L. Jacobs(a), M. Kervyn(b), P. Reichenbach(c), M. Rossi(c), I. Marchesini(c), M. Alvioli(c), O. Dewitte(d), 2020, Regional susceptibility assessments with heterogeneous landslide information_ Slope unit- vs. pixel-based approach, Geomorphology (Amst.) 356 (2020): 1–20. doi_10.1016/j.geomorph.2020.107084,
URL: http://www.cnr.it/prodotto/i/416884

Regional landslide inventories are often prepared by several different experts, using a variety of data sources. This can result in a combination of polygon and point landslide data, characterized by different meanings, uncertainties and levels of reliability. The propagation of uncertainties due to such heterogeneous data is a relevant issue in statistical landslide susceptibility zonation at supra-local scale. In the inhabited highlands of the Rwenzori Mountains, we compare different approaches and mapping units to provide a robust methodology for susceptibility mapping using a combination of landslide point and polygon data. First, the effect of the uncertainty related to a point representation of landslides is assessed comparing slope unit-based and pixel-based analyses, using digital elevation models with different resolutions. Secondly, with regard to landslide polygon inventories, we compare the use of thresholds versus a presence/absence of the depletion centroid or a randomly selected point in the landslide polygon in order to identify slope units with landslides. Based on these results, we prepare regional slope unit-based susceptibility maps using a logistic regression model calibrated with the landslide polygon inventory and validated with the point inventory. Although pixel-based mapping remains the most common approach for statistical landslide susceptibility zonation, our analysis clearly favours the use of slope units as a powerful tool to prepare regional susceptibility maps and, in particular, to exploit heterogeneous information in a consistent way.

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