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精确估算森林生物量对全球碳平衡以及气候变化的研究有重要意义。以亚热带天然次生林为研究对象,借助地面实测样地数据,通过对机载LiCHy(LiDAR,CCD and Hyperspectral)传感器同时获取的高光谱和高空间分辨率数据进行信息提取和数据融合,建模反演森林生物量。首先通过面向对象分割方法进行单木冠幅提取,然后融合从高光谱数据提取的光谱特征变量和从高空间分辨率数据提取的单木冠幅统计变量,构建多元回归模型估算地上、地下生物量,最后利用地面实测生物量经交叉验证评价模型精度。结果表明,综合模型的精度(R~2为0.54—0.62)高于高光谱模型(R~2为0.48—0.57);在高光谱模型中地上生物量模型精度(R~2为0.57)高于地下生物量模型(R~2为0.48);在综合模型中地上生物量模型精度(R~2为0.62)同样高于地下生物量模型(R~2为0.54)。交叉验证结果表明,与仅使用高光谱数据(单一数据源)相比,通过集成高光谱和高空间分辨率数据的生物量反演效果有所提升,可以更加有效地估算亚热带森林生物量。
Accurate estimation of forest biomass is of great importance for the study of global carbon balance and climate change. Taking the subtropical natural secondary forest as the research object, the data of the hyperspectral and spatial resolution obtained simultaneously from LiCHy (LiDAR, CCD and Hyperspectral) sensors were extracted and fused with the help of the ground-based sample data. The modeling inversion Forest biomass. First, object-oriented segmentation was used to extract the single-wood canopy, and then the spectral characteristic variables extracted from the hyperspectral data and the single-crown statistical variables extracted from the high spatial resolution data were merged to construct the multiple regression model to estimate the aboveground and underground biomass Finally, the accuracy of the model was evaluated by cross-validation using measured biomass on the ground. The results showed that the precision of the integrated model (R 2 = 0.54-0.62) was higher than that of the hyperspectral model (R 2 = 0.48-0.57) (R ~ 2 = 0.48). The aboveground biomass model accuracy (R ~ 2 = 0.62) in the integrated model was also higher than that of the underground biomass model (R ~ 2 = 0.54). The cross-validation results show that the biomass inversion by integrating hyperspectral and high-spatial-resolution data has been improved compared to using only hyperspectral data (single data source), and the subtropical forest biomass can be more effectively estimated.