ALCSF: An adaptive and anti-noise filtering method for extracting ground and top of canopy from ICESat-2 LiDAR data along single tracks

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) is an active spaceborne remote sensing system that utilizes photon-counting LiDAR to capture highly detailed information about under-vegetation terrain and forest structure over vast spatial regions. It facilitates the accurate retrieval of terrain elevation and canopy height information, critical for assessing the global carbon budget and understanding the role of forests in climate change mitigation. However, challenges arise from the characteristics of the ICESat-2 photon-counting LiDAR data, such as their linear distribution, extensive spatial coverage, and substantial residual noise. These challenges hinder the performances of the state-of-the-art methods when applied on ICESat-2 data for extracting ground or top of canopy, while they perform well on airborne LiDAR that is featured with planar distribution, small coverage, and high signal-to-noise ratio. Consequently, this study proposes a novel algorithm termed Adaptive Linear Cloth Simulation Filtering (ALCSF), for the automated extraction of ground and top-of-canopy photons from ICESat-2 signal photons. The ALCSF algorithm innovatively introduces a cloth strip model as a reference to accommodate the distribution characteristics of ICESat-2 photons. Additionally, it employs a terrain-adaptive strategy to adjust the rigidity of the cloth strip by utilizing terrain slope information, thus making ALCSF applicable to large-scale areas with significant topographical changes. Furthermore, the proposed ALCSF addresses noise interference by simultaneously considering the movability of particles of the cloth strip model and the photon distribution during iterative adjustments of the cloth strip. The performance of the ALCSF is evaluated by comparing it with the ICESat-2 Land–Vegetation Along-Track Products (ATL08) across twelve datasets that encompass various times of day and scenes. In the results, the ALCSF exhibits notable improvements over ATL08 products, effectively reducing the root mean square error (RMSE) of ground elevation by 21.8% and canopy height by 25.8%, with superior performance in preserving terrain details. This highlights the significance of ALCSF as a valuable tool for enhancing the accuracy of ICESat-2 land and vegetation products, ultimately contributing to the estimation of the global carbon budget in future studies.