文献类型：期刊文献

中文题名：联合机载LiDAR和星载多光谱数据的森林地上生物量异速生长模型构建方法

英文题名：An allometric model method for estimating forest aboveground biomass based on airborne LiDAR and satellite multispectral data

作者：丁相元[1,2,3] 陈尔学[1,2,3] 赵磊[1,2,3] 范亚雄[1,2,3] 徐昆鹏[1,2,3] 马云梅[1,2,3]

第一作者：丁相元

机构：[1]林木资源高效生产全国重点实验室,北京100091;[2]中国林业科学研究院资源信息研究所,北京100091;[3]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：37

期号：3

起止页码：123-132

中文期刊名：自然资源遥感

外文期刊名：Remote Sensing for Natural Resources

收录：;北大核心:【北大核心2023】；

基金：国家重点研发项目“多源遥感协同森林地上生物量估测技术”(编号:2023YFF1303900)资助。

语种：中文

中文关键词：森林AGB;多源数据;异速生长模型;遥感估测

外文关键词：aboveground biomass(AGB);multi-source data;allometric model;remote sensing estimation

分类号：TP79

摘要：森林地上生物量(above ground biomass,AGB)是森林资源监测的重要指标之一,是森林碳储量的重要组成部分。形式简单且具有物理意义的森林AGB估测模型对提高森林资源监测效率具有重要的意义。文章在已有研究基础上,提出了一种联合机载LiDAR提取的高度特征、森林郁闭度以及星载多光谱植被指数的森林AGB异速生长模型估测方法。以内蒙古根河市为试验区,基于2022年获取的LiDAR数据、哨兵2A多光谱数据以及邻近时间获得的样地数据,对比分析了LiDAR高度特征和多种植被指数与森林AGB的相关性,选择最优LiDAR高度特征与植被指数应用于所提出的模型(ModelB_(N)),并与仅利用高度特征(ModelB)、高度特征与植被指数联合(ModelB_(Y))、高度特征与郁闭度联合(ModelB_(HC))3种模型进行对比。结果表明:LiDAR高度特征中,90%高度分位数(H90)与研究区森林AGB的相关性最高;所用植被指数中,核函数植被指数KNDVI_(rel)与森林AGB的相关性最高。4种模型中,ModelB_(N)模型具有最高的R_(adj^(2))值(0.78)和估测精度(83.25%)、最低的均方根误差(root mean squared error,RMSE)(15.87 t/hm^(2));ModelB_(N)模型估测结果精度优于ModelB_(HC)(R_(adj^(2))和EA分别提高0.05和1.75百分点,RMSE降低1.66 t/hm^(2)),ModelB_(Y)模型估测结果精度优于ModelB(R_(adj^(2))和EA分别提高0.03和1.19百分点,RMSE降低1.12 t/hm^(2)),说明植被指数作为指数幂的合理性;虽然ModelB_(N)模型并非所有像元的不确定性最低,但整体最优。总体来看,ModelB_(N)模型精度最高,简单高效,且有一定的物理意义,可作为一种新的森林AGB估测技术手段,为森林资源监测提供技术支撑。
Forest aboveground biomass(AGB)serves as a significant indicator for monitoring forest resources and a crucial part of forest carbon stock.AGB estimation methods,characterized by simple models and physical significance,play a significant role in improving the monitoring efficiency of forest resources.Based on previous studies,this study proposed an allometric model method for AGB estimation by integrating the height features and forest canopy closure derived from the airborne light detection and ranging(LiDAR),and the vegetation indice derived from satellite multispectral data(also referred to as ModelBN).This study investigated Genhe City in Inner Mongolia using LiDAR data and Sentinel-2A multispectral data acquired in 2022,combined with sample plot data obtained around this period.By comparatively analyzing the correlations of LiDAR-derived height features and vegetation indices with AGB,this study applied optimal LiDAR-derived height features and vegetation indices to ModelBN.Finally,this model was compared with models using only height features(ModelB),integrating both height features and vegetation indices(ModelBY),and combining height features and canopy closure(ModelBHC).The results indicate that among the LiDAR-derived height features,the 90th height percentile(H90)exhibited the highest correlation with AGB in the study area.Among the vegetation indices,the kernel normalized difference vegetation index manifested the highest correlation with AGB.Among the four models,the ModelBN achieved the highest adjusted R-square value(R_(adj)^(2),0.78),the highest estimation accuracy(EA,83.25%),and the lowest root mean square error(RMSE,15.87 t/m^(2)).The ModelBN outperformed the ModelBHC,with improvements in R_(adj)^(2) value and EA by 0.05 and 1.75%,respectively,and a reduction in RMSE by 1.66 t/hm^(2).The ModelBY outperformed the ModelB,with improvements in R_(adj)^(2) value and EA by 0.03 and 1.19%,respectively,and a reduction in RMSE by 1.12 t/hm^(2).These results demonstrate the rationality of using vegetation indices as an exponential power.Despite the failure to possess the lowest uncertainty in all pixels,the ModelBN showed the optimal performance.Overall,the ModelBN demonstrates the highest accuracy,a simple and efficient process,and certain physical significance.Therefore,the ModelBN can function as a novel technique for AGB estimation to provide technical support for forest resource monitoring.