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Journal of Applied Remote Sensing

Forest and deforestation identification based on multitemporal polarimetric RADARSAT-2 images in Southwestern China
Author(s): Fengli Zhang; Chou Xie; Kun Li; Maosong Xu; Xuejun Wang; Zhongsheng Xia
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Paper Abstract

In the southwest of China, it is anticipated that synthetic aperture radar (SAR) will become an important tool for forest inventory because of its all-weather capabilities. The Zhazuo area in Guizhou Province of southwest China, with a typical Karst landform, was selected as the test site. Six RADARSAT-2 polarimetric images were acquired in order to analyze polarimetric backscattering behavior and temporal variation of forest and deforested area. Polarimetric decomposition was conducted, and Pauli and Freeman-Durden decomposition were demonstrated to be more suitable for identifying forest and deforestation respectively. Finally, a scheme for multitemporal polarimetric SAR data fusion was proposed, which could greatly improve image quality and make forest identification more efficient. Support vector machine classification showed that the overall accuracy for forest identification was 87.63%, and the accuracy could be enhanced to 91.49% after gamma filtering.

Paper Details

Date Published: 27 April 2012
PDF: 14 pages
J. Appl. Remote Sens. 6(1) 063527 doi: 10.1117/1.JRS.6.063527
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Fengli Zhang, Institute of Remote Sensing Applications (China)
Chou Xie, Institute of Remote Sensing Applications (China)
Kun Li, Institute of Remote Sensing Applications (China)
Maosong Xu, State Forestry Administration (China)
Xuejun Wang, State Forestry Administration (China)
Zhongsheng Xia, Forest Resource Management and Conservation Station (China)

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