@article{CHEN2026, 
author = {Ming CHEN and Song CHEN and Binbin WANG and Hua SUN},
title = {The impact of spaceborne LiDAR footprint quality on canopy height extraction},
year = {2026},
journal = {Journal of Central South University of Forestry & Technology},
volume = {46},
number = {3},
pages = {197-207},
keywords = {forest canopy height, footprints, quality filtering, GEDI, random forest model},
url = {https://www.sciopen.com/article/10.14067/j.cnki.1673-923x.2026.03.018},
doi = {10.14067/j.cnki.1673-923x.2026.03.018},
abstract = {【Objective】To compare the impact of spaceborne LiDAR footprint quality under different screening conditions on forest canopy height extraction, providing a reference for the precise estimation of forest structural parameters using spaceborne LiDAR data.【Method】The research was conducted in the Harvard Forest, Worcester County, Massachusetts, USA. GEDI parameters including quality_flag, degrade_flag, sensitivity, solar_elevation, and full_power_beam were used as filtering conditions. Random forest models were built under different conditions to establish canopy height models. By analyzing the variations in model accuracy under different quality filtering conditions, the optimal screening method for spaceborne LiDAR footprint shots was determined.【Result】1) Under single-criterion filtering, the degradation flag yielded the best performance. Compared to the unfiltered data, quality filtering improved the accuracy of forest canopy height retrieval, reducing the RMSE by 6.97%; 2) Multi-criteria combination filtering further enhanced the accuracy. The optimal combination-comprising the quality flag, degradation flag, sensitivity, solar elevation angle, and full-power beam-reduced the RMSE by 20.48% compared to the unfiltered data; 3) Some combined conditions (e.g., solar elevation angle with quality flag) resulted in decreased accuracy, indicating that not all indicators are necessary for filtering; 4) Footprint loss due to filtering mainly occurred in areas with canopy heights between 18 m and 36 m; 5) The combination of quality flag, degradation flag, and sensitivity outperformed the optimal combination in regional interpolation mapping, reducing the RMSE by 4.25% compared to the best combination.【Conclusion】Footprint quality filtering improves the estimation accuracy of forest canopy height, and combined filtering methods are more beneficial than single filtering methods in obtaining high-quality data. However, the screening strategy aimed at single-point accuracy during the modeling phase is not optimal for regional-scale spatial mapping tasks. By selecting appropriate filtering conditions, more accurate forest structural parameters can be obtained, providing reliable technical support for forest resource monitoring and management.}
}