基于机载P波段全极化SAR数据的森林地上生物量估测

【目的】森林生物量的空间精准量化对了解陆地碳储量、碳收支、碳平衡，以及揭示森林碳储量与全球气候变化的影响过程具有重要意义。P波段波长较长，在森林中具有更高的穿透能力，研究机载P波段SAR数据提高森林地上生物量(AGB)估测精度的可行性。【方法】以机载P波段全极化合成孔径雷达(SAR)数据和高精度激光雷达(LiDAR)数据估测的森林AGB抽样点为基础，提取20个极化SAR特征，并分别与森林AGB变化作敏感性响应情况分析。采用多元线性回归模型(MLR)、K近邻方法 (KNN)、支持向量回归(SVR)和随机森林(RF)4种估测方法，探究机载P波段SAR数据的森林AGB估测精度。【结果】在较低森林AGB(均值约45 t·hm^-2)的森林覆盖区中，P波段的同极化后向散射系数、Freeman-Durden和Yamaguchi分解中的表面和二次散射分量对森林AGB变化敏感；此外H-A-ALPHA极化分解的散射角(alpha)、拓展极化参数极化辨别率参数(PDR)也对森林AGB变化敏感。4种方法估测的森林AGB相对误差均约30%，其中MLR估测结果精度最低，估测精度为63.55%...

Forest above ground biomass estimation using airborne P band polarimetric SAR data

  Objective  Forests play an important role in carbon sequestration in terrestrial ecosystems. The spatial accurate quantification of forest biomass is of great significance to understand terrestrial carbon reserves, carbon budget, carbon balance and the resulting global climate change. Taking the advantage of the longer wavelength of P-band and higher penetration ability in the forest, the feasibility of improving the accuracy of forest above ground biomass (AGB) estimation using airborne P-band SAR data need to be studied .   Method  Based on the domestic airborne P band full polarimetric SAR data, 20 polarimetric SAR features are extracted, and were analyzed their sensitivity to change of forest AGB. Multiple linear regression model (MLR), k-nearest neighbor method (KNN), support vector regression (SVR) and random forest (RF), which were more popular forest AGB estimation models in previous studies, were used and compared in forest AGB estimation in this study.   Result  The results showed that polarimetric features including co-polarimetric backscatter coefficients, odd and double bounce scattering components extracted from Freeman-Durden and Yamaguchi decomposition methods, alpha from H-A-ALPHA decomposition method and polarization discrimination ratio (PDR), the extended polarimetric feature were sensitive to the change of forest AGB. The relative errors of estimated AGB using the four estimation methods were all about 30%, among which the accuracy of MLR estimation result was the lowest, with accurancy of 63.55% and root mean square error (RMSE) of 19.16 t·hm?2; The accuracy of RF estimation result was the highest, with Acc of 72.97% and RMSE of 15.98 t·hm?2; There is no significant difference between the accuracies between the estimated results of KNN and SVR, and the values of RMSE for them were 17.04 and 17.09 t·hm?2, respectively.   Conclusion  P-band SAR data has certain potential for estimating forest AGB. The estimation results of nonparametric method are significantly better than those of MLR. The AGB estimation accuracy of P-band is obviously affected by the level of forest AGB to be estimated, and the estimation accuracy is higher in the group with higher forest AGB level. In the study area, with an average forest AGB around 45 t·hm?2 and maximum value around 120 t·hm?2, the accurancy value for the estimated forest AGB at group with all AGB values lower than 50 t·hm?2 was lower around 6% than the value at group with all forest AGB values higher than 50 t·hm?2. [Ch, 5 fig. 3 tab. 34 ref.]