Mapas de susceptibilidad de deslizamientos basados en GIS aplicados a la planificación natural y urbanística en Trikala, Grecia Central

G. D. Bathrellos; D. P. Kalivas; H. D. Skilodimou

doi:10.3989/egeol.08642.036

Authors

G. D. Bathrellos Department of Geography & Climatology, Faculty of Geology & Geoenvironment, National & Kapodistrian University of Athens
D. P. Kalivas Department of Natural Resources Management & Agricultural Engineering, Soil Science Laboratory, Agricultural University of Athens
H. D. Skilodimou Department of Geography & Climatology, Faculty of Geology & Geoenvironment, National & Kapodistrian University of Athens

DOI:

https://doi.org/10.3989/egeol.08642.036

Keywords:

Landslide susceptibility models, Maps, GIS, spatial planning, Central Greece

Abstract

Landslide susceptibility mapping is a practical tool in natural and urban planning; it can be applied for determining land use zones, in construction design and planning of a variety of projects. In this study, two different GIS based landslide susceptibility maps were generated in the mountainous part of the Trikala Prefecture in Thessaly, Central Greece. This was accomplished by using different methods for correlating factors, which have an effect on landslide occurrences. The instability factors taken into account were: lithology, tectonic features, slope gradients, road network, drainage network, land use and rainfall. A frequency distribution of the half number of the landslide events of the study area in each class of the instability factors was performed in order to rate the classes. Two models have been used to combine the instability factors and assess the overall landslide susceptibility, namely: the Weight Factor Model (WeF), which is a statistical method, and the Multiple Factor Model (MuF) that is a logical method. The produced maps were classified into four zones: Low, Moderate, High and Very High susceptible zones and validated using the other half number of the landslide events of the area. Evaluation of the results is optimized through a Landslide Models Indicator (La.M.I.).

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