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Automatic Detection of Potential Dam Locations in Digital Terrain Models

TU Wien, Department of Geodesy and Geoinformation, Research Group Photogrammetry, Gusshausstr. 27–29, 1040 Wien, Austria
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ISPRS Int. J. Geo-Inf. 2019, 8(4), 197; https://doi.org/10.3390/ijgi8040197
Received: 27 February 2019 / Revised: 12 April 2019 / Accepted: 22 April 2019 / Published: 24 April 2019
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Abstract

Structural measures for retaining and distributing water—i.e., reservoirs, flood retention and power plants—play a key role to protect and feed a growing world population in a rapidly changing climate. In this work, we introduce an automated method to detect potential reservoir or retention area locations in digital terrain models. In this context, a potential reservoir is a larger terrain form that can be turned into an actual reservoir by constructing a dam. Based on contour lines derived from terrain models, potential reservoirs are found within a predefined range of dam lengths, and the locally optimal ones are then extracted. Our method is to be applied in the very early stages of project planning and for area-wide potential analysis. Tests in a 100 km2 study area bring promising results, but also show a certain sensitivity regarding terrain model quality and resolution. In total, 250–300 candidate polygons with a total volume of more than 6 million m3 were found. In order to facilitate further processing, these are stored as a GIS vector dataset. View Full-Text
Keywords: dam placement; digital terrain model; reservoir; flood retention; contour lines; water resource management dam placement; digital terrain model; reservoir; flood retention; contour lines; water resource management
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wimmer, M.H.; Pfeifer, N.; Hollaus, M. Automatic Detection of Potential Dam Locations in Digital Terrain Models. ISPRS Int. J. Geo-Inf. 2019, 8, 197.

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