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Open AccessArticle

Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Suite 211., Orlando, FL 32816, USA
Department of Civil & Environmental Engineering, Center for Computation and Technology, Louisiana State University, 3418 Patrick F. Taylor, Baton Rouge, LA 70803, USA
Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL 32816, USA
Sandia National Laboratories, P.O. Box 5800, MS 1163, Albuquerque, NM 87185, USA
Author to whom correspondence should be addressed.
Academic Editors: Alisa L. Gallant, Nicolas Baghdadi and Prasad S. Thenkabail
Remote Sens. 2015, 7(4), 3507-3525;
Received: 5 December 2014 / Revised: 10 March 2015 / Accepted: 17 March 2015 / Published: 25 March 2015
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three- class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%. View Full-Text
Keywords: ASTER; biomass; IfSAR; lidar; salt marsh ASTER; biomass; IfSAR; lidar; salt marsh
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Medeiros, S.; Hagen, S.; Weishampel, J.; Angelo, J. Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density. Remote Sens. 2015, 7, 3507-3525.

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