Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 319-323, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/319/2016/
doi:10.5194/isprs-archives-XLI-B7-319-2016
 
21 Jun 2016
MAPPING TROPICAL FOREST FOR SUSTAINABLE MANAGEMENT USING SPOT 5 SATELLITE IMAGE
Huong Thi Thanh Nguyen Department of Forest resource & Environment management (Frem), Faculty of Agriculture and Forestry, Tay Nguyen University, Le Duan Str. 567, Buon Ma Thuot City, Daklak Province, Vietnam
Keywords: SPOT 5, tropical forest, stand volume, supervised classification, unsupervised classification Abstract. This paper describes the combination of multi-data in stratifying the natural evergreen broadleaved tropical forest of the Central Highlands of Vietnam. The forests were stratified using both unsupervised and supervised classification methods based on SPOT5 and field data. The forests were classified into 3 and 4 strata separably. Correlation between stratified forest classes and forest variables was analyzed in order to find out 1) how many classes is suitable to stratify for the forest in this area and 2) how closely the forest variables are related with forest classes. The correlation coefficient shows although all forest variables did have a significant correlation with the forest classes, stand volume appeared to have the strongest correlation with forest classes. These are 0.64 and 0.59 for four and three strata respectively. The results of supervised classification also show the four strata of heavily degraded forest, moderate disturbance, insignificant disturbance, and dense forest were discriminated more clearly comparing to the forest stratified into three classes. The proof is that overall accuracy of supervised classification was 86% with Kappa of 0.8 for four classes, meanwhile, these are 77% and 0.62 respectively for forest area classified into 3 classes.
Conference paper (PDF, 1483 KB)


Citation: Nguyen, H. T. T.: MAPPING TROPICAL FOREST FOR SUSTAINABLE MANAGEMENT USING SPOT 5 SATELLITE IMAGE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 319-323, doi:10.5194/isprs-archives-XLI-B7-319-2016, 2016.

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