Szczepanek Robert, Szczęch Mateusz, Kania Maciej
Institute of Geological Sciences, Faculty of Geography and Geology, Jagiellonian University, 30-387, Krakow, Poland.
Sci Rep. 2024 Jun 19;14(1):14130. doi: 10.1038/s41598-024-65026-z.
High-resolution digital elevation models are commonly utilized for detecting and classifying landslides. In this study, we aim to refine landslide detection and classification by analyzing the geometry of landslides using slope and aspect, coupled with descriptive statistics up to the fourth central moment (kurtosis). Employing the Monte Carlo method for creating terrain topography probability distributions and ANOVA tests for statistical validation, we analyzed 364 landslides in Gorce National Park, Poland, revealing significant kurtosis differences across landslide types and lithologies. This methodology offers a novel approach to landslide classification based on surface geometry, with implications for enhancing scientific research and improving landslide risk management strategies.
高分辨率数字高程模型通常用于检测和分类滑坡。在本研究中，我们旨在通过使用坡度和坡向分析滑坡的几何形状，并结合直至四阶中心矩（峰度）的描述性统计数据，来优化滑坡检测和分类。我们采用蒙特卡罗方法创建地形概率分布，并使用方差分析测试进行统计验证，对波兰戈尔采国家公园的364处滑坡进行了分析，结果表明不同滑坡类型和岩性的峰度存在显著差异。这种方法为基于地表几何形状的滑坡分类提供了一种新途径，对加强科学研究和改进滑坡风险管理策略具有重要意义。
