Unsupervised classification for landslide detection from airborne laser scanning

• Autores: Tran, Caitlin J.; Mora, Omar E.; Fayne, Jessica V.; Lenzano, María Gabriela
• Año de publicación: 2019
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: DEM; DETECTION; FEATURE EXTRACTION; GAUSSIAN MIXTURE MODEL (GMM); K-MEANS CLUSTERING; LANDSLIDE; LIDAR
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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 landslide scarred areas over large swaths 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) feature extractors allow for individual analysis of specific topographic signatures; (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. The conclusions of this study can be summarized by stating that unsupervised classification mapping methods and airborne light detection and ranging (LiDAR)-derived DEMs can offer important surface information that can be used as effective tools for digital terrain analysis to support landslide detection.
Fil: Tran, Caitlin J.. California State Polytechnic University; Estados Unidos
Fil: Mora, Omar E.. California State Polytechnic University; Estados Unidos
Fil: Fayne, Jessica V.. University of California at Los Angeles; Estados Unidos
Fil: Lenzano, María Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina