Landslide change detection based on Multi-Temporal airborne LIDAR-derived DEMs

• Autores: Mora, Omar E.; Lenzano, María Gabriela; Toth, Charles Karoly; Grejner-Brzezinska, Dorota A.; Fayne, Jessica V.
• Año de publicación: 2018
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: CHANGE DETECTION; DEM; FEATURE EXTRACTION; LANDSLIDE; LIDAR; MULTI-TEMPORAL
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m2 to 27,000 m2 in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM) quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides.
Fil: Mora, Omar E.. California State Polytechnic University; 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
Fil: Toth, Charles Karoly. Ohio State University; Estados Unidos
Fil: Grejner-Brzezinska, Dorota A.. Ohio State University; Estados Unidos
Fil: Fayne, Jessica V.. University of California at Los Angeles; Estados Unidos