森林工程2026，Vol.42Issue(1)：1-10,10.DOI:10.7525/j.issn.1006-8023.2026.01.001
基于星载激光雷达数据的光子云去噪与树高提取
Photon Cloud Denoising and Forest Height Extraction Based on Satellite-Borne Laser Radar Data
摘要
Abstract
Tree height is a key parameter for assessing forest carbon storage,and satellite-borne laser radar technology provides an effective means for large-scale monitoring.The new generation of ice,cloud and land elevation satellite-2(ICESat-2)equipped with the advanced topographic laser altimeter system(ATLAS)generates a lot of noise in the pro-cess of receiving signals,and the terrain is a key factor affecting the denoising results.To address this problem,a ground slope adaptive density clustering denoising algorithm is proposed to complete the photon cloud data denoising.It-erative median filtering and dynamic residual threshold method are used to classify photon clouds and then extract tree height.The canopy height model(CHM)obtained from airborne laser radar data is used as verification data.The reli-ability of extracting tree height from ICESat-2/ATLAS global geolocated photon data(ATL03)is analyzed and evaluated from three aspects:strong and weak beams,slope,and vegetation coverage.The results show that,1)The recall rate(R),precision rate(P)and harmonic mean(F)of the proposed denoising algorithm are better than those of the differen-tial progressive gaussian adaptive denoising algorithm(DRAGANN).2)The accuracy of extracting tree height from nighttime strong beam data is the best,with a mean absolute error(MAE)of 2.49 m and a root mean square error(RMSE)of 3.03 m.3)As the slope increases,the accuracy of tree height extraction gradually decreases,and the RMSE increases from 2.25 m to 6.52 m.4)As the vegetation coverage increases,the accuracy of tree height extraction gradually decreases,and the RMSE increases from 3.06 m to 4.53 m.The results show that it is feasible to extract tree height using ATL03 photon cloud data,which can provide effective data support for studying forest growth conditions in forest areas.关键词
ICESat-2/ATL03/光子云去噪/光子云分类/树高/密度聚类/坡度自适应/森林遥感Key words
ICESat-2/ATL03/photon cloud denoising/photon cloud classification/forest height/density clustering/slope adaptivity/forest remote sensing分类
农业科技引用本文复制引用
王芳昕,邢艳秋,李苑鑫,唐杰,王德军..基于星载激光雷达数据的光子云去噪与树高提取[J].森林工程,2026,42(1):1-10,10.基金项目
国家重点研发计划项目(2023YFD2201701). （2023YFD2201701）
