Ice,Cloud,and land Elevation Satellite-2(ICESat-2)provides effective photon-counting light detection and ranging(LiDAR)data for estimating forest height across extensive geographical areas.Although prior studies have ...Ice,Cloud,and land Elevation Satellite-2(ICESat-2)provides effective photon-counting light detection and ranging(LiDAR)data for estimating forest height across extensive geographical areas.Although prior studies have illustrated canopy conditions during leaf-on and leafoff phases may influence ICESat-2 derived forest heights,a comprehensive understanding of this effect remains incomplete.This study seeks to comprehensively assess how varying canopy conditions(leaf-on/leaf-off)affect ICESat-2 forest height retrieval and modelling.First,the accuracies of ICESat-2 terrain and canopy heights under leafon and leaf-off conditions were validated.Second,random forest algorithm was utilized to model forest height by integrating ICESat-2,Sentinel-2,and other ancillary datasets.Finally,we evaluated the influence of leaf-on and leaf-off conditions on forest height retrieval and modelling.Results reveal higher consistency between ICESat-2 and airborne LiDAR-derived terrain heights compared to the agreement between two canopy height datasets.Accuracies of ICESat-2 terrain and canopy heights are higher under leaf-off conditions in contrast to leafon conditions.Notably,the accuracies of ICESat-2 terrain and canopy heights under various conditions are closely linked to canopy cover.Furthermore,the accuracy of forest height modelling can be enhanced by combining ICESat-2 data collected during both leaf-on and leaf-off seasons with further eliminating low-quality samples.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant[numbers XDA19090130 and XDA19090124]National Key Research and Development Program of China under Grant[number 2021YFF0704600]+2 种基金National Natural Science Foundation of China under Grant[number 41901289]Youth Innovation Promotion Association CAS under Grant[number 2019130]Postgraduate Scientific Research Innovation Project of Hunan Province under Grant[number CX20201147].
文摘Ice,Cloud,and land Elevation Satellite-2(ICESat-2)provides effective photon-counting light detection and ranging(LiDAR)data for estimating forest height across extensive geographical areas.Although prior studies have illustrated canopy conditions during leaf-on and leafoff phases may influence ICESat-2 derived forest heights,a comprehensive understanding of this effect remains incomplete.This study seeks to comprehensively assess how varying canopy conditions(leaf-on/leaf-off)affect ICESat-2 forest height retrieval and modelling.First,the accuracies of ICESat-2 terrain and canopy heights under leafon and leaf-off conditions were validated.Second,random forest algorithm was utilized to model forest height by integrating ICESat-2,Sentinel-2,and other ancillary datasets.Finally,we evaluated the influence of leaf-on and leaf-off conditions on forest height retrieval and modelling.Results reveal higher consistency between ICESat-2 and airborne LiDAR-derived terrain heights compared to the agreement between two canopy height datasets.Accuracies of ICESat-2 terrain and canopy heights are higher under leaf-off conditions in contrast to leafon conditions.Notably,the accuracies of ICESat-2 terrain and canopy heights under various conditions are closely linked to canopy cover.Furthermore,the accuracy of forest height modelling can be enhanced by combining ICESat-2 data collected during both leaf-on and leaf-off seasons with further eliminating low-quality samples.
