Horizontal Geolocation Error Evaluation and Correction on Full-Waveform LiDAR Footprints via Waveform Matching

The geolocation accuracy of spaceborne LiDAR (Light Detection And Ranging) data is important for quantitative forest inventory. Geolocation errors in Global Ecosystem Dynamics Investigation (GEDI) footprints are almost unavoidable because the instability orbital parameter estimation and GNSS (Global Navigation Satellite Systems) positioning accuracy. This study calculates horizontal error multiple temporal GEDI using a waveform matching method, which compares original waveforms with corresponding simulated from airborne point clouds. results show that footprint varies 3.04 m to 65.03 m. In particular, good orbit perform better than those weak data, while nighttime daytime similarly. After removing system error, average similarity coefficient multi-temporal increases obviously low-waveform-similarity footprints, especially footprints. When effect measured threshold coefficient, method can significantly improve up 32% datasets poor effect. improvement ratio individual similarity, mean value training set test about two thirds, but variance large. Our first quantifies newest version (Version 2). Future research should focus on detail combination terrain LiDAR.