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1 March 2016 Evaluation of Error Reduction Techniques on a LIDAR-Derived Salt Marsh Digital Elevation Model
Adam McClure, XiaoHang Liu, Ellen Hines, Matthew C. Ferner
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McClure, A.; Liu, X.; Hines, E., and Ferner, M.C., 2016. Evaluation of error reduction techniques on a LIDAR-derived salt marsh digital elevation model.

Accurate elevation information is a necessity for conservation and management of tidal salt marshes where elevation differences can be as little as 2 m and where sea-level rise is a critical threat. This study applied an existing method to evaluate and improve the vertical accuracy of a 1-m LIDAR-derived digital elevation model (DEM) using a real-time kinematic (RTK) GPS dataset with a vertical accuracy of ±0.02 m and local vegetation data within a tidal salt marsh. Correction factors were generated for vegetation species within each major vegetation class and produced a modified DEM of the site. Comparison between the original and modified DEM showed that the mean error was reduced from 0.16 m to −0.004 m and the root mean squared error was reduced from 0.212 m to 0.098 m. These results demonstrate that it is possible to significantly reduce vertical error contained within a salt marsh DEM derived from a LIDAR dataset using highly accurate RTK GPS data combined with vegetation data collected on a per site basis.

Adam McClure, XiaoHang Liu, Ellen Hines, and Matthew C. Ferner "Evaluation of Error Reduction Techniques on a LIDAR-Derived Salt Marsh Digital Elevation Model," Journal of Coastal Research 32(2), 424-433, (1 March 2016).
Received: 23 September 2014; Accepted: 1 June 2015; Published: 1 March 2016

China Camp State Park
coastal vegetation
National Estuarine Research Reserve
sea-level rise
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