Use of µUAS in enhancing/validating SLAMM Model predictions of Sea Level Rise and Coastal Wetland Migration Pathways.

Authors: Scott Graves*, Southern Connecticut State University
Topics: Coastal and Marine, Remote Sensing, Land Use and Land Cover Change
Keywords: µUAS, Drones, SLAMM modeling, Coastal Wetlands
Session Type: Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download


An important challenge in evaluating and predicting coastal salt marsh resources in the face of anticipated Sea Level Rise (SLR) using numerical modeling is that in many cases, we are at the nexus of potential scalar mismatch scenarios in the application of available data (elevation data, tidal range and descriptions of marsh species zonation - i.e. landcover). The particular Scale Mis-Match scenario highlighted here is in reference to a widely employed numerical modeling and visualization system known as Sea Level Affecting Marshes Model (SLAMM). In SLAMM I identify scale mis-match as a serious challenge in interpreting model results due to imprecise and/or inappropriate scales/resolutions in the remotely sensed data used as model input for resolving the Cove River (West Haven, CT) salt marsh responses to SLR. The data in question are Digital Surface Models (DSM) or elevation models derived from LiDAR, and tidal inundations projected from tide gauges that are miles from the wetland of interest, as well as “assumed marsh landcover classifications” across a wetland complex, based solely on predicted tidal inundation and a generalized knowledge of marsh species’ preference for living/thriving at different marsh elevations. The Coastal Planning, Engineering and Management community, as well as academicians, need to think more deeply about modeling programs such as SLAMM and it is strongly suggested that decision-making process includes continued on-ground observation, and data gathering using emerging technologies such as micro Unmanned Aerial Systems (µUAS/drones) and Structure from Motion (SfM) mapping and 3D modeling as appropriate alternative (higher resolution) model inputs.

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