【摘要】 星载光子计数激光雷达系统以冰云与陆地高程卫星-2 ICESat-2(Ice, Cloud and land Elevation Satellite-2)搭载的先进地形激光高度计系统ATLAS(Advanced Topographic Laser Altimeter System)为代表，能够快速获取大区域植被三维信息，已广泛用于森林参数反演，同时也存在普适性差等问题。为此，本研究以瑞士阿尔高州针阔混交森林为研究对象，从森林三维结构解析的视角出发，评估冠层水平与垂直结构特征在提升ICESat-2数据复杂林分蓄积量估测精度中的作用，并探索适用于该区域的最优模型形式，同时与仅包含传统高度统计特征的基线模型进行对比。首先，将去噪后的ICESat-2 ATLAS数据分割为100 m的估测单元，并通过质量控制，识别并去除异常单元，确保数据质量；然后，通过特征参数分组预筛选和有规则约束的全子集筛选方法，综合利用点云高度分布特征、冠高及冠高异质性特征、垂直结构特征进行森林蓄积量估测，筛选最优特征组合形式。研究结果表明，瑞士阿尔高州针阔混交森林ATLAS蓄积量估测的最优模型由冠层顶部平均高、65%密度分位数、叶面积加权冠层体积和枝叶剖面的平均值组成。十折交叉验证结果显示，该模型的精度达到平均精度为R~2=0.78,RMSE=92.48 m~3/hm~2,rRMSE=0.24。相比之下，仅包含传统特征参数的基线模型R~2=0.66,rRMSE由0.28降低至0.24，说明综合引入结构特征可将精度提高ICESat-2数据蓄积量估测精度，改善在冠层异质性较高林分中的估测表现。综上，综合利用森林三维结构特征改进基于ICESat-2数据森林蓄积量估测精度可以有效提升模型在复杂林分条件下的适用性，为大区域森林蓄积量与碳储量监测提供方法支撑。更多还原

【Abstract】 Satellite-based photon-counting LiDAR systems, represented by the Advanced Topographic Laser Altimeter System(ATLAS) onboard the Ice, Cloud, and land Elevation Satellite-2(ICESat-2), enable rapid acquisition of large-scale three-dimensional vegetation information and have been widely applied to forest parameter retrieval. However, their general applicability remains limited. To address this issue, this study focused on mixed coniferous and broadleaved forests in the canton of Aargau, Switzerland, and evaluated the role of canopy horizontal and vertical structural features in improving the accuracy of stand volume estimation from ICESat-2 data under complex forest conditions. Furthermore, we explored the optimal model form for this region and compared it against a baseline model using only conventional height-based statistical metrics. First, the denoised ICESat-2 ATLAS data were segmented into 100 m estimation units, and quality control was performed to identify and remove anomalous units, ensuring stable data quality. Next, feature grouping pre-screening combined with rule-constrained all-subset selection was applied to integrate point cloud height distribution metrics, canopy height and heterogeneity indices, and vertical structural features for stand volume estimation, yielding the optimal feature subset. Results indicated that the best-performing model for stand volume estimation in the Aargau mixed forests comprised the mean top-of-canopy height, the 65% height percentile, the leaf area–weighted canopy volume, and the mean value of the foliage profile. Ten-fold cross-validation demonstrated that this model achieved high accuracy, with an average R2 =0.78, RMSE=92.48 m~3/hm~2, and rRMSE=0.24. By comparison, the baseline model using only traditional metrics yielded an R2 =0.66 and an rRMSE reduced from 0.28 to 0.24, confirming that the incorporation of structural features substantially improved the accuracy of ICESat-2-based stand volume estimation, particularly in forests with high canopy heterogeneity. In conclusion, integrating three-dimensional structural attributes significantly enhances the applicability of ICESat-2 data for forest stand volume estimation under complex stand conditions, thereby providing methodological support for large-scale forest volume and carbon stock monitoring.更多还原