15 D1. Geomorphology (Posters)

Monday, 15 March 2021
10:15 AM - 12:00 PM
Windsor

ANALYZING COASTAL GEOMORPHOLOGY AT THE RUTGERS UNIVERSITY MARINE FIELD STATION WITH REMOTE SENSING TECHNOLOGY

Paper number: 15-2
Program: Technical Sessions
Time: 10:20 AM – 10:30 AM

Authors

Abstract

Geodesy is the study and measurement of temporal changes of the earth’s surface (Torge, 1991). Remote sensing technology has quickly advanced in recent years, increasing its presence and usefulness in geodesy. Unmanned aerial vehicles (UAVs) collect images for photogrammetric software, which creates three-dimensional (3-D) models of topography that are utilized for geodetic analysis (Buckley et al., 2019). Remote sensing can increase understanding of coastal geomorphology, which requires frequent measurements to quantify its high morphodynamic activity (Casella et al., 2020). Despite the importance of coastal management, environmental monitoring of various coastal types is under-researched (French & Burningham, 2011).

This study monitors beach topography at the Rutgers University Marine Field Station in Little Egg Harbor Township, NJ. Environmental surveys are scheduled monthly at low tide, with additional surveys completed before and after significant storm events. UAVs were used to gather high-resolution images of the landscape (Nex & Remondino, 2014). Point clouds were from this imagery were created using the Structure from Motion (SfM) computer algorithm to produce a time series analysis of the changes to the dry beach. While it is well known most coastal weathering occurs during the summer and during significant storm events (French & Burningham, 2011 and Casella et al., 2020, respectively), this work will allow the more precise monitoring of when major erosional events are occurring throughout the year. Though work is ongoing, preliminary results demonstrate the viability of these methods for measuring and quantifying the subannual changes to the coastal geomorphological processes.

Geological Society of America Abstracts with Programs. Vol. 53, No. 1

doi: 10.1130/abs/2021NE-361846

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