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Proceedings Paper

Forest stand mapping with data from Hyperion, ALI, and ETM
Author(s): Bingxiang Tan; Zengyuan Li; Erxue Chen; Yong Pang
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Paper Abstract

The EO-1 spacecraft, launched November 21, 2000 into a sun synchronous orbit behind Landsat 7, hosts advanced technology demonstration instruments, whose capabilities are currently being assessed by the user community for future missions. A significant part of the EO-1 program is to perform data comparisons between Hyperion, ALI and Landsat 7 ETM+. In this paper, a comparison of forest classification results from Hyperion, ALI, and ETM+ of Landsat-7 are provided for Wangqing Forest Bureau, Jilin Province, and Northeast of China. The data have been radiometrically corrected and geometrically resampled. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 129 channels to 15 features. Classes chosen for discrimination included Larch, Oak, Birch, Popular, Young tree, mixed forest, Grassland and Shrub. Classification accuracies by sensors for classes in the demonstration area were: Hyperion 88.89%, ALI 85.19%, and ETM+ 77.78%. The results shows: Hyperion classification results were the best, ALI's were much better than ETM+. Therefore, we can consider that hyper spectral remote sensing provides significant advantages and greater accuracies over ETM+ for forest discrimination. The EO-1 sensors, Hyperion and ALI, provide data with better discrimination for Northeast forests of China in comparison to Landsat-7 ETM+.

Paper Details

Date Published: 9 June 2006
PDF: 8 pages
Proc. SPIE 6200, Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China, 62000H (9 June 2006); doi: 10.1117/12.681708
Show Author Affiliations
Bingxiang Tan, Chinese Academy of Forestry (China)
Zengyuan Li, Chinese Academy of Forestry (China)
Erxue Chen, Chinese Academy of Forestry (China)
Yong Pang, Chinese Academy of Forestry (China)

Published in SPIE Proceedings Vol. 6200:
Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China
Qingxi Tong; Wei Gao; Huadong Guo, Editor(s)

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