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

Abstract

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

Representing hydrodynamically important blocking features in coastal or riverine lidar topography

“…A potential area where the edge-blocking method might be expanded is in the estimation of topographic roughness, which has been a subject of extensive prior research (e.g., Abu-Aly et al, 2014;Casas et al, 2010;Dorn et al, 2014;Forzieri et al, 2011;Straatsma and Baptist, 2008). By defining edge features, a portion of the difference between the grid cell elevation and subgrid features can be removed from the roughness estimation; i.e., we could use, for example, G xx , G yy , H xx , and H yy to remove pixels that have been resolved in to edge features and only consider the remaining pixels in a coarse-grid cell as contributing to roughness.…”

Volunteered Geographic Information in Natural Hazard Analysis: A Systematic Literature Review of Current Approaches with a Focus on Preparedness and Mitigation

“…In many cases of disaster management, there are already very good data available, e.g., from remote sensing or official datasets. Hence, VGI may be an alternative to such existing datasets in order to provide up-to-date and low-cost input data, e.g., for process modelling [38]. Furthermore, the advantage of VGI can be seen in the complementing of existing datasets in order to get up-to-date datasets, which might also include local knowledge [11].…”

Citizen observations contributing to flood modelling: opportunities and challenges

“…A similar approach is taken by Dong et al (2012), that analyses pictures uploaded by citizens using a different web application. The research conducted by Dorn et al (2014) …”

“…Considering the model setup, citizens contributed to improving the datasets that are used in the model, both for Land Cover 5 (Dorn et al, 2014) and the Digital Elevation Model (Shaad et al, 2016). The first case uses Open Street Maps, an online platform that provides maps, including land cover, which can be changed by citizens at any time.…”

“…The first case uses Open Street Maps, an online platform that provides maps, including land cover, which can be changed by citizens at any time. Dorn et al (2014) do not analyse how much contribution was made by the citizens and data processing is restricted to attributing land cover classes to the features displayed in the maps. In the study of Shaad et al (2016), which addresses topography, there is only one citizen contribution (low-cost alternative) in one selected location that is merged with an existing DEM.…”