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

Remote Sensing

Stephen Medeiros³  Scott Hagen¹  John Weishampel⁵  James Angelo²  Alisa L. Gallant⁴  Nicolas Baghdadi⁴ 

[1] Department of Civil & Environmental Engineering, Center for Computation and Technology, Louisiana State University, 3418 Patrick F. Taylor, Baton Rouge, LA 70803, USA; E-Mail:;Sandia National Laboratories, P.O. Box 5800, MS 1163, Albuquerque, NM 87185, USA; E-Mail:;Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Suite 211., Orlando, FL 32816, USA;id="af1-remotesensing-07-03507">1Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Suite 211., Orlando, FL 32816, U;Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL 32816, USA; E-Mail:

关键词: ASTER;    biomass;    IfSAR;    lidar;    salt marsh;   

DOI  :  10.3390/rs70403507

来源: mdpi

【 摘 要 】

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%.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.