遥感技术估算森林生物量的研究进展

以贡嘎山地区为研究区，选取HJ-1B CCD2和SPOT4 HRVIR为数据源，结合地面同步实测数据，分别采用植被指数法和主成分分析法对森林地上生物量进行估算，并基于交叉验证的方法对比分析了两种传感器估算贡嘎山森林地上生物量的效果：针对单一植被指数，基于比值植被指数构建的生物量反演模型明显优于其他植被指数，且HJ-1B CCD2的表现好于SPOT4 HRVIR；在联合多种植被指数建立的生物量反演模型方面，两种数据源的估算能力基本相当，交叉验证的相关系数分别为0.545 8和0.563 4，均方根误差分别为27.811 4 t·ha和27.169 6 t·ha；主成分分析法则为HJ-1B CCD2传感器的表现优于SPOT4 HRVIR。

General Review on Remote Sensing-based Biomass Estimation

The paper presents a method for estimating the aboveground biomass of forest stands using vegetation index and principle component analysis methods; and in a case study in the Mt. Gongga region, combines remote sensing data (HJ-1B CCD2 and SPOT4 HRVIR) with field measurements to evaluate the method. The accuracies of aboveground biomass estimation were assessed through the cross validation method, and comparative analysis was done for HJ-1B CCD2 and SPOT4 HRVIR sensors in order to evaluate their abilities and differences on the estimation of aboveground biomass in forest stands. The results showed that the retrieval model of aboveground biomass based on the simple ratio vegetation index performed better than other vegetation indices, and the performance of HJ-1B CCD2 was superior to SPOT4 HRVIR in a single linear regression model. As for the model of biomass estimation using multiple vegetation indices, their differences on the estimation of aboveground biomass were not apparent, according to the results of cross validation for HJ-1B CCD2 (r: 0.5458; RMSE: 27.8114 t·ha) and SPOT4 HRVIR sensors (r: 0.5634; RMSE: 27.1696 t·ha). Moreover, the performance of HJ-1B CCD2 was better than SPOT4 HRVIR for the principle component analysis method. In general, both of HJ-1B CCD2 and SPOT4 HRVIR sensors could satisfy the need for aboveground biomass estimation in Mt. Gongga region. Additionally, the results of HJ-1B CCD2 data were found to outperform SPOT4 HRVIR.