• 论文与研究报告 •

### 基于机载P-波段全极化SAR数据的复杂地形森林地上生物量估测方法

1. 中国林业科学研究院资源信息研究所 国家林业局遥感与信息技术重点开放性实验室 北京 100091
• 收稿日期:2015-03-23 修回日期:2015-12-19 出版日期:2016-03-25 发布日期:2016-04-08
• 通讯作者: 陈尔学
• 基金资助:
国家973计划"复杂地表遥感信息动态分析与建模"(2013CB733404);高分辨率对地观测系统重大专项(民用部分)"高分林业遥感应用示范系统"(21-Y30B05-9001-13/15-1)。

### Forest Above-Ground Biomass Estimation Method for Rugged Terrain Based on Airborne P-Band PolSAR Data

Feng Qi, Chen Erxue, Li Zengyuan, Li Lan, Zhao Lei

1. Key Lab. of Remote Sensing and Information Technology, State Forestry Administration Research Institute of Forest Resource Information Techniques, CAF Beijing 100091
• Received:2015-03-23 Revised:2015-12-19 Online:2016-03-25 Published:2016-04-08

Abstract: [Objective] To obtain an accurate estimation of forest above-ground biomass (AGB), the polynomial model integrating the terrain factors was presented based on the relationship of Synthetic Aperture Radar (SAR) response for forest AGB and terrain using the airborne P-band full Polarimetric SAR (PolSAR) data acquired by CASMSAR.[Method] Firstly, the slope map and the true forest AGB map over the study area were obtained as reference data using LiDAR data, and the forest AGB map was trained by the field AGB data. The systematical sampling was carried out based on the reference data to analyze the relationships between the backscattering intensity and the forest AGB and to analyze the changes of these relationships when the slope varied. Secondly, the local incidence angle was calculated from the LiDAR DEM and the orbit parameters of the airborne P-band SAR platform, and the polynomial model was built integrating the features of intensity, local incidence angle and look angle. Some of the sample plots were used to train the model parameters, and the others were performed as the validation samples. In order to avoid the contingency caused by sample size, more experiments were implemented with different sample size from 20m×20 m to 100 m×100 m.[Result] In the case of the plots with the size of 90 m×90 m, for the estimation model with the slope parameter (called as the second set of features) and for that without the slope parameter (called as the first set of features), the following quantitative technical targets were achieved. With the slope from 0°to 5°, the determination coefficients(R2) were 0.634 and 0.634 respectively, the root mean squared error(RMSE) were 12.07 t·hm-2 and 12.08 t·hm-2 respectively, the overall accuracies were 78.91% and 78.89% respectively. With the slope from 5°to 10°, the R2 were 0.524 and 0.523 respectively, the RMSE were 13.52 t·hm-2 and 13.97 t·hm-2 respectively, the overall accuracies were 80.57% and 80.52% respectively. With the slope above 10°, the R2 were 0.628 and 0.519 respectively, the RMSE were 13.16 t·hm-2 and 15.70 t·hm-2 respectively, the overall accuracies were 81.05% and 78.55% respectively. In addition, with the plot size increasing, the precisions of both methods were all improved. Especially, the accuracy of the estimation model with the slope parameter was higher than that without the slope parameter.[Conclusion] It was shown that the terrain had little effects on the intensity of the SAR data when the slope less than 10°, while it had a significant effect when the slope increases to more than 10°.The refined model involving local incidence angle could improve the accuracy, demonstrating the effectiveness and stability of the refined model. In addition, the accuracy would increase and tend to be stable with the scale enlarging regardless of the adopted model considered the effect of terrain or not, which revealed that the plot scale for evaluating the estimation model needed to be valued. The size of the sample plots should be considered for a reliable evaluation.