1College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, Hubei, 430070, China
2Hubei Engineering Technology Research Centre for Forestry Information, Huazhong Agricultural University, Wuhan, Hubei, 30070, China
3Key Laboratory of Urban Agriculture in Central China, Ministry of Agriculture, Wuhan, Hubei, 430070, China
| Received 25 Nov 2024 |
Accepted 13 May 2025 |
Published 27 May 2025 |
Assessing forest loss from snow and ice storms is vital for disaster evaluation and sustainable management. Traditional optical remote sensing methods, which focus on horizontal canopy changes, struggle to capture vertical stand alterations caused by snow and ice storms. This study introduces the LiDAR Forest Structure Change Index (LFSCI), a novel index that employs bitemporal unmanned aerial vehicle (UAV) LiDAR point data to comprehensively evaluate changes in the vertical distribution of forest stands. Following ice storms in Shizishan, Wuhan, China in early 2024, research was conducted to compare the performance of LFSCI with traditional metrics, such as canopy cover (CC), Leaf Area Index (LAI), and tree height (TH), across two spatial scales (grid and individual tree). LFSCI was evaluated at nine point densities (5–177 pt/m2). Through validation with field-measured stand volume changes from 43 plots, LFSCI showed superior correlation (R2 = 0.64 for grids, 0.59 for trees) in comparison to CC (R2 = 0.52), LAI (R2 = 0.38), and TH (R2 = 0.16). Higher point densities enhanced accuracy, with 50 pt/m2 recommended for effective snow and ice storm impact detection. Pure broad-leaved forests were more susceptible to loss in comparison to mixed conifer-broadleaf forests, mixed broadleaf forests, and needle forests. Additionally, stands characterized by greater tree heights, steeper slopes, and shaded conditions were more vulnerable to damage than those in other environments.
