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

Abstract

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