Unsupervised Classification for Landslide Detection from Airborne Laser Scanning

FAYNE, JESSICA V.; MORA, OMAR E.; TRAN, CAITLIN J.; LENZANO, M. GABRIELA

Geosciences

MDPI

Año: 2019 vol. 9

Landslides are natural disasters that cause extensive environmental, infrastructure and socioeconomic damage worldwide. Since they are difficult to identify, it is imperative to evaluate innovative approaches to detect early-warning signs and assess their susceptibility, hazard and risk. The increasing availability of airborne laser-scanning data provides an opportunity for modern landslide mapping techniques to analyze topographic signature patterns of landslide,landslide-prone and landslides carred areas over larges waths of terrain. In this study,a methodology based on several feature extractors and unsupervised classification, specifically k-means clustering and the Gaussian mixture model (GMM) were tested at the Carlyon Beach Peninsula in the state of Washington to map slide and non-slide terrain. When compared with the detailed, independently compiled landslide inventory map, the unsupervised methods correctly classify up to 87% of the terrain in the study area. These results suggest that (1) landslide scars associated with past deep-seated landslides may be identified using digital elevation models (DEMs) with unsupervised classification models; (2)featureextractorsallowforindividualanalysisofspecifictopographicsignatures;(3)unsupervised classification can be performed on each topographic signature using multiple number of clusters; (4) comparison of documented landslide prone regions to algorithm mapped regions show that algorithmic classification can accurately identify areas where deep-seated landslides have occurred. Theconclusionsofthisstudycanbesummarizedbystatingthatunsupervisedclassificationmapping methods and airborne light detection and ranging(LiDAR)-derivedDEMscanofferimportantsurface information that can be used as effective tools for digital terrain analysis to support landslide detection.