Abstract
As geomorphological processes operate at various spatial scales, their morphological expressions, i.e., land-surface variables (LSVs) should be scaled accordingly. Most approaches on landslide susceptibility modeling and landslide detection have been performed based on arbitrarily scaled LSVs. We propose a methodology to improve automated landslide detection by fitting each LSV to its optimal scale. We test our approach on two landslide inventories, with different landslide morphology. First, we derive seven LSVs from a DEM in a standard 3 × 3 moving window. Then, we rescale each LSV using focal mean statistics in increasingly larger moving windows until the optimal scale is found, i.e., scale at which logistic regression shows the best fit between the existence of landslide scarps and individual LSVs. The LSVs at the optimal scale are used as input data in a random forest (RF) model. In order to calculate the effect of scaling predictors on the accuracy of the model, we compare the results, using the area under the curve (AUC), against the results from an RF model with unscaled LSVs as input data. The results show (i) that different LSVs have different optimal scales, and (ii) the multi-scale approach improved the models significantly, from AUC = 0.73 to 0.80 for the first study area and from AUC = 0.59 to 0.73 for the second study area. Based on these results, we conclude that a multi-scale approach should be considered when automated models are used in order to detect landslides, in complex terrain settings.
Highlights
The concept of scale stands at the basis of geomorphology and geomorphometry (Pike et al 2008) and yet it is not straightforwardly defined in both theory and practical applications (Bishop et al 2012; Zhilin 2008)
The performance of the predictors for both study areas shows that two predictors display a higher scale for Buzau, while three others show a higher scale for Shizuoka
These results suggest that the relationship between the size of landslides and the scale of predictors is not straightforward. If such a relationship exists, it is most probably defined for each individual predictor, and is more complex than a simple linear one. This confirms the findings of Paudel et al (2016) and Pawluszek et al (2018) in that there is no universal scale that works for all Land-surface variable (LSV), but each LSV performs best at its own scale
Summary
The concept of scale stands at the basis of geomorphology and geomorphometry (Pike et al 2008) and yet it is not straightforwardly defined in both theory and practical applications (Bishop et al 2012; Zhilin 2008). The concept of scale has different meanings in different fields of study (Goodchild 2001). In cartography it represents the ratio between the real dimensions and the represented dimensions, while in geomorphology scale usually refers to the size of the study area (e.g., local, regional, national, or continental scale) (Broeckx et al 2016; Gariano et al 2017; Segoni et al 2018). Because the computational scale is strongly related to the observational scale, they are often discussed together or in an interchangeable manner.
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