Automated Satellite-Based Landslide Identification Product for Nepal

Earth Interactions

Justin Roberts-Pierel^2¹  Jessica V. Fayne²  Aakash Ahamed^1³  Amanda C. Rumsey^3⁴ 

[1]Department of Geography, U.S. Census Bureau, Suitland, Maryland^3

[2]Department of Geophysics, Stanford University, Stanford, California^1

[3]Department of Physics and Astronomy, University of South Carolina, Columbia, South Carolina^2

[4]Hydrologic Sciences Laboratory, Code 617, NASA Goddard Space Flight Center Greenbelt, Maryland See all authors & affiliations^4

关键词: Land surface;    Geographic information systems (GIS);    Remote sensing;   

DOI  :  10.1175/EI-D-17-0022.1

学科分类：地球科学(综合)

来源: American Geophysical Union

【 摘 要 】

Section: Choose Top of page<< 1.Introduction 2.Data and method 3.Validation 4.Results and discussion 5.Conclusions References CITING ARTICLES Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open-source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat-8 Operational Land Imager sensor, elevation data from the Shuttle Radar Topography Mission, and precipitation data from the Global Precipitation Measurement mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change-detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-Time Increased Precipitation (DRIP) model that helps to identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state of the art for landslide detection. A case study and validation exercise in Nepal were performed for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium-resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool.

【 授权许可】

Unknown