Performance evaluation and improvement of ICESat-2 and GEDI forest canopy height retrievals in Northeast China

• Published in: GIScience & Remote Sensing
• Main Authors: Cancan Yang, Daoli Peng, Nan Zhang, Mingjie Chen, Weisheng Zeng, Xiangnan Sun, Longwei Li, Weitao Li
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2025-12-01
• Subjects: Forest canopy height; GEDI; ICESat-2; random forest; performance
• Online Access: https://www.tandfonline.com/doi/10.1080/15481603.2025.2497603
• ISSN: 1548-1603; 1943-7226

The advent of new-generation spaceborne Light Detection and Ranging (lidar) systems, exemplified by the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), has opened up an unprecedented opportunity for observing forest canopy structures. However, forest canopy height derived from ICESat-2 ATL08 land and vegetation products and GEDI L2A geolocated elevation and height products exhibit varying accuracy across different regions. Low data accuracy limits the broader application of these systems. Moreover, the canopy height detection abilities of the two spaceborne lidar systems in the ecologically important forests of Northeast China require further investigation. In this study, airborne lidar data were used to evaluate the performance of canopy height retrievals from ICESat-2 ATL08 and GEDI L2A, as well as their influencing factors. In addition, a canopy height improvement method, based on a random forest model, was proposed to enhance the accuracy and consistency of canopy height data derived from ICESat-2 and GEDI. The results indicate that the performance of strong beams surpassed that of weak beams in detecting canopy height, with only weak beam data collected during the day recommended for exclusion in ICESat-2 applications. In contrast, for the GEDI mission, the advantages of the power beam were not pronounced, and its performance was better during the day than at night. Compared to ICESat-2, GEDI exhibited lower accuracy in canopy height detection under nighttime conditions or in evergreen needle-leaved forests, but showed greater sensitivity to slope. Moreover, the proposed method increased the coefficient of determination (R2) for ICESat-2 canopy height accuracy from 0.53 to 0.82, and reduced the root mean square error (RMSE) from 3.98 m to 2.00 m. Similarly, the R2 for GEDI improved from 0.52 to 0.80, while RMSE decreased from 4.45 m to 2.41 m. The consistency between ATL08 and GEDI L2A was also improved, with the RMSE reduced by 3.14 m. The findings of this study could provide valuable guidance for the selection and utilization of the two spaceborne lidar data. Canopy height data derived from the improved strategy may enable new opportunities for forest canopy height mapping in Northeast China and support further applications, such as the quantification of aboveground carbon stocks in forests.