基于星载ICESat-2/ATLAS数据的森林地上生物量估测

为探讨星载激光雷达数据ICESat-2（Ice，Cloud，and land Elevation Satellite-2）在山地森林地上生物量（Aboveground Biomass，AGB）的估测可行性和方法。以ATLAS（Advanced Terrain Laser Altimeter System）光子点云数据为主要信息源，以滇西北典型山地香格里拉为研究区，结合地面54块实测生物量遥感样地，在前期进行点云数据去噪、分类预处理基础上，对研究区74 873个林地光斑进行冠层参数及地形因子的提取（共计53个变量），采用非参数模型随机森林回归和超参数优化后的随机森林进行建模，以均方根误差（Root Mean Square Error，RMSE）、决定系数（R2）、总体估测精度（P1）作为模型的评价指标，建立研究区AGB模型。研究结果表明：1）分析以ICESat-2/ATLAS提取的冠层参数、地形因子与生物量的相关性可知，冠层光子总数与生物量具有极显著相关性（P<0.01），基于陆地卫星的乔木冠层百分比、冠层光子比率、坡度、光子总数、表观反射率与生物量具有显著相关性（0.01 < P <0.05），可作为山地森林生物量遥感模型参数变量；2）地形因子对ICESat-2/ATLAS光斑遥感建模具有一定的影响，地形坡度因子对模型的贡献率为13.97%，小于基于陆地卫星的乔木冠层百分比、表观反射率与冠层光子总数对模型的贡献率。未加入地形坡度因子的传统的随机森林回归模型R2=0.90、RMSE=11.90 t/hm2、P1为80.06%；加入地形坡度因子后模型精度为：R2=0.91、RMSE=11.01 t/hm2、P1=81.30%；3）进行超参数优化的随机森林回归模型精度明显高于传统的随机森林回归模型，优化后的随机森林模型精度R2为0.93、RMSE为10.13 t/hm2、P1为83.31%，可用来进行山地森林生物量估测。估测的光斑点总生物量为1.32×105 t，光斑点平均生物量为77.41 t/hm2。参数优化后的随机森林构建的山地森林地上生物量模型拟合度精度较高，模型决定系数在0.9以上，估测精度在82%以上，表明ICESat-2在山地提取的光斑参数进行AGB反演具有可行性；ICESat-2光斑点生物量的空间分布表明，高生物量的光斑点主要分布在研究区北部，存在分布不均、区域差异较大的现象，与2021年研究区蓄积量空间分布具有一致性，可用来进行森林地上生物量估测。

Remote sensing estimation of forest above-ground biomass based on spaceborne lidar ICESat-2/ATLAS data

Abstract: In order to evaluate the feasibility of the remote sensing estimation using spaceborne Lidar ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) data for the forest Aboveground Biomass (AGB) in the mountains region, the Random Forest Regression (RFR) model was conducted by combining the Advanced Terrain Laser Altimeter System (ATLAS) photon point cloud data and 54 sample plots in Shangri-La, a typical mountain area in northwest Yunnan, Southwest China. On the basis of the data denoising and classification, the 50 canopy parameters and 3 topographic factors of 74 873 footprints were extracted. A biomass model was established with 53 parameters as the independent variables after the hyper-parametric optimizing RF, and the biomass data was collected from 54 remote sensing plots to serve as dependent variables. The Root Mean Square Error (RMSE), coefficient of determination (R2) and overall estimation accuracy (P1) were used to evaluate the model accuracy. The results showed that: 1) There was the highest significant correlation of the number canopy photons parameters with the forests aboveground biomass (P<0.01) by a correlation analyzing between the 53 footprint parameters and footprints biomass. The other parameters, landsat percentage canopy, canopy photon rate, slope, number of photons and apparent surface reflectance were significantly correlated with biomass (0.01