Diagnosing GEDI Canopy Height Errors in Steep Mountainous Forests: Ground Elevation Representation and Geolocation Uncertainty

Spaceborne LiDAR provides essential observations of forest vertical structure, yet canopy height retrievals remain vulnerable to terrain-related errors in steep mountainous forests, where terrain heterogeneity complicates attribution of error to footprint geolocation and ground elevation representation. To distinguish these effects, we used 1 m airborne laser scanning (ALS)-derived digital terrain and canopy height models (DTM and CHM) as local references in Jiuzhaigou, China. A 2 × 2 diagnostic design independently varied footprint geolocation and ground reference across four slope classes. For 2102 strictly filtered footprints, geolocation refinement slightly increased RMSE from 13.36 to 13.47 m, whereas median-based ground reference replacement reduced RMSE to 9.98 m, a 25.3% reduction. The GEDI-implied ground was closest to the footprint median below 35° but shifted toward P20–P30 in the steepest terrain. Vertical reference sensitivity showed that the exact ground-diagnostic optimum shifted between P20 and P30, whereas the footprint median consistently minimized corrected-RH98 RMSE against ALS CHM P98. The ATL03 comparison likewise showed substantially improved canopy height agreement when ALS DTM replaced Copernicus DEM as the terrain support input under otherwise identical photon processing. These results identify ground elevation representation, rather than the tested horizontal geolocation refinement, as the dominant terrain-related factor shaping GEDI–ALS canopy height disagreement in steep terrain and show that ground diagnosis and RH98 correction require different terrain-reference choices.

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Publication Details

Journal
Forests
Published
2026-09-11
DOI
https://doi.org/10.3390/f17091087
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
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article

Diagnosing GEDI Canopy Height Errors in Steep Mountainous Forests: Ground Elevation Representation and Geolocation Uncertainty

Zhengyuan Qin, Yanchao Gu, Xiaohong Wu, Fuqiang Shen et al.
Forests
Remote Sensing and LiDAR Applications
article

Diagnosing GEDI Canopy Height Errors in Steep Mountainous Forests: Ground Elevation Representation and Geolocation Uncertainty

Zhengyuan Qin, Yanchao Gu, Xiaohong Wu, Fuqiang Shen, Xiaohai He
article en

Abstract

Spaceborne LiDAR provides essential observations of forest vertical structure, yet canopy height retrievals remain vulnerable to terrain-related errors in steep mountainous forests, where terrain heterogeneity complicates attribution of error to footprint geolocation and ground elevation representation. To distinguish these effects, we used 1 m airborne laser scanning (ALS)-derived digital terrain and canopy height models (DTM and CHM) as local references in Jiuzhaigou, China. A 2 × 2 diagnostic design independently varied footprint geolocation and ground reference across four slope classes. For 2102 strictly filtered footprints, geolocation refinement slightly increased RMSE from 13.36 to 13.47 m, whereas median-based ground reference replacement reduced RMSE to 9.98 m, a 25.3% reduction. The GEDI-implied ground was closest to the footprint median below 35° but shifted toward P20–P30 in the steepest terrain. Vertical reference sensitivity showed that the exact ground-diagnostic optimum shifted between P20 and P30, whereas the footprint median consistently minimized corrected-RH98 RMSE against ALS CHM P98. The ATL03 comparison likewise showed substantially improved canopy height agreement when ALS DTM replaced Copernicus DEM as the terrain support input under otherwise identical photon processing. These results identify ground elevation representation, rather than the tested horizontal geolocation refinement, as the dominant terrain-related factor shaping GEDI–ALS canopy height disagreement in steep terrain and show that ground diagnosis and RH98 correction require different terrain-reference choices.

ForestsVol. 17(9)
Southwest Petroleum University (CN), Sichuan University (CN), Chinese Academy of Surveying and Mapping (CN), Nanjing Surveying and Mapping Research Institute (China) (CN), Wuhan Institute of Technology (CN)
Life in Land
Openalex Percentile: Top 18%
Remote Sensing and LiDAR Applications
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