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Proceedings Paper

Forest mapping using bi-aspect polarimetric SAR data in southwest China
Author(s): Fengli Zhang; Maosong Xu; Zhongsheng Xia; Zi Wan; Kun Li; Xiaofang Li
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Paper Abstract

Synthetic aperture radar (SAR) provides a powerful tool for forestry inventory because of its all-weather and all-day capabilities. In this paper forest mapping method using bi-aspect polarimetric SAR data acquired from ascending and descending path has been studied. Zhazuo forest farm in Guizhou province was selected as test site and an 8-temporal field experiment was designed to obtain bio-physical parameters and spatial structure parameters of the 12 sample plots. Then the Michigan Microwave Canopy Scattering model (MIMICS) was employed to analyze the seasonal variation of these 4 types of managed forests. Using polarimetric Radarsat 2 data, scattering mechanisms of each forest type were determined and polarimetric variables were extracted and analyzed for forest discrimination. Considering the inherent geometric distortion of SAR imaging in hilly areas, a geometric correction strategy using bi-aspect SAR images and high resolution DEM was proposed. Then support vector machines method was adopted for classification of the whole test area. Experiments show that the bi-aspect geometric strategy is useful for hilly areas especially for shadow elimination in SAR image, and polarimetric SAR data is helpful to forest mapping.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74940W (30 October 2009); doi: 10.1117/12.833185
Show Author Affiliations
Fengli Zhang, Institute of Remote Sensing Applications, CAS (China)
Maosong Xu, State Forestry Administration (China)
Zhongsheng Xia, Forest Resource Management and Conservation Station (China)
Zi Wan, Institute of Remote Sensing Applications, CAS (China)
Kun Li, Institute of Remote Sensing Applications, CAS (China)
Xiaofang Li, Institute of Remote Sensing Applications, CAS (China)


Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing

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