Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James

doi:10.3390/rs70403507

Title: Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

Abstract

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%.

Authors:

Medeiros, Stephen ^[1]; Hagen, Scott ^[2]; Weishampel, John ^[1]; Angelo, James ^[3]

Univ. of Central Florida, Orlando, FL (United States)
Louisiana State Univ., Baton Rouge, LA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Wed Mar 25 00:00:00 EDT 2015

Research Org.:: Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1213406

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: Remote Sensing

Additional Journal Information:: Journal Volume: 7; Journal Issue: 4; Journal ID: ISSN 2072-4292

Publisher:: MDPI

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES; ASTER; biomass; IfSAR; lidar; salt marsh

Citation Formats


                    Medeiros, Stephen, Hagen, Scott, Weishampel, John, and Angelo, James. Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density.  United States: N. p., 2015. 
Web.  doi:10.3390/rs70403507.

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                    Medeiros, Stephen, Hagen, Scott, Weishampel, John, & Angelo, James. Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density.  United States.  https://doi.org/10.3390/rs70403507

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                    Medeiros, Stephen, Hagen, Scott, Weishampel, John, and Angelo, James. Wed .  
"Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density".  United States.  https://doi.org/10.3390/rs70403507.  https://www.osti.gov/servlets/purl/1213406.

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@article{osti_1213406,

  title        = {Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density},

  author       = {Medeiros, Stephen and Hagen, Scott and Weishampel, John and Angelo, James},

  abstractNote = {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%.},

  doi          = {10.3390/rs70403507},

  journal      = {Remote Sensing},

  number       = 4,

  volume       = 7,

  place        = {United States},

  year         = {Wed Mar 25 00:00:00 EDT 2015},

  month        = {Wed Mar 25 00:00:00 EDT 2015}

}

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Title: Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

Abstract

Citation Formats

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Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR)
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