Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9302
Title: Satellite remote sensing and GIS-based multi-criteria analysis for flood hazard mapping
Authors: Franci, Francesca 
Bitelli, Gabriele 
Mandanici, Emanuele 
Hadjimitsis, Diofantos G. 
Agapiou, Athos 
metadata.dc.contributor.other: Χατζημιτσής, Διόφαντος
Αγαπίου, Άθως
Major Field of Science: Natural Sciences
Field Category: Earth and Related Environmental Sciences
Keywords: Analytic hierarchy process;Flood hazard;GeoEye-1;Object-based classification;Yialias river
Issue Date: 1-Oct-2016
Source: Natural Hazards, 2016, vol. 83, pp. 31-51
Volume: 83
Start page: 31
End page: 51
Journal: Natural Hazards 
Abstract: This work focuses on the exploitation of very high-resolution (VHR) satellite imagery coupled with multi-criteria analysis (MCA) to produce flood hazard maps. The methodology was tested over a portion of the Yialias river watershed basin (Nicosia, Cyprus). The MCA methodology was performed selecting five flood-conditioning factors: slope, distance to channels, drainage texture, geology and land cover. Among MCA methods, the analytic hierarchy process technique was chosen to derive the weight of each criterion in the computation of the flood hazard index (FHI). The required information layers were obtained by processing a VHR GeoEye-1 image and a digital elevation model. The satellite image was classified using an object-based technique to extract land use/cover data, while GIS geoprocessing of the DEM provided slope, stream network and drainage texture data. Using the FHI, the study area was finally classified into seven hazard categories ranging from very low to very high in order to generate an easily readable map. The hazard seems to be severe, in particular, in some urban areas, where extensive anthropogenic interventions can be observed. This work confirms the benefits of using remote sensing data coupled with MCA approach to provide fast and cost-effective information concerning the hazard assessment, especially when reliable data are not available.
URI: https://hdl.handle.net/20.500.14279/9302
ISSN: 0921030X
DOI: 10.1007/s11069-016-2504-9
Rights: © Springer Nature
Type: Article
Affiliation : University of Bologna 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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