基于GEDI、Sentinel-2和机载激光雷达的森林冠层高度反演方法

doi:10.3969/j.issn.0255-8297.2025.04.011

Abstract

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

CLC Number:

S771.8

JI Cuicui, YUE Lianggaoke, LI Xiaosong, SUN Bin. Forest Canopy Height Inversion Method Based on GEDI, Sentinel-2 and Airborne LiDAR[J]. Journal of Applied Sciences, 2025, 43(4): 694-708.

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

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