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TitleDEM fusion of elevation REST API data in support of rapid flood modelling
AuthorMcGrath, H; Stefanakis, E; Nastev, M
SourceGeomatica vol. 70, no. 4, 2016 p. 283-297, https://doi.org/10.5623/cig2016-402
Year2016
Alt SeriesNatural Resources Canada, Contribution Series 20182530
PublisherCanadian Institute of Geomatics
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectshydrogeology; geophysics; floods; modelling; statistical methods; Canadian Digital Elevation Model; elevation data fusion; methodology; digital elevation models; elevations; Elevation REST API; LiDAR; data sources
Illustrationstables; location maps; sketch maps; 3-D diagrams; graphs; histograms
ProgramQuantitave risk assessment project, Public Safety Geoscience
Released2016 12 01; 2017 05 05
AbstractDigital elevation models (DEM) are an integral part of flood modelling. High resolution DEM data are not always available or affordable for communities, thus other elevation data sources are explored. While the accuracy of some of these sources has been rigorously tested (e.g., SRTM, ASTER), others, such as Natural Resources Canada's Canadian Digital Elevation Model (CDEM) and Google and Bings' Elevation REST APIs, have not yet been properly evaluated. Details pertaining to acquisition source and accuracy are often unreported for APIs. To include these data in geospatial applications and test and reduce uncertainty, data fusion is explored. Thus, this paper introduces a new method of elevation data fusion. The novel method incorporates clustering and inverse distance weighting (IDW) concepts in the computation of a new fusion elevation surface. The results of the individual DEMs and fusion DEMs are compared to high-resolution Light Detection and Ranging (LiDAR) surface and flood inundation maps for two study areas in New Brunswick. Comparison of individual surfaces to LiDAR find that the results meet their posted accuracy specifications, with the Bing data computing the smallest mean bias and the CDEM the smallest RMSE. Fusion of all three surfaces via the proposed method increases the correlation and minimizes both RMSE and mean bias when compared to LiDAR, independent of the terrain, thus producing a more accurate DEM.
Summary(Plain Language Summary, not published)
To obtain a more accurate terrain elevations dataset, a new method which combines the less precise elevation data available everywhere Canada is proposed. The results are compared to high resolution LiDAR data. The improved accuracy elevation datasets are used for generation of flood inundation maps for two study areas in New Brunswick: Fredericton and Bathurst.
GEOSCAN ID311125