Abstract
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.
| Original language | English |
|---|---|
| Article number | 107084 |
| Journal | Geomorphology |
| Volume | 356 |
| DOIs | |
| Publication status | Published - 1 May 2020 |
Bibliographical note
Funding Information:This work was supported by the Belgium Science Policy ( BELSPO ) through the AfReSlide project ( BR/121/A2/AfReSlide ) in the BRAIN program entitled “Landslides in Equatorial Africa: identifying culturally, technically and economically feasible resilience strategies”. Support was also received from the Research Foundation Flanders (FWO) for a long research stay at CNR-IRPI (Perugia, Italy). Additional support was received from the VLIR South-Initiative projects ZEIN2013Z145 entitled “Diagnosis of land degradation processes, their socio-economical and physical controls and implications in the Mt Rwenzori region” and UG2017SIN208A105 entitled “Enhancing community-based natural resources and hazard management in Rwenzori Mountains”. We would like to explicitly thank John Sekajugo for his data collection in the field and Kewan Mertens and his team of enumerators for providing data on affected plots in the Rwenzori region. Finally, we thank Clovis Kabaseke and Mountains of the Moon University for their support.
Funding Information:
This work was supported by the Belgium Science Policy (BELSPO) through the AfReSlide project (BR/121/A2/AfReSlide) in the BRAIN program entitled “Landslides in Equatorial Africa: identifying culturally, technically and economically feasible resilience strategies”. Support was also received from the Research Foundation Flanders (FWO) for a long research stay at CNR-IRPI (Perugia, Italy). Additional support was received from the VLIR South-Initiative projects ZEIN2013Z145 entitled “Diagnosis of land degradation processes, their socio-economical and physical controls and implications in the Mt Rwenzori region” and UG2017SIN208A105 entitled “Enhancing community-based natural resources and hazard management in Rwenzori Mountains”. We would like to explicitly thank John Sekajugo for his data collection in the field and Kewan Mertens and his team of enumerators for providing data on affected plots in the Rwenzori region. Finally, we thank Clovis Kabaseke and Mountains of the Moon University for their support.
Publisher Copyright:
© 2020 Elsevier B.V.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
Keywords
- Data heterogeneity
- Landslide susceptibility
- Positional inaccuracy
- Slope units