Forest Canopy Height Extraction Method Based on ICESat-2/ATLAS Data

Ice, cloud, and land elevation satellite (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) multibeam micropulse photoncounting light detection and ranging (LiDAR) can be effectively applied to extract forest canopy height. However, the ICESat-2/ATLAS photon point cloud interfered with the signal-to-noise ratio (SNR), fraction vegetation coverage (FVC), and terrain slope. The main challenge of this research is to extract high-precision canopy heights. Therefore, this article improves the canopy height extraction method based on the ICESat-2/ATL08 theoretical algorithm. First, an adaptive filter, Threshold Segmentation based on Spatial Clustering and Bimodal Reconstruction (TS-SCABR), is proposed, which can adapt to different SNR scenarios. Then, combined with the gradient method, the discontinuous data are detrended in sections to eliminate the edge mutation problem of the detrended data. Based on the detrended data, the iterative filtering algorithm of the local terrain is employed to fit the ground curve, and the mutation detection and empirical mode decomposition (EMD)-digital smoothing polynomial (DISPO) filtering remove the pseudoground photons to identify the data of ground and nonground photons accurately. Finally, the percentile statistics method is utilized to extract the canopy-top photons from the nonground photons according to their elevation difference. The results indicate that, under different natural conditions, the improved algorithm has better adaptability than the previous algorithm. Compared with the original ATL08 ATBD algorithm, the canopy height accuracy is significantly improved, especially in low FVC and high slope scenarios. When the FVC is lower than 25%,
$R_{2}$
increases by 50.3%, and the root mean square error (RMSE) is reduced by 2.175 m, and when the slope is higher than 45°, it increases by 41.7%, and the RMSE is reduced by 2.159 m. Therefore, the algorithm has apparent advantages in inverting the canopy height in a mountainous environment with lush forests.