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Article

GIS-Based Roughness Derivation for Flood Simulations: A Comparison of Orthophotos, LiDAR and Crowdsourced Geodata

1
Institute of Geography & Heidelberg Center for the Environment (HCE), Heidelberg University, Berliner Str. 48, D-69120 Heidelberg, Germany
2
Institute of Geography, University of Innsbruck, Innrain 52f, A-6020 Innsbruck, Austria
3
Centre for Water Resource Systems, Research Groups Photogrammetry & Remote Sensing, Vienna University of Technology, Karlsplatz 13, A-1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(2), 1739-1759; https://doi.org/10.3390/rs6021739
Received: 4 October 2013 / Revised: 28 January 2014 / Accepted: 12 February 2014 / Published: 24 February 2014
Natural disasters like floods are a worldwide phenomenon and a serious threat to mankind. Flood simulations are applications of disaster control, which are used for the development of appropriate flood protection. Adequate simulations require not only the geometry but also the roughness of the Earth’s surface, as well as the roughness of the objects hereon. Usually, the floodplain roughness is based on land use/land cover maps derived from orthophotos. This study analyses the applicability of roughness map derivation approaches for flood simulations based on different datasets: orthophotos, LiDAR data, official land use data, OpenStreetMap data and CORINE Land Cover data. Object-based image analysis is applied to orthophotos and LiDAR raster data in order to generate land cover maps, which enable a roughness parameterization. The vertical vegetation structure within the LiDAR point cloud is used to derive an additional floodplain roughness map. Further roughness maps are derived from official land use data, OpenStreetMap and CORINE Land Cover datasets. Six different flood simulations are applied based on one elevation data but with the different roughness maps. The results of the hydrodynamic–numerical models include information on flow velocity and water depth from which the additional attribute flood intensity is calculated of. The results based on roughness maps derived from LiDAR data and OpenStreetMap data are comparable, whereas the results of the other datasets differ significantly. View Full-Text
Keywords: hydraulic modeling; land use/land cover classification; OpenStreetMap; ALS point cloud; floodplain; vertical vegetation structure; Volunteered Geographic Information; hydraulic friction coefficient hydraulic modeling; land use/land cover classification; OpenStreetMap; ALS point cloud; floodplain; vertical vegetation structure; Volunteered Geographic Information; hydraulic friction coefficient
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MDPI and ACS Style

Dorn, H.; Vetter, M.; Höfle, B. GIS-Based Roughness Derivation for Flood Simulations: A Comparison of Orthophotos, LiDAR and Crowdsourced Geodata. Remote Sens. 2014, 6, 1739-1759. https://doi.org/10.3390/rs6021739

AMA Style

Dorn H, Vetter M, Höfle B. GIS-Based Roughness Derivation for Flood Simulations: A Comparison of Orthophotos, LiDAR and Crowdsourced Geodata. Remote Sensing. 2014; 6(2):1739-1759. https://doi.org/10.3390/rs6021739

Chicago/Turabian Style

Dorn, Helen, Michael Vetter, and Bernhard Höfle. 2014. "GIS-Based Roughness Derivation for Flood Simulations: A Comparison of Orthophotos, LiDAR and Crowdsourced Geodata" Remote Sensing 6, no. 2: 1739-1759. https://doi.org/10.3390/rs6021739

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