DocumentCode
479808
Title
Automated Estimation of Vegetation Fraction Based on Landsat TM/ETM+ Imagery
Author
Yang, Shengmei ; Zhang, Qiuwen ; Li, Wenbo
Author_Institution
Hubei Key Lab. of Digital Watershed Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan
Volume
1
fYear
2008
fDate
12-14 Dec. 2008
Firstpage
891
Lastpage
894
Abstract
Vegetation fraction is mainly associated with the living environment of human being. Forest canopy density (FCD) model and sub-pixel model are applied to automatically estimate the vegetation fraction in Qingjiang river basin. FCD model uses the vegetation bare-soil shadow index (VBSI). The sub-pixel model method applies the normalized difference vegetation index (NDVI) based on the dimidiate pixel algorithm. The vegetation fraction of Qingjiang river area is estimated based on the Landsat enhanced thematic mapper (ETM+) imagery with the ENVI 4.0 and the EARDAS 8.7 image software. Results indicate that the estimate accuracy of two models exceeds 75%. Both the FCD model and the sub-pixel model are reliable to extract the vegetation fraction. The information of this study is useful for further study on the vegetation fraction automated estimation, especially in mountainous area.
Keywords
satellite communication; vegetation mapping; EARDAS 8.7 image software; ENVI 4.0; FCD model; Landsat TM-ETM+ imagery; Qingjiang river basin; automated vegetation fraction estimation; dimidiate pixel algorithm; enhanced thematic mapper; forest canopy density model; normalized difference vegetation index; subpixel model; vegetation bare-soil shadow index; Biological system modeling; Bismuth; Computer science; Infrared spectra; Large-scale systems; Remote sensing; Rivers; Satellites; Soil; Vegetation mapping; Qingjiang River Basin; automated estimation; image-processing; landsat TM/ETM+; remote sensing; vegetation fraction; vegetation index;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location
Wuhan, Hubei
Print_ISBN
978-0-7695-3336-0
Type
conf
DOI
10.1109/CSSE.2008.1337
Filename
4721893
Link To Document