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利用机载激光雷达点云数据,结合大量实测单木结构信息,分别从样地和单木尺度估算了森林地上生物量AGB。首先,利用局部最大值单木提取算法提取了每个样地内的单木结构参数,并针对样地和单木尺度分别计算了一组激光雷达变量。然后,利用激光雷达变量和地上生物量及其两者的对数形式,从样地和单木尺度分别构建了估算模型。最后,针对两种尺度估算过程中存在的不确定性进行了详细讨论。结果表明:(1)样地和单木尺度模型估算的森林地上生物量与地面实测值都具有明显的相关性,且对数模型估算效果要优于非对数模型;(2)样地尺度模型估算效果(R2=0.84,rRMSE=0.23)明显优于单木尺度模型(R2=0.61,rRMSE=0.46);(3)按树木类型分别进行估算可以提高单木地上生物量的估算精度;(4)不论是样地还是单木尺度地上生物量估算都存在一定的不确定性,与样地尺度相比,单木尺度估算过程的不确定性更大,这种不确定性主要来自单木识别过程。
Based on the point cloud data of airborne lidar and the information of a large number of measured single-wood structures, the AGB of forest aboveground biomass was estimated from plots and single-tree scales respectively. First, the single-tree structure of each plot was extracted by using the local maximal single-tree extraction algorithm, and a set of lidar variables were calculated for the plots and single-wood scales respectively. Then, using the Lidar variables and the aboveground biomass and their logarithmic forms, the estimation models were constructed from plots and single-wood scales respectively. Finally, the uncertainties in the process of estimating two scales are discussed in detail. The results showed that: (1) The above-ground biomass estimated from the plots and single-tree scale models had obvious correlation with the measured values on the ground, and the logarithm model was superior to the non-logarithmic model in estimating the results; (2) (R2 = 0.84, rRMSE = 0.23) was significantly better than single-tree scale model (R2 = 0.61, rRMSE = 0.46); (3) Estimation by tree type could improve the estimation precision of aboveground biomass of single- 4) There are some uncertainties in the estimation of above-ground biomass both in sample plots and single-tree scales. Compared with the sample plots, the uncertainty of the single-tree scale estimation process is greater. The uncertainty mainly comes from single-tree Identify the process.