Ground Photon Extraction From Photon-Counting LiDAR Data Using Adaptive Cloth Simulation With Terrain Index

Photon-counting light detection and ranging (LiDAR) Ice, Cloud, Land Elevation Satellite-2 (ICESat-2) enables the drafting of global elevation maps. However, vegetation cover, terrain undulation, residual noise in signal photons substantially reduce accuracy ground photon extraction. Existing extraction algorithms do not consider factors influencing extraction, threshold setting lacks a theoretical basis. This study proposed photon-extraction algorithm with scenario adaptability. First, cloth simulation (CS) was adapted index (TI) to extract photons; based on this, breakage concept remove noise. We tested Denali National Park compared its results those other algorithms. The showed that TI robust consistent actual terrain; adaptive CS achieved best precision under different canopy heights terrains. mean absolute error (MAE) root square (RMSE) extracted were 0.95 3.41 m, respectively. provides solution estimate using photon-counting LiDAR data.