基于机载P波段全极化SAR数据的森林地上生物量估测  

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

作　　者：姬永杰[1,2] 杨丛瑞 张王菲 曾鹏 张甫香 屈亚妮 JI Yongjie;YANG Congrui;ZHANG Wangfei;ZENG Peng;ZHANG Fuxiang;QU Ya’ni(School of Geography and Ecotourism,Southwest Forestry University,Kunming 650224,Yunnan,China;Southwest Research Center for Eco-civilization,National Forestry and Grassland Administration,Southwest Forestry University,Kunming 650224,Yunnan,China;Surveying and Mapping Geographic Service Center,Honghe 661199,Yunnan,China;College of Forestry,Southwest Forestry University,Kunming 650224,Yunnan,China)

机构地区：[1]西南林业大学地理与生态旅游学院,云南昆明650224 [2]西南林业大学国家林业和草原局西南生态文明研究中心,云南昆明650224 [3]云南省红河州测绘地理信息服务中心,云南红河661199 [4]西南林业大学林学院,云南昆明650224

出　　处：《浙江农林大学学报》2022年第5期971-980,共10页Journal of Zhejiang A&F University

基　　金：国家自然科学基金资助项目(32160365,31860240,42161059);云南省万人计划青年拔尖人才项目(80201444)。

摘　　要：【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%,均方根误差(RMSE)为19.16 t·hm^(-2);RF估测结果精度最高,估测精度为72.97%,RMSE为15.98 t·hm^(-2);KNN和SVR估计结果差别不明显,RMSE分别为17.04和17.09 t·hm^(-2)。【结论】P波段SAR数据对估测森林AGB具有一定潜力,3种非参数方法的估测结果明显优于MLR参数方法。此外,P波段的森林AGB估测精度受到待估森林AGB水平高低的影响明显,在森林AGB水平较高的分组中估测精度较高。在森林AGB均值为45 t·hm^(-2),最大值为120 t·hm^(-2)的森林覆盖区,以50 t·hm^(-2)将森林AGB样点分为2组时,高森林AGB组的估测精度高出低AGB组约6%。[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 e

关 键 词：P波段 森林地上生物量 合成孔径雷达(SAR) 极化 

分 类 号：S757[农业科学—森林经理学]