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Abstract:The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin.

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