DocumentCode :
2783297
Title :
Forest cover classification from MODIS images in Northeastern Asia
Author :
Anmin Fu ; Guoqing Sun ; Zhifeng Guo ; Dianzhong Wang
Author_Institution :
State Key Lab. of Remote Sensing Sci., Beijing Normal Univ., Beijing
fYear :
2008
fDate :
June 30 2008-July 2 2008
Firstpage :
1
Lastpage :
6
Abstract :
Forest ecosystem in Eastern Siberia and Northeastern China (ESNC) has been undergoing dramatic changes during the last several decades due to forest fires and massive logging. These changes affect climate dynamics, economic activity and living heritage in local region, further, to the global carbon balance and climate changes. In this paper, a 2D feature space grid split (FSGS) algorithm was developed to identify forests cover region by combined TM/ ETM+ images and MODIS datasets, due to its dark object attributes. This no-parametric algorithm was based on statistical signatures in feature space and Bayesian rule. The producer accuracy of tree cover commission can be approximately 90%, comparing with local TM/ETM+ classification results. Then, forests cover was stratified into different biomes by a decision tree classifier. and Forests cover map was respectively compared with MODIS land cover products and Global land cover 2000(GLC2000) products derived from images observed by VEGETATION (VGT) sensor on both areal and per-pixel bases.
Keywords :
decision trees; feature extraction; forestry; geophysical signal processing; image classification; statistical analysis; vegetation mapping; 2D feature space grid split algorithm; Bayesian rule; Eastern Siberia; Global land cover products; MODIS image; MODIS land cover products; Northeastern Asia; Northeastern China; TM- ETM+ images; VEGETATION sensor; biomes; climate change; climate dynamics; dark object attribute; decision tree classifier; economic activity; forest cover classification; forest ecosystem; forest fire; forests cover map; global carbon balance; living heritage; massive logging; no-parametric algorithm; statistical signature; tree cover commission; Asia; Bayesian methods; Biosensors; Classification tree analysis; Decision trees; Ecosystems; Fires; Image sensors; MODIS; Vegetation mapping;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Earth Observation and Remote Sensing Applications, 2008. EORSA 2008. International Workshop on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2393-4
Electronic_ISBN :
978-1-4244-2394-1
Type :
conf
DOI :
10.1109/EORSA.2008.4620301
Filename :
4620301
Link To Document :
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