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Data Release

Data set titled 'Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA' was added to the GCE data catalog

Data set titled 'Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA' was added to the GCE data catalog.  You can view the data set here:  GIS-GCET-2308

Abstract:  We used a DJI Matrice 210 UAV with a MicaSense Altum to collect a total of 20 images from January 2020 - December 2021 in a the Dean Creek marsh on Sapelo Island, GA. Wrack was classified using a principal component analysis. Wrack patches under 1 m2 were excluded from analyses. Wrack classifications were converted to polygon and point data where each point represents a 5 cm x 5 cm pixel. Those files were then used to analyze wrack characteristics, their relation to environmental drivers, and landscape based patterns. For both polygon and point data, we used the National Elevation Dataset (https://gdg.sc.egov.usda.gov/Catalog/ProductDescription/NED.html) to determine the elevation of each wrack patch. Creeks and shorelines were digitized and used to determine each wrack patches' distance to water. We calculated the frequency of wrack deposition at each point by adding together the number of images where that pixel was classified as wrack over the course of the study. Polygon data were related to tide height from a NOAA tidal station data product (Ft. Pulaski, Station 8670870; https://tidesandcurrents.noaa.gov) and wind speed and wind direction from the Marsh Landing weather station (downloaded data for the SAPMLMET met station from: https://cdmo.baruch.sc.edu/) to evaluate the relationship of wrack to environmental drivers.

Citation: Lynn, Tyler. 2023. Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA. Georgia Coastal Ecosystems LTER Project, University of Georgia, Long Term Ecological Research Network.  http://dx.doi.org/10.6073/pasta/964d3375e5bbc81847fa72093bb087ef

(Contact Adam Sapp for additional information)


submitted Sep 01, 2023

LTER
NSF

This material is based upon work supported by the National Science Foundation under grants OCE-9982133, OCE-0620959, OCE-1237140 and OCE-1832178. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.