Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

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.

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

“…Given the steep terrain, the large rural population, and reported initial shaking intensities in Nepal, the potential for landslideinduced losses as a result of the 2015 earthquakes was quickly recognized (e.g., Gallen et al, 2016;Robinson et al, 2017). We reflect upon a rapid landslide assessment that was undertaken over the first 2 months after the earthquake and efforts to disseminate the findings to potential end users in Nepal and elsewhere.…”

“…Although this provided a first-order approximation of potential landslide locations, coseismic landsliding is determined by the interactions between topography, ground shaking, and local site geology (Meunier et al, 2008;Parker et al, 2015;Marc et al, 2016). Empirical landslide susceptibility models (Gallen et al, 2016;Parker et al, 2017;Robinson et al, 2017) provided probabilistic estimates of the likelihood of a landslide at any point in space within the affected area. These models predicted that landslide probabilities were high but also variable across the affected districts, especially in the middle to high Himalayas north and east of the epicenter where topographic relief increases, but where population densities remain high.…”

“…Generating a useful assessment of landsliding immediately after an earthquake remains challenging due to a lack of clarity around what information is possible to acquire under severe time constraints and what information is actually useful (Robinson et al, 2017). Our mapping effort showed that delays in information production can occur due to image availability, image quality, cloud cover, and the time taken to handle and map from imagery once it became available.…”

“…However, as the number and exact location of all landslides is not important to disaster managers at this stage of a response (OCHA, 2013;IASC, 2015), a faster approach is preferable. Robinson et al (2017) explored the merits of seeding an empirical landslide model with the initial outputs from rapid post-earthquake mapping efforts, such as our initial attempts (Fig. 3).…”

“…Models do not predict Parker et al, 2017; assessment. Modeling is independent of weather that individual landslide locations and only provide relative Robinson et al, 2017) might otherwise restrict aerial reconnaissance. Potential impacts or probabilities, which can be difficult to to run models in near real time with ShakeMap.…”