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Regional forest and non-forest mapping using Envisat ASAR data


Ling, Feilong; Li, Zengyuan; Chen, Erxue; Huang, Yanping; Tian, Xin; Schmullius, Christina; Leiterer, Reik; Reiche, Johannes; Santoro, Maurizio (2012). Regional forest and non-forest mapping using Envisat ASAR data. Yaogan Xuebao, 16(5):1101-1114.

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 Land- sat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classif ication method was then proposed based on the HH (horizontal transmit and horizontal receive) to HV (horizontal transmit and vertical receive) polarization intensity ratio and HV images of ASAR data at single acquisition time in winter. The developed method was applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user’s accuracy and the pro- ducer’s accuracy of forest were 83.7%, 85.6% and 75.7%, respectively. These results indicate that the proposed method is prom- ising 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 Land- sat TM image for geocoding in rugged terrain and smooth terrain areas, respectively. An object-oriented forest and non-forest classif ication method was then proposed based on the HH (horizontal transmit and horizontal receive) to HV (horizontal transmit and vertical receive) polarization intensity ratio and HV images of ASAR data at single acquisition time in winter. The developed method was applied to forest and non-forest mapping in Northeast China. The overall accuracy, the user’s accuracy and the pro- ducer’s accuracy of forest were 83.7%, 85.6% and 75.7%, respectively. These results indicate that the proposed method is prom- ising for operational forest mapping at regional scale.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2012
Deposited On:26 Feb 2013 14:07
Last Modified:05 Apr 2016 16:36
Publisher:Kexue Chubanshe
ISSN:1007-4619

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