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Water 2014, 6(2), 381-398; doi:10.3390/w6020381
Article

Real Time Estimation of the Calgary Floods Using Limited Remote Sensing Data

1,* , 2
, 3
 and 4
1 Department of Geography and GeoInformation Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA 2 Department of Geography and Institute for CyberScience, The Pennsylvania State University, 201 Old Main, University Park, PA 16802, USA 3 School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S. Mill Avenue, Tempe, AZ 85281, USA 4 Center for Excellence in GIS, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
* Author to whom correspondence should be addressed.
Received: 16 December 2013 / Revised: 28 January 2014 / Accepted: 8 February 2014 / Published: 18 February 2014
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Abstract

Every year, flood disasters are responsible for widespread destruction and loss of human life. Remote sensing data are capable of providing valuable, synoptic coverage of flood events but are not always available because of satellite revisit limitations, obstructions from cloud cover or vegetation canopy, or expense. In addition, knowledge of road accessibility is imperative during all phases of a flood event. In June 2013, the City of Calgary experienced sudden and extensive flooding but lacked comprehensive remote sensing coverage. Using this event as a case study, this work illustrates how data from non-authoritative sources are used to augment traditional data and methods to estimate flood extent and identify affected roads during a flood disaster. The application of these data, which may have varying resolutions and uncertainities, provide an estimation of flood extent when traditional data and methods are lacking or incomplete. When flooding occurs over multiple days, it is possible to construct an estimate of the advancement and recession of the flood event. Non-authoritative sources also provide flood information at the micro-level, which can be difficult to capture from remote sensing data; however, the distibution and quantity of data collected from these sources will affect the quality of the flood estimations.
Keywords: flood assessment; volunteered geographical data; data fusion flood assessment; volunteered geographical data; data fusion
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.

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MDPI and ACS Style

Schnebele, E.; Cervone, G.; Kumar, S.; Waters, N. Real Time Estimation of the Calgary Floods Using Limited Remote Sensing Data. Water 2014, 6, 381-398.

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