 and index map (administrative border), and Landsat ETM+ data from National Geographic Department, National Agriculture and Forestry Intension Service, and National Agriculture and Forestry Research Institute.

8. Available Data

9. 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).

10. Forest land cover map in 1997

11. 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 Software application

12. Methodology Approach

13. Experimental analysis and result Simple of training data using AOI tools for signature editor, plus with experience of specialist, and analysis forest land cover map in 1997, then we classified image of 2002 into 3 classes : 1) Forest, 2) Non Forest 3) Water body.

14. Image before classification and After classification

15. Estimating area variation by remote sensing

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

17. Accuracy assessment

18. Image analysis • The normalized different vegetation index (NDVI) has been most widely used index in global vegetation studies . • The transformation of the raw satellite images using principal components analysis (PCA) can result in new principal component images that may be more interpretable than the original data

19. Relation between TM bands and pixel data.

20. Flow chart of spectral enhancement on ETM image

21. Next classification for image analysis, we selected bands 1,2,5,7 from normalization stretches with NDVI ,using supervised (maximum likelihood classification) Distribution of Land cover image base on bands 1,2,5,7 and NDVI

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

23. Distribution of Land cover image base on PCA and NDVI

24. Error matrix and accuracy totals for Land cover classes base on PCA and NDVI

25. result of classification image base on PCA and NDVI and Overlaid with forest map

26. Result image data after classification ( Landsat 7 ETM+ in 2002)

27. The classification of forest cover is an important element in both forest resources management and scientific research issues. By power tools of Remote sensing and GIS, the final work we classify forest into 3 classes: (1) forest high cover density, (2) Forest medium, low cover, (3) Forest mosaic.

28. Forest mask to classify forest into 3 classes

29. Result of forest classification image data.

30. Future work and conclusions • Forest cover types information in case of our study is extracted from the Landsat 7 ETM+ image, and base on classification of image, using maximum likelihood of supervised method, • we can determine the area of forest cover change during period time 5 years, the forest cover have been decrease about 17% to compare with the forest cover map in 1997 in that area . • Forest cover were changed a lot during period 5 year. To be seen clearly , we overlaid and compare between forest map cover in 1997 and Landsat image in 2002, mostly forest over medium, low cover density, deciduous and deciduous mosaic have been decreased

31. As result accuracy assessment, for the first classification, the result showed overall accuracy and kappa statistics 87.15% and 0.75 . For the second and third, we classified data base one PCA and NDVI, the result of accuracy assessment are better than the first one with overall accuracy (90.50% , 0.83 ) and (93.30% , 0.87). • Image analysis have been effected in quality of classification data. In case of our study, Water body showed clearly after preferment classification data with PCA and NDVI.

32. 谢谢各位老师！欢迎提出宝贵意见和建议！