Date of Award


Document Type

Open Access Thesis


Marine Science


College of Arts and Sciences

First Advisor

James Morris


The management of saltmarshes requires detailed knowledge of the underlying processes driving their distribution in both time and space to make appropriate management decisions. With most of the world’s population living in the coastal zone and rising sea levels, one of our most important natural resources in the coastal zone faces increasing threat of collapse. This study uses the current state of Light Detection and Ranging (LiDAR) technology to model and predict saltmarsh distribution at a landscape-scale and provide evidence that a terrestrial laser scanner (TLS) can be used to estimate saltmarsh biomass for inclusion into existing models.

Land cover classification of the dominant saltmarsh species, S. alterniflora and S. patens, of the Plum Island Estuary in Massachusetts indicate that when augmented by LiDAR, aerial imagery can spectrally discriminate these species allowing for the identification of species elevation range. A spatial ‘bathtub’ model of the estuary indicates that the saltmarshes will survive a 1m sea-level rise but not without a change in the dominant marsh plant species. These changes will occur at different rates along a latitudinal gradient owing to a difference in relative marsh tidal elevation.

Although the numerical Marsh Equilibrium Model (MEM) was developed with data from North Inlet, South Carolina and has been coupled with spatial models to predict saltmarsh distribution, no such study exists for North Inlet. A stand-alone python model, MEM3D, was created to couple MEM with a Geographic Information System (GIS) and analyze the future distribution of saltmarshes within North Inlet following a 1m sea-level rise in the next 100 yr. Results indicate that the saltmarshes will not survive sea-level rise of this magnitude, and the system will switch to mudflat dominance by the end of the simulation.

A TLS was used to address the need to quickly and non-destructively estimate biomass. Results indicate that there exists an optimal resolution for collecting data in a saltmarsh and that contrary to airborne LiDAR systems, TLS can also penetrate the canopy to ground level. Predictive biomass equations are generated for S. alterniflora and J. roemerianus with R2 = 0.