Abstract. Landslides constitute a hazard to life and infrastructure and their risk is mitigated primarily by reducing exposure. This requires information on landslide hazard on a scale that can enable informed decisions. Such information is often unavailable to, or not easily interpreted by, those who might need it most (e.g. householders, local governments and non-governmental organisations). To address this shortcoming, we develop simple rules to minimise exposure to coseismic landslide hazard that are understandable, communicable and memorable, and that require no prior knowledge, skills or equipment to apply. We examine rules based on two common metrics of landslide hazard, (1) local slope and (2) upslope contributing area as a proxy for hillslope location relative to rivers or ridge crests. In addition, we introduce and test two new metrics: the maximum angle to the skyline and the hazard area, defined as the upslope area with slope >40∘ from which landslide debris can reach a location without passing over a slope of <10∘. We then test the skill with which each metric can identify landslide hazard – defined as the probability of being hit by a landslide – using inventories of landslides triggered by six earthquakes that occurred between 1993 and 2015. We find that the maximum skyline angle and hazard area provide the most skilful predictions, and these results form the basis for two simple rules: “minimise your maximum angle to the skyline” and “avoid steep (>10∘) channels with many steep (>40∘) areas that are upslope”. Because local slope alone is also a skilful predictor of landslide hazard, we can formulate a third rule as “minimise the angle of the slope under your feet, especially on steep hillsides, but not at the expense of increasing skyline angle or hazard area”. In contrast, the upslope contributing area has a weaker and more complex relationship to hazard than the other predictors. Our simple rules complement but do not replace detailed site-specific investigation: they can be used for initial estimations of landslide hazard or to guide decision-making in the absence of any other information.

Abstract. Earthquakes in mountainous areas can trigger thousands of co-seismic landslides, causing significant damage, hampering relief efforts, and rapidly redistributing sediment across the landscape. Efforts to understand the controls on these landslides rely heavily on manually mapped landslide inventories, but these are costly and time-consuming to collect, and their reproducibility is not typically well constrained. Here we develop a new automated landslide detection index (ALDI) algorithm based on pixel-wise normalised difference vegetation index (NDVI) differencing of Landsat time series within Google Earth Engine accounting for seasonality. We compare classified inventories to manually mapped inventories from five recent earthquakes: Kashmir in 2005, Aysén in 2007, Wenchuan in 2008, Haiti in 2010, and Gorkha in 2015. We test the ability of ALDI to recover landslide locations (using receiver operating characteristic – ROC – curves) and landslide sizes (in terms of landslide area–frequency statistics). We find that ALDI more skilfully identifies landslide locations than published inventories in 10 of 14 cases when ALDI is locally optimised and in 8 of 14 cases both when ALDI is globally optimised and in holdback testing. These results reflect not only good performance of the automated approach but also surprisingly poor performance of manual mapping, which has implications both for how future classifiers are tested and for the interpretations that are based on these inventories. We find that manual mapping, which typically uses finer-resolution imagery, more skilfully captures the landslide area–frequency statistics, likely due to reductions in both the censoring of individual small landslides and amalgamation of landslide clusters relative to ALDI. We conclude that ALDI is a viable alternative to manual mapping in terms of its ability to identify landslide-affected locations but is less suitable for detecting small isolated landslides or precise landslide geometry. Its fast run time, cost-free image requirements, and near-global coverage suggest the potential to significantly improve the coverage and quantity of landslide inventories. Furthermore, its simplicity (pixel-wise analysis only) and parsimony of inputs (optical imagery only) mean that considerable further improvement should be possible.

Abstract. Landslides constitute a hazard to life and infrastructure, and their risk is mitigated primarily by reducing exposure. This requires information on landslide hazard at a scale that can enable informed decisions about how to respond to that hazard. Such information is often unavailable to, or not easily interpreted by, those who might need it most (e.g., householders, local government, and NGOs). To address this shortcoming, we develop simple rules to identify landslide hazard that are understandable, communicable, and memorable, and that require no prior knowledge, skills, or equipment to evaluate. We examine rules based on two common metrics of landslide hazard, local slope and upslope contributing area as a proxy for hillslope location, and we introduce and test two new metrics: the maximum angle to the skyline and the hazard area, defined as the upslope area with slope > 39° that reaches a location without passing over a slope of  10°) channels with many steep (> 39°) areas that are upslope. Because local slope alone is a skilful predictor of landslide hazard, we can formulate a third rule as minimise local slope, especially on steep slopes and even at the expense of increasing upslope contributing area, but not at the expense of increasing skyline angle or hazard area. Upslope contributing area, by contrast, has a weaker and more complex relationship to hazard than the other predictors. Our simple rules complement, but do not replace, detailed site-specific investigation; they can be used for initial estimation of landslide hazard or guide decision-making in the absence of any other information.

Abstract. Earthquakes in mountainous areas can trigger thousands of co-seismic landslides, causing significant damage, hampering relief efforts, and rapidly redistributing sediment across the landscape. Efforts to understand the controls on these landslides rely heavily on manually mapped landslide inventories, but these are costly and time-consuming to collect, and their reproducibility is not typically well constrained. Here we develop a new automated landslide detection algorithm (ALDI) based on pixel-wise NDVI differencing of Landsat time series within Google Earth Engine accounting for seasonality. We compare classified inventories to manually mapped inventories from five recent earthquakes: 2005 Kashmir, 2007 Aisen, 2008 Wenchuan, 2010 Haiti, and 2015 Gorkha. We test the ability of ALDI to recover landslide locations (using ROC curves) and landslide sizes (in terms of landslide area-frequency statistics). We find that ALDI more skilfully identifies landslides than published inventories in 10 of 14 cases when ALDI is locally optimised, and in 8 of 14 cases both when ALDI is globally optimised and in holdback testing. These results reflect both good performance of the automated approach but also surprisingly poor performance of manual mapping, which has implications not only for how future classifiers are tested but also for the interpretations that are based on these inventories. We conclude that ALDI already represents a viable alternative to manual mapping in terms of its ability to identify landslide-affected image pixels. Its fast run-time, cost-free image requirements and near-global coverage make it an attractive alternative with the potential to significantly improve the coverage and quantity of landslide inventories. Its simplicity (pixel-wise analysis only) and parsimony of inputs (optical imagery only) suggests that considerable further improvement should be possible.