北京林业大学学报2018，Vol.40Issue(2)：1-10,10.DOI:10.13332/j.1000-1522.20170284
基于SAR极化分解与Landsat数据的森林生物量遥感估测
Remote sensing quantification on forest biomass based on SAR polarization decomposition and Landsat data
摘要
Abstract
[Objective] Forest biomass is one of the important indexes to evaluate the structure, function and productivity of forest ecosystem, and accurate forest biomass estimation on regional scale has great significance in understanding the current forest status and scientific forest management. This study aims to quantify regional scale forest biomass through the polarimetric SAR and Landsat5 TM. [Method]Firstly, SAR data was polarized by polarization decomposition. Then 51 parameters from 45 polarization decomposition parameters and 6 TM bands were used as predictor variables with forest biomass W as response variables, the best model was used in the research area finally. [Result] Two methods were implemented for model construction: (1) stepwise regression, the final model includes two variables with R2 of 0. 534, predicting accuracy of 67. 51% and RMSE of 43. 21 t/ ha; (2) Optimal subset method, Bootstrap was applied to select 9 parameters. Then, we got 511 models by optimal subset method and cross-validation was used for model validation. The final model had 5 parameters(TM_band4, Neumann_delta_ mod, Neumann _ psi, TSVM _ psi, TSVM _ tau _ m3), R2 of 0. 7682, simulating accuracy of 88. 32% , simulating RMSE of 14. 98 t/ ha, test accuracy of 86. 21% , test RMSE of 19. 14 t/ ha , Mallows' Cp of 5. 2495 and AIC of 256. 5045 t/ ha. We used optimal subset method to build forest biomass estimation model and acquired forest biomass distributing map. [Conclusion] The results show that C band polarimetric SAR and optical Landsat5 TM data can get accurate estimation of forest biomass.关键词
森林生物量/SAR/极化分解/Bootstrap/最优子集Key words
forest biomass/SAR/polarization decomposition/Bootstrap/optimal subset分类
农业科技引用本文复制引用
李明泽,于欣彤,高元科,范文义..基于SAR极化分解与Landsat数据的森林生物量遥感估测[J].北京林业大学学报,2018,40(2):1-10,10.基金项目
国家自然科学基金项目(31470640). （31470640）
