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Forest and non-forest mapping with envisat ASAR images


Ling, F; Li, Z; Chen, E; Huang, Y; Tian, X; Schmullius, C; Leiterer, Reik; Reiche, J; Santoro, M (2012). Forest and non-forest mapping with envisat ASAR images. In: dragon 2 - dragon 3 symposium, Beijing, China, 25 June 2012 - 29 June 2012, 8 S..

Abstract

Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Landsat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classification method is then proposed based on the HH to HV intensity ratio and HV images of ASAR data at single acquisition in winter. The developed methods were applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user’s accuracy and the producer’s accuracy of forest are 83.7%, 85.6% and 75.7% respectively. The results indicate that the proposed methods are promising for operational forest mapping at regional scale.

Abstract

Envisat Advanced Synthetic Aperture Radar (ASAR) dual-polarization data are shown to be effective for regional forest monitoring. To this scope, an automatic SAR image preprocessing procedure was developed using SRTM DEM and Landsat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classification method is then proposed based on the HH to HV intensity ratio and HV images of ASAR data at single acquisition in winter. The developed methods were applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user’s accuracy and the producer’s accuracy of forest are 83.7%, 85.6% and 75.7% respectively. The results indicate that the proposed methods are promising for operational forest mapping at regional scale.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:29 June 2012
Deposited On:26 Feb 2013 14:08
Last Modified:21 Nov 2017 16:35
Related URLs:http://www.dragon-symposium2012.org (Organisation)

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