气候变化领域集成服务门户(CCPortal): Automated landslide detection model to delineate the extent of existing landslides

CCPortal

DOI	10.1007/s11069-021-04650-8
	Automated landslide detection model to delineate the extent of existing landslides
	Alimohammadlou Y.; Tanyu B.F.; Abbaspour A.; Delamater P.L.
发表日期	2021
ISSN	0921030X
起始页码	1639
结束页码	1656
卷号	107 期号:2
英文摘要	Landslides are one of the most common natural hazards and cause major socioeconomic impacts worldwide. Identifying the locations of the active or inactive landslides before development may play a major role in identifying areas of high risk. Traditional methods for inventorying landslides involve field surveying and interpretation of photogrammetric data. The advent of recent remote sensing technologies has expedited this process, and as a result, several computer-based algorithms used to identify the locations of past landslides have been proposed. Computer-based analyses provide significant advantages over traditional methods; however, a majority of these computer-based analyses require the user to define the properties of the landslide prior to the search and require supervision and quality assurance. The purpose of this study is to present a simple, new methodology that can be implemented with readily available tools and datasets without the need to supervise the analysis after the parameters regarding landslide morphology are defined for that region. This methodology is referred to as automated landslide detection model (ALDM). Three areas with LiDAR bare earth digital elevation models (DEMs) have been used to test the ALDM, each consisting of a varying range of mapped landslide features. The ALDM results were compared against data obtained from the Pennsylvania Department of Conservation and Natural Resources and landslides that were determined visually from the hillshade map of the study area. The results demonstrate that the ALDM method was able to accurately capture both the landslides and non-landslides in all of the areas evaluated with accuracies of 70% and 92%, respectively. Additionally, the study showed that the proposed ALDM method could be implemented in different regions where landslides of different shapes and sizes could be detected. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
关键词	Image processing Inventory analysis Landslide detection LiDAR Roughness analysis
英文关键词	accuracy assessment; data interpretation; detection method; digital elevation model; image processing; inventory; landslide; lidar; mapping method; photogrammetry; remote sensing; roughness; Pennsylvania; United States
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206075
作者单位	Department of Civil, Environmental and Infrastructure Engineering, Krasnow Institute, George Mason University, 4461 Rockfish Creek Ln, Suite 105, MSN 2A1, Fairfax, VA 22030, United States; Department of Geography, University of North Carolina, Chapel Hill, NC 27599-3220, United States
推荐引用方式 GB/T 7714	Alimohammadlou Y.,Tanyu B.F.,Abbaspour A.,et al. Automated landslide detection model to delineate the extent of existing landslides[J],2021,107(2).
APA	Alimohammadlou Y.,Tanyu B.F.,Abbaspour A.,&Delamater P.L..(2021).Automated landslide detection model to delineate the extent of existing landslides.Natural Hazards,107(2).
MLA	Alimohammadlou Y.,et al."Automated landslide detection model to delineate the extent of existing landslides".Natural Hazards 107.2(2021).