English

应用科学学报 ›› 2025, Vol. 43 ›› Issue (4): 694-708.doi: 10.3969/j.issn.0255-8297.2025.04.011

• 信号与信息处理 • 上一篇    

基于GEDI、Sentinel-2和机载激光雷达的森林冠层高度反演方法

姬翠翠1,5, 月亮高可1,2,3, 李晓松2,3, 孙斌4   

  1. 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 1000915. 重庆地质矿产研究院, 重庆 400042
  • 收稿日期:2024-06-11 发布日期:2025-07-31
  • 通信作者: 姬翠翠,副教授,研究方向为生态遥感、植被参量遥感定量反演。E-mail:jijingqiang@163.com E-mail:jijingqiang@163.com
  • 基金资助:
    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）

Forest Canopy Height Inversion Method Based on GEDI, Sentinel-2 and Airborne LiDAR

JI Cuicui1,5, YUE Lianggaoke1,2,3, LI Xiaosong2,3, SUN Bin4   

  1. 1. School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China;
    2. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China;
    3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
    4. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China 5. Chongqing Institute of Geology and Mineral Resources, Chongqing 400042, China
  • Received:2024-06-11 Published:2025-07-31

PDF

7

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R2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R2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。

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

Abstract: 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 R2 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 R2 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 degra-dation and restoration.

Key words: global ecosystem dynamics investigation (GEDI), Sentinel-2, canopy height, airborne LiDAR

中图分类号: