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1 September 2010 Salt Marsh Geomorphological Analyses via Integration of Multitemporal Multispectral Remote Sensing with LIDAR and GIS
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Abstract

Ecogeomorphological modeling in salt marshes faces distinct challenges due to tidal oscillation and variability, fieldwork logistics, and the inherent dynamic nature of these environments. Recently developed technologies and methods introduce the capability to create fine-scale, system-wide databases to quantitatively characterize salt marsh geomorphology in support of improved understanding of the evolution of intertidal systems. This study combines the use of 20-cm AIMS-1 multispectral imagery flown at spring high and low tides with a LIDAR–derived digital elevation mode (DEM) of a New England estuarine system to quantify relationships among marsh features, their metrics, elevations, and tidal datums. The methods used to quantify features across an intertidal watershed in its entirety represent a significant advance in support of future development of process-driven models to explain these observations.

Geomorphological analyses of the distribution of marsh features at the Great Marsh, Massachusetts, study area support field observations that water-filled ponds are concentrated in regions of the marsh around and above mean higher high water (MHHW), whereas drained ponds and pannes are distributed across the marsh platform; pannes are most abundant in the high marsh. These analyses are complicated by human interference in the form of ditches, which have the general effect of both draining ponds and inhibiting pond formation, altering the natural distribution of drained ponds, pannes, and water-filled ponds. However, the geomorphic distribution of pannes and ponds indicates strong elevational control on feature formation, most likely as a function of depth and duration of inundation.

Thomas L. Millette, Brittina A. Argow, Eugenio Marcano, Chris Hayward, Charles S. Hopkinson, and Vinton Valentine "Salt Marsh Geomorphological Analyses via Integration of Multitemporal Multispectral Remote Sensing with LIDAR and GIS," Journal of Coastal Research 2010(265), 809-816, (1 September 2010). https://doi.org/10.2112/JCOASTRES-D-09-00101.1
Received: 4 August 2009; Accepted: 31 December 2009; Published: 1 September 2010
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