文献类型：期刊文献

中文题名：基于GEDI、Sentinel-2和机载激光雷达的森林冠层高度反演方法

英文题名：Forest Canopy Height Inversion Method Based on GEDI,Sentinel-2 and Airborne LiDAR

作者：姬翠翠[1,5] 月亮高可[1,2,3] 李晓松[2,3] 孙斌[4]

第一作者：姬翠翠

机构：[1]重庆交通大学智慧城市学院,重庆400074;[2]可持续发展大数据国际研究中心,北京100094;[3]中国科学院空天信息创新研究院,北京100094;[4]中国林业科学研究院资源信息研究所,北京100091;[5]重庆地质矿产研究院,重庆400042

年份：2025

卷号：43

期号：4

起止页码：694-708

中文期刊名：应用科学学报

外文期刊名：Journal of Applied Sciences

收录：;北大核心:【北大核心2023】；

基金：国家自然科学基金(No.42301459,No.4221101459);中国博士后科学基金(No.2023M740418);重庆市自然科学基金面上项目(No.CSTB2023NSCQ-MSX1285,No.CSTB2022NSCQ-MSX1093)。

语种：中文

中文关键词：全球生态系统动态调查(GEDI);Sentinel-2;冠层高度;机载激光雷达

外文关键词：global ecosystem dynamics investigation(GEDI);Sentinel-2;canopy height;airborne LiDAR

分类号：S771.8

摘要：大规模监测森林冠层高度对精准估算森林碳排放和分析森林长势至关重要。本文基于全球生态系统动态调查(global ecosystem dynamics investigation,GEDI)获取的冠层高度指标、Sentinel-2影像光谱信息和ASTER GDEM地形数据,选择四川省西昌市以乔木和灌木为主的森林区域为对象,分别采用随机森林(random forest,RF)、梯度提升决策树(gradient boosting decision tree,GBDT)和多元线性回归(multivariable linear regression,MLR)算法进行森林冠层高度反演。研究发现,选用“光谱信息+植被指数+地形信息”特征参量组合反演精度最佳,且RF算法在森林冠层高度反演上的精度最高,决定系数R^(2)为0.58,均方根误差RMSE为4.78 m,估算精度EA为56%。利用RF算法反演冠层高度,采用大疆无人机获取的激光点云数据进行精度验证,结果显示R^(2)为0.52,RMSE为2.71 m,EA为85%,表明GEDI星载全波形激光雷达数据的小光斑直径和高密度光斑优势可为空间连续森林冠层高度制图提供可能,为精准掌握森林退化及恢复的长势分析提供重要理论依据。
Large-scale monitoring of forest canopy height is essential for accurate estimation of forest carbon emissions and analysis of forest growth.In this study,we integrate canopy height metrics obtained from global ecosystem dynamics investigation(GEDI),Sentinel-2 image spectral information and ASTER GDEM terrain data to estimate canopy height in a forest area dominated by trees and shrubs in Xichang City,Sichuan Province.Random forest,gradient boosting decision tree,and multivariable linear regression algorithms are employed for canopy height inversion.Algorithm validation demonstrates that the combination of spectral information,vegetation index,and terrain information yields the highest inversion accuracy.The random forest algorithm performs best,achieving a coefficient of determination R^(2) of 0.58,a root mean square error RMSE of 4.78 m,and an estimation accuracy EA of 56%.Meanwhile,we use the RF algorithm to invert the canopy height and verify the accuracy with the laser point cloud data obtained by DJI UAV,resulting in R^(2) of 0.52,RMSE of 2.71 m,and EA of 85%.Overall,this study confirms that the small spot diameter and high-density spot of GEDI spaceborne full-waveform liDAR data offers potential for spatially continuous forest canopy height mapping.The findings contribute a theoretical basis for accurately grasping the growth analysis of forest degradation and restoration.