Forest mapping using bi-aspect polarimetric SAR data in southwest China

Fengli Zhang; Maosong Xu; Zhongsheng Xia; Zi Wan; Kun Li; Xiaofang Li

doi:10.1117/12.833185

30 October 2009 Forest mapping using bi-aspect polarimetric SAR data in southwest China

Fengli Zhang, Maosong Xu, Zhongsheng Xia, Zi Wan, Kun Li, Xiaofang Li

Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74940W (2009) https://doi.org/10.1117/12.833185
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Fengli Zhang, Maosong Xu, Zhongsheng Xia, Zi Wan, Kun Li, and Xiaofang Li "Forest mapping using bi-aspect polarimetric SAR data in southwest China", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74940W (30 October 2009); https://doi.org/10.1117/12.833185

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