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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2017-441
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2017-441
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Dec 2017

Research article | 18 Dec 2017

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Land use and land cover geoinformation properties and its influence on the landslide susceptibility zonation of road network

Bruno M. Meneses, Susana Pereira, and Eusébio Reis Bruno M. Meneses et al.
  • Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Edif. IGOT, Rua Branca Edmée Marques, Lisboa, 1600-276, Portugal

Abstract. This paper evaluates the influence of land use and land cover (LUC) geoinformation with different properties on landslide susceptibility zonation of the road network in Zêzere watershed (Portugal). The Information Value method was used to assess landslide susceptibility using two models: one including detailed LUC geoinformation (Portuguese Land Cover Map – COS) and other including more generalized LUC geoinformation (Corine Land Cover – CLC). A set of six fixed independent layers were considered as landslide predisposing factors (slope angle, slope aspect, slope curvature, slope over area ratio, soil, and lithology), while COS and CLC were used to find the differences in the landslide susceptibility zonation. A landslide inventory was used as dependent layer, including 259 shallow landslides obtained from photo-interpretation of orthophotos of 2005 and further validated in three sample areas (128 landslides). The landslide susceptibility maps were merged into road network geoinformation, and resulted in two landslide susceptibility road network maps. Models performance was evaluated with success rate curves and area under the curve. Landslide susceptibility results obtained in the two models are very good, but in comparison the model obtained with more detailed LUC geoinformation (COS) produces better results in the landslide susceptibility zonation and on the road network detection with the highest landslide susceptibility. This last map also provides more detailed information about the locals where the next landslides will probably occur with possible road network disturbances.

Bruno M. Meneses et al.
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Bruno M. Meneses et al.
Bruno M. Meneses et al.
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