A remote-sensing-based intensity–duration threshold, Faifa Mountains, Saudi Arabia

• Published in: Natural hazards and earth system sciences 2019-06, Vol.19 (6), p.1235-1249
• Main Authors: Karki, Sita, Sultan, Mohamed, Alsefry, Saleh, Alharbi, Hassan, Emil, Mustafa Kemal, Elkadiri, Racha, Alfadail, Emad Abu
• Format: Article
• Language: English
• Subjects: Backscatter; Backscattering; Duration; Earth; Forecasts and trends; Hydrologic data; Landslides; Landslides & mudslides; Locations (working); Mass movement; Methods; Mountains; Movement; Nowcasting; Pixels; Precipitation; Precipitation measurements; Procedures; Prototypes; Radar; Radar imagery; Radar imaging; Remote sensing; Saudi Arabia; Spatial data; Storms; Technology application; Warning systems
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-19-1235-2019

Construction of intensity–duration (ID) thresholds and early-warning and nowcasting systems for landslides (EWNSLs) are hampered by the paucity of temporal and spatial archival data. This work represents significant steps towards the development of a prototype EWNSL to forecast and nowcast landslides over the Faifa Mountains in the Red Sea Hills. The developed methodologies rely on readily available, temporal, archival Google Earth and Sentinel-1A imagery, precipitation measurements, and limited field data to construct an ID threshold for Faifa. The adopted procedures entail the generation of an ID threshold to identify the intensity and duration of precipitation events that cause landslides in the Faifa Mountains, and the generation of pixel-based ID curves to identify locations where movement is likely to occur. Spectral and morphologic variations in temporal Google Earth imagery following precipitation events were used to identify landslide-producing storms and generate the Faifa ID threshold (I =4.89D−0.65). Backscatter coefficient variations in radar imagery were used to generate pixel-based ID curves and identify locations where mass movement is likely to occur following landslide-producing storms. These methodologies accurately distinguished landslide-producing storms from non-landslide-producing ones and identified the locations of these landslides with an accuracy of 60 %.