气候变化领域集成服务门户(CCPortal): Persistent homology on LiDAR data to detect landslides

CCPortal

DOI	10.1016/j.rse.2020.111816
	Persistent homology on LiDAR data to detect landslides
	Syzdykbayev M.; Karimi B.; Karimi H.A.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	Landslides can result in loss of lives, cause damage to property, infrastructure, utilities, and residential structures and can block transportation routes. Landslide inventory maps can provide spatial-temporal information about past and recent landslides and are used for analysis to create models that can characterize susceptibility. As such, these maps are considered an essential source for risk management tasks. In this paper, we propose a persistent homology method applied on LiDAR-derived digital terrain model data to detect landslides for landslide inventory maps. In testing the hypothesis that persistent homology, a method for computing topological features of a space at different resolutions, can be used to accurately detect landslides, we applied the method on LiDAR-derived digital terrain models to detect shapes and patterns that are indicative of landslide surface expressions. We validated our test results by comparing them to currently available landslide inventory maps for selected locations in Pennsylvania, Oregon, Colorado and Washington. The results show a different performance for each state; the accuraces were 0.79, 0.71, 0.53, 0.69, and 0.77 for five study areas. Variations in performance are linked to varying surface roughness between different types of landslides, their size, shape, ages, composition, and a possible history of reactivations of landslides that would further pronounce surface expressions. To overcome some of these challenges that may hinder performance of our method, we recommend that other datasets, containing landslides, be considered as additional nth-order dimensions beyond the spatial and elevation dimensions inherent in topographic datasets. © 2020 Elsevier Inc.
英文关键词	DTM; GIS; Landslide detection; LiDAR; Persistent homology
语种	英语
scopus关键词	Optical radar; Risk management; Surface roughness; Transportation routes; Different resolutions; Digital terrain model; Landslide inventories; Persistent homology; Residential structures; Surface expression; Topographic datasets; Topological features; Landslides; data set; digital terrain model; hypothesis testing; landslide; lidar; performance assessment; satellite data; surface roughness; topography; Colorado; Oregon; Pennsylvania; United States; Washington [United States]
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179282
作者单位	Geoinformatics Laboratory, School of Computing and Information, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, United States; Department of Environmental Engineering and Earth Sciences, Wilkes University, 84 West South Street, Wilkes-Barre, PA, United States
推荐引用方式 GB/T 7714	Syzdykbayev M.,Karimi B.,Karimi H.A.. Persistent homology on LiDAR data to detect landslides[J],2020,246.
APA	Syzdykbayev M.,Karimi B.,&Karimi H.A..(2020).Persistent homology on LiDAR data to detect landslides.Remote Sensing of Environment,246.
MLA	Syzdykbayev M.,et al."Persistent homology on LiDAR data to detect landslides".Remote Sensing of Environment 246(2020).