基于ICESat-2/ATLAS数据结合克里格插值进行区域尺度森林LAI估测

Regional-scale forest LAI estimation based on ICESat-2/ATLAS data combined with Kriging interpolation

森林叶面积指数(leaf area index,LAI)是探究植被与气候相互作用关系的关键参数.为更加科学、高效获取森林LAI空间格局信息,研究基于星载光子计数激光雷达ICESat-2/ATLAS数据进行大尺度森林LAI估测,以探讨星载光子数据在山地森林LAI建模中的潜力和可行性.以香格里拉市为试验区,对试验区有林地光子点云数据进行去噪、分类等预处理.随后提取各个林地光斑的特征因子属性,并结合47块森林LAI样地实测数据,分别采用逐步回归和随机森林回归模型构建了基于研究区光斑尺度的森林LAI模型.为实现研究区森林LAI的连续估测,研究对经优选的光斑特征因子进行克里格插值,形成其各自属性值的栅格图层,并利用LAI最佳模型进行研究区森林LAI的整体估测.结果表明,Landsat percent,Canopy photon rate,Minimum canopy height和Number of canopy photons等4个光斑特征因子与LAI样地实测值呈显著相关,可作为LAI的建模因子.随机森林模型对研究区LAI的预测能力优于逐步回归模型,其R^(2)和RMSE分别为0.557和0.175,为研究区森林LAI的估测模型.变异函数结构分析显示,指数模型对Landsat percent、Canopy photon rate和Minimum canopy height等3个因子的变异特征的拟合效果较好,其R^(2)分别为0.89,0.78和0.77,而Number of canopy photons的最优理论变异函数模型为球状模型,其R^(2)为0.68.4个LAI模型的光斑特征因子的变异程度仅为6%-12%,块金效应微弱,空间自相关性显著,可对其进行克里格插值.研究为基于星载光子点云数据反演区域森林LAI提供了方法示范,是以较小成本实现大尺度森林LAI快速反演的一个思路.

The leaf area index(LAI)of forests is a key parameter for investigating the interaction between vegetation and climate.In order to obtain forest LAI spatial patterns in a more scientific and efficient manner,this study conducted large-scale forest LAI estimation based on the satellite-based photon-counting laser altimeter data from ICESat-2/ATLAS.The potential and feasibility of using satellite-based photon data for modeling LAI in mountainous forests were explored.Shangri-La City was selected as the study area,and preprocessing techniques such as denoising and classification were applied to the forest photon point cloud data in the study area Subsequently,the feature factors of each forest patch were extracted,and using 47 measured forest LAI plots forest LAI models at the patch scale in the study area were constructed using stepwise regression and random forest regression models.To achieve continuous estimation of forest LAI in the study area,Kriging interpolation was applied to the selected optimal patch feature factors to generate raster layers of their respective attribute values The overall estimation of forest LAI in the study area was performed using the best LAI model.The results showed that four patch feature factors,namely Landsat percent,Canopy photon rate,Minimum canopy height,and Number of canopy photons,were significantly correlated with the measured LAI values and could be used as modeling factors for LAI.The random forest model outperformed the stepwise regression model in predicting LAI in the study area,with R^(2) and RMSE values of 0.557 and 0.175,respectively,serving as the estimation model for fores LAI in the study area.The analysis of the variation function structure indicated that the exponential model provided a good fit to the variation characteristics of Landsat percent,canopy photon rate,and minimum canopy height,with R^(2) values of 0.89,0.78,and 0.77,respectively.The optimal theoretical variation function model for the number of canopy photons was a spherical model,with an R^(2) value of 0.68.The variation of the patch feature factors in the four LAI models ranged from 6%to 12%,with weak nugget effects and significant spatial autocorrelation,which allowed for Kriging interpolation.This study provides a method demonstration for inverting regional forest LAI based on satellite-based photon point cloud data,presenting a cost-effective approach for rapid large-scale fores LAI retrieval.