Window-based morphometric indices as predictive variables for landslide susceptibility models

Identification of areas prone to landslides is essential to mitigate associated risks. This is usually achieved using landslide susceptibility models, which estimate landslide likelihood given local terrain conditions and the location of known past events. Detailed databases covering different conditioning factors are paramount to produce reliable susceptibility maps. However, thematic data from developing countries are scarce. As a result, susceptibility models often rely on morphometric parameters derived from widely-available digital elevation models. In most cases, simple parameters such as slope, aspect, and curvature, computed using a moving window of 3{$\times$}3 pixels, are used. Recently, the use of window-based morphometric indices as an additional input has increased. These rely on a user-defined observation window size. In this contribution, we examine the influence of observation window size when using window-based morphometric indices as core predictive variables for landslide susceptibility assessment. We computed a variety of models that include morphometric indices calculated with different window sizes, and compared the predictive capabilities and reliability of the resulting predictions. All models are based on the random forest algorithm. The results improved significantly when each window-based morphometric index was calculated with a different and meaningful observation window. The sensitivity analysis highlights both the highly-informative observation windows and the impact of their selection on the model performance. We also stress the importance of evaluating landslide susceptibility results using different adapted metrics for predictive performance and reliability.