Forest signal detection for photon counting LiDAR using Random Forest

ICESat (The Ice, Cloud, and Land Elevation Satellite)-2, as the new generation of NASA (National Aeronautics and Space Administration)?s ICESat mission, had been successfully launched in September 2018. The sensor onboard the satellite is a newly designed photon counting LiDAR (Light Detection And Ranging) system for the first time used in space. From the currently?released airborne simulation data, it can be seen that there exist numerous noise photons scattering from the atmosphere to even below the ground, especially for the vegetation areas. Therefore, relevant research on methods to distinguish the signal photons effectively?is crucial for further forestry applications. In this paper, a machine learning based approach was proposed to detect the potential signal photons from 14 MATLAS datasets across 3 sites in the USA. We found that k-NN (k-Nearest Neighbour) distance and the reachability of the photon towards the nearby signal centre showed good stability and contributed to a robust model establishment. The relevant quantitative assessment demonstrated that the machine learning approach could achieve high detection accuracy over 85% based on a very limited number of samples even in rough terrain conditions. Further analysis proved the potential of model transferability across different sites.