Abstract
Abstract. Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. In this study, we demonstrate how traditional empirical methods for modelling the total distribution and relative intensity (in terms of point density) of coseismic landsliding can be successfully undertaken in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in a GIS (Geographic Information Systems) to quickly assess and identify the location-specific relationships between predisposing factors and landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the number density of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high-fidelity mapping of landslides following an earthquake is not necessary for informing rapid modelling attempts. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the modelling. This method is therefore suited to conditions in which imagery is affected by partial cloud cover or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake and may therefore inform emergency operations more effectively compared to current practice.
Highlights
Coseismic landslides are one of the most widespread and destructive hazards to result from earthquakes in mountainous environments
This study has addressed the need for more rapid assessments of coseismic landslide intensity and distribution following a major earthquake
The present study has demonstrated an empirical method to model landslide intensity and distribution using fuzzy logic based on initial inventories with small numbers of landslides
Summary
Coseismic landslides are one of the most widespread and destructive hazards to result from earthquakes in mountainous environments. Landslides are a key inhibitor of relief and reconstruction via the blocking of critical infrastructure and present a chronic hazard, with post-earthquake landslide rates remaining elevated compared to pre-earthquake rates for at least several years (Marc et al, 2015). Identifying the distribution of landslides following an earthquake is crucial for understanding the total earthquake impacts (Robinson and Davies, 2013); aiding immediate emergency response efforts, including search and rescue; and assessing the longer-term post-earthquake risks. Post-earthquake landslide mapping is a difficult and time-consuming task, hindered by issues relating to the collection and processing of appropriate satellite or aerial images, cloud cover, and the slow speeds associated with manually identifying and mapping large numbers of landslides. Following the 2015 Mw 7.8 Gorkha earthquake in Nepal, efforts to rapidly identify and map coseismic land-
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