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Ice-snow storm (ISS) is one of the most destructive natural disasters impacting forest ecosystems in temperate, frigid, and mountainous plateau regions. Due to the increasing global climate extremes, a notable rise of frequency and magnitude of ISS was observed in the mid- and low-latitude subtropical climate zones. Specifically, in February 2024, Central-Eastern China experienced persistent low temperatures, rain, snow, and ice events (i.e., ISS), and the forest canopy experienced substantial damage. This research, based on the ICESat-2 data points (i.e., ATL08 data) and multi-source remote sensing data, employs a point-to-surface (PTS) approach to monitor spatio-temporal changes in forest canopy height caused by the 2024 ISS in Central-Eastern China. Firstly, PTS used the canopy height differences calculated from repeated ICESat-2 ATL08 points in 2023 and 2024 to assess canopy height reductions at various spatial distances within 50 m. The results indicated that approximately 70% of the repeated ICESat-2 points experienced a significant canopy height reduction ranging from 0 to 10 m. Secondly, PTS integrated the ICESat-2 ATL08 canopy height points and multi-source remote sensing data to construct Random Forest (RF) regression models. The wall-to-wall canopy height maps for 2023 and 2024 in Central-Eastern China were predicted based on the RF regression models and five-fold cross-validation, and the R 2 of the estimated canopy height maps in 2023 and 2024 are 0.658 ± 0.004 and 0.648 ± 0.004, respectively. The median forest canopy height decreased by 2.56 m from 2023 to 2024, corresponding to 10.11% of the 2023 median canopy height. Furthermore, only 0.1% of the total forest area in 2023 experienced serious damage after the 2024 ISS, whereas nearly 30% of the total forest area exhibited canopy height reductions exceeding 10%. Furthermore, we analyzed the environmental and climate drivers of forest canopy reduction, indicating that regions with severe forest canopy reduction were primarily located in areas with gentler terrain, lower elevation and canopy height, and higher temperature and precipitation. The results of interpretable machine learning-based method (i.e., RF-SHAP) showed that elevation and original canopy height were the two most important variables affecting canopy height reduction in Central-Eastern China. This study contributes to improving the monitoring and assessment of forest canopy damage caused by ISS in mid- and low-latitude regions of subtropical climate zone.
Yu et al. (Mon,) studied this question.