Processing Hyperion and ALI for forest classification

• Published in: IEEE transactions on geoscience and remote sensing 2003-06, Vol.41 (6), p.1321-1331
• Main Authors: Goodenough, D.G., Dyk, A., Niemann, K.O., Pearlman, J.S., Hao Chen, Han, T., Murdoch, M., West, C.
• Format: Article
• Language: English
• Subjects: Earth; Field study; Hyperspectral imaging; Hyperspectral sensors; Image sensors; Monitoring; Radiometry; Remote sensing; Satellite broadcasting; Sustainable development; Testing
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2003.813214

Hyperion (a hyperspectral sensor) and the Advanced Land Imager (ALI) (a multispectral sensor) are carried on the National Aeronautics and Space Administration's Earth Observing 1 (EO-1) satellite. The Evaluation and Validation of EO-1 for Sustainable Development (EVEOSD) is our project supporting the EO-1 mission. With 10% of the world's forests and the second largest country by area in the world, Canada has a natural requirement for effective monitoring of its forests. Eight test sites have been selected for EVEOSD, with seven in Canada and one in the United States. Extensive fieldwork has been conducted at four of these sites. A comparison is made of forest classification results from Hyperion, ALI, and the Enhanced Thematic Mapper Plus (ETM+) of Landsat-7 for the Greater Victoria Watershed. The data have been radiometrically corrected and orthorectified. Feature selection and statistical transforms are used to reduce the Hyperion feature space from 198 channels to 11 features. Classes chosen for discrimination included Douglas-fir, hemlock, western redcedar, lodgepole pine, and red alder. Overall classification accuracies obtained for each sensor were Hyperion 90.0%, ALI 84.8%, and ETM+ 75.0%. Hyperspectral remote sensing provides significant advantages and greater accuracies over ETM+ for forest discrimination. The EO-1 sensors, Hyperion and ALI, provide data with excellent discrimination for Pacific Northwest forests in comparison to Landsat-7 ETM+.