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Integrating LIDAR elevation data, multi‐spectral imagery and neural network modelling for marsh characterization

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Abstract:

Vertical elevation relative to mean sea level is a critical variable for the productivity and stability of salt marshes. This research classified a high spatial resolution Airborne Data Acquisition and Registration (ADAR) digital camera image of a salt marsh landscape at North Inlet, South Carolina, USA using an artificial neural network. The remote sensing‐derived thematic map was cross‐referenced with Light Detection and Ranging (LIDAR) elevation data to compute the frequency distribution of marsh elevation relative to tidal elevations. At North Inlet, the median elevation of the salt marsh dominated by Spartina alterniflora was 0.349 m relative to the North American Vertical Datum 1988 (NAVD88), while the mean high water level was 0.618 m (2001 to May, 2003) with a mean tidal range of 1.39 m. The distribution of elevations of Spartina habitat within its vertical range was normal, and 80% of the salt marsh was situated between a narrow range of 0.22 m and 0.481 m. Areas classified as Juncus marsh, dominated by Juncus roemerianus , had a broader, skewed distribution, with 80% of the distribution between 0.296 m and 0.981 m and a median elevation of 0.519 m. The Juncus marsh occurs within the intertidal region of brackish marshes and along the upper fringe of salt marshes. The relative elevation of the Spartina marsh at North Inlet is consistent with recent work that predicts a decrease in equilibrium elevation with an increasing rate of sea‐level rise and suggests that the marshes here have not kept up with an increase in the rate of sea‐level rise during the last two decades.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160500219018

Affiliations: 1: Department of Geography, University of South Carolina, Columbia, SC 29208, USA 2: Belle W. Baruch Institute for Marine and Coastal Sciences, University of South Carolina, Columbia, SC 29208, USA 3: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA 4: South Carolina Geodetic Survey, Columbia, South Carolina, 29210, USA

Publication date: 2005-12-10

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