文献类型：期刊文献

英文题名：Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data

作者：Zhang, Haibo[1] Zhu, Jianjun[1] Wang, Changcheng[1] Lin, Hui[2] Long, Jiangping[2] Zhao, Lei[3] Fu, Haiqiang[1] Liu, Zhiwei[1]

第一作者：Zhang, Haibo

通信作者：Zhu, JJ[1]

机构：[1]Cent S Univ, Sch Geosci & Infophys, Changsha 410083, Hunan, Peoples R China;[2]Cent South Univ & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2019

卷号：10

期号：3

外文期刊名：FORESTS

收录：;EI(收录号：20191506747989);Scopus(收录号：2-s2.0-85063860437);WOS:【SCI-EXPANDED(收录号:WOS:000464458000002)】；

基金：This research was funded by the National Natural Science Foundation of China (No. 41820104005, 41531068, 41842059 and 41671356), and the Innovation Foundation for Postgraduate of Central South University, China (No. 2017zzts179).

语种：英文

外文关键词：forest growing stock volume (GSV); full polarimetric SAR; subtropical forest; topographic effects; environment effects

摘要：Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R-2 value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m(3)/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m(3)/ha, and the mean value was 135.759 m(3)/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.