文献类型：期刊文献

中文题名：激光雷达生物量指数计算落叶松小班地上生物量

英文题名：LiDAR biomass index-based method for aboveground biomass calculation of larch subcompartments

作者：杜黎明[1,2] 庞勇[1,2]

第一作者：杜黎明

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]国家林业和草原局林业遥感与信息技术重点实验室,北京100091

年份：2025

卷号：29

期号：10

起止页码：2933-2943

中文期刊名：遥感学报

外文期刊名：NATIONAL REMOTE SENSING BULLETIN

收录：;EI(收录号：20254419440208);北大核心:【北大核心2023】；

基金：国家重点研发计划(编号:2022YFF1302100,2020YFE0200800)。

语种：中文

中文关键词：激光雷达生物量指数;LBI;机载激光雷达;单木;小班尺度;生物量

外文关键词：LiDARBiomassIndex;LBI;airborneLiDAR;individualtree;sub-compartmentlevel;biomass

分类号：S771.8;P2

摘要：激光雷达生物量指数LBI(LiDAR Biomass Index)能够基于机载激光雷达数据计算单株树木的地上生物量,在单木及样地尺度上具有较高的生物量计算精度,但其大范围生物量制图的能力尚未得到充分应用。本研究以中国北方广泛种植的落叶松树种为例,利用LBI计算森林小班内每株单木的生物量,累加得到了对应小班尺度的生物量,并结合作业设计调查数据验证其计算精度。同时,结合其他林场基于LBI的落叶松生物量模型评估不同区域相同树种模型的通用性,并与常用的变量回归法LMR(LiDAR metrics-based regression)进行对比。结果表明:LBI能够以较高精度实现小班尺度的森林生物量计算,不同区域生物量模型计算得到的结果与实测数据回归的R^(2)为0.86—0.80,相对均方根误差(relative Root Mean Square Error),rRMSE为33.51%—40.23%;LBI方法采用35株单木建模计算的生物量精度与LMR方法采用30+的样地(包含>3000株单木)建模计算的生物量精度整体相当,且LBI在不同林场的相同树种之间的通用性更强。本研究利用AGB_LBI模型进行孟家岗林场西部区域每个小班内单木生物量的计算并实现了落叶松生物量制图,激光雷达计算的生物量分布与地面调查的生物量图具有相似的趋势,二者在20 m×20 m的尺度上获取了较高的一致性(R^(2)=0.75,RMSE=1.55 t)。本研究在区域范围内验证了LBI方法估算小班尺度森林地上生物量的能力,表明其具有在大范围开展森林地上生物量估算的潜力。
The LiDAR Biomass Index(LBI)can calculate the aboveground biomass(AGB)of individual trees on the basis of airborne LiDAR data,and it has been verified to have high accuracy for biomass calculation at tree and plot levels.However,its ability to complete large-scale forest biomass mapping has not been fully explored.The aim of this research is to verify the accuracy of LBI for AGB estimation on a subcompartment scale,taking the widely planted Larix olgensis tree species in north China as an example and laying a theoretical foundation for the widespread application of this index.First,the existing tree species classification results based on hyperspectral data were used to select the point clouds of L.olgensis species in Mengjiagang Forest Farm.Second,the NSC algorithm was employed to complete the individual tree segmentation of the selected point clouds.Third,the LBI was used to calculate the forest biomass of each individual tree.Finally,with reference to the AGB_LBI biomass model of L.olgensis species constructed on the basis of 35 individual sample trees,the biomass of each individual tree was calculated,and the biomass of each subcompartment was obtained through accumulating the biomass of individual trees within the subcompartment.In this research,the calculation accuracy was verified through the silviculture survey data obtained from the local forestry department,including over 70000 individual trees.Meanwhile,the universality of LBI in estimating the biomass of the same tree species across different regions at the subcompartment level was evaluated on the basis of the existing AGB LBI models of other forest farms,and the results were compared with those of the commonly used LiDAR Metric-based Regression(LMR)methods.The results indicated that LBI can achieve forest biomass estimation at the subcompartment level with high accuracy.When individual tree samples selected from different regions were used to calibrate the AGB_LBI model,the obtained biomass values were comparable with the measured data,with R^(2) ranging from 0.86 to 0.87 and relative root-mean-square error(RMSE)ranging from 34.20% to 40.23%.The biomass results calculated from each model did not have significant differences.However,the increase in the number of sample trees used for model calibration still exerted a certain effect on the robustness and accuracy of biomass calculation.Overall,the accuracy of the LBI-based method was comparable to that of the LMR method,although the sample trees used to calibrate the AGB_LBI model only accounted for 1%of that used to calibrate the LMR model.Meanwhile,the LBI method exhibited stronger universality among the same tree species in different forest farms.The AGB_LBI model was used to calculate the biomass of each individual tree in the western region of Mengjiagang Forest Farm and complete the biomass mapping.The obtained biomass distribution presented a similar trend to the existing biomass map and was consistent with the forest subcompartment map,achieving high consistency at the scale of 20 m×20 m(R^(2)=0.75,RMSE=1.55 t).The high-precision estimation of biomass by LBI at the subcompartment scale demonstrates its potential for conducting large-scale estimation of forest AGB.Because of the difficulty in obtaining validation data,this research only verified its accuracy on the species of L.olgensis and did not conduct experiments on other tree species.Nevertheless,previous studies have shown that this method can theoretically be applied to other tree species and forest situations,which is worth further exploration.This research provides a theoretical basis for precise,large-scale,and high-precision forest biomass estimation.