Decoupling biogeomorphic controls on coastal foredune evolution using remote sensing and in-situ measurement approaches
Presentation Type
Abstract
Faculty Advisor
Jorge Lorenzo-Trueba
Access Type
Open Access
Start Date
4-2023 12:00 AM
Description
Despite the importance of coastal dunes for protecting barrier island communities from storm and sea-level rise impacts, we lack an adequate quantitative understanding of the key geomorphic controls on aeolian processes within vegetated coastal foredunes between storm periods. There currently remains limited quantitative parameterizations of biophysical processes that are necessary for predicting dune shape evolution across time scales of relevance for coastal management. This knowledge gap is in part due to the lack of ecological and morphological data at resolutions suitable for decoupling the effects of vegetation on dune morphology. The advent of technologies, such as Unmanned Aerial Systems (UAS), Light Detection and Ranging (LiDAR), and spectral imaging, open up new ways to measure relevant biophysical feedbacks in the field at the intra-storm or longer time scales. Using data from five UAS flights and a combination of remote sensing and in-situ approaches, we quantify the key factors controlling spatio-temporal deposition rates between storms of a natural foredune system at Long Branch, NJ. We generate bed elevation surfaces and vertical change estimates from aerial LiDAR and from a grid of thirty-five measuring pipes. We use machine learning to map vegetation coverage. We analyze the effects of wind, topography, vegetation, and grain size on sedimentation rates. Preliminary results suggest that sedimentation rates are generally higher in the foredune front slopes and foredune crests. Contributing to this are differences in berm widths and vegetation coverage. These results suggest that dune geometry and vegetation are important factors in determining morphological change of dune systems.
Decoupling biogeomorphic controls on coastal foredune evolution using remote sensing and in-situ measurement approaches
Despite the importance of coastal dunes for protecting barrier island communities from storm and sea-level rise impacts, we lack an adequate quantitative understanding of the key geomorphic controls on aeolian processes within vegetated coastal foredunes between storm periods. There currently remains limited quantitative parameterizations of biophysical processes that are necessary for predicting dune shape evolution across time scales of relevance for coastal management. This knowledge gap is in part due to the lack of ecological and morphological data at resolutions suitable for decoupling the effects of vegetation on dune morphology. The advent of technologies, such as Unmanned Aerial Systems (UAS), Light Detection and Ranging (LiDAR), and spectral imaging, open up new ways to measure relevant biophysical feedbacks in the field at the intra-storm or longer time scales. Using data from five UAS flights and a combination of remote sensing and in-situ approaches, we quantify the key factors controlling spatio-temporal deposition rates between storms of a natural foredune system at Long Branch, NJ. We generate bed elevation surfaces and vertical change estimates from aerial LiDAR and from a grid of thirty-five measuring pipes. We use machine learning to map vegetation coverage. We analyze the effects of wind, topography, vegetation, and grain size on sedimentation rates. Preliminary results suggest that sedimentation rates are generally higher in the foredune front slopes and foredune crests. Contributing to this are differences in berm widths and vegetation coverage. These results suggest that dune geometry and vegetation are important factors in determining morphological change of dune systems.