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Accession: GIS-GCET-2308 Research Theme: Geospatial Analysis (Graduate Thesis Study)
Contributors: Tyler Lynn, Merryl Alber, Jacob Shalack, Deepak Mishra
Title: Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA
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.
DOI: 10.6073/pasta/964d3375e5bbc81847fa72093bb087ef
Key Words: disturbance, GCE6, mapping, remote sensing, salt marshes
LTER Core Area: Disturbance Patterns
Research Themes: Geospatial Analysis
Study Period: 01-Jan-2020 to 31-Dec-2021
Study Sites:
DC_drone -- Dean Creek drone imagery area, Sapelo Island, Georgia, USA
» Download Geographic Coverage: Google Earth
Publications:

Lynn, T., Alber, M., Shalack, J. and Mishra, D. 2023. Utilizing Repeat UAV Imagery to Evaluate the Spatiotemporal Patterns and Environmental Drivers of Wrack in a Coastal Georgia Salt Marsh. Estuaries and Coasts. (DOI: https://doi.org/10.1007/s12237-023-01265-z)

Lynn, T., Alber, M., Shalack, J. and Mishra, D. 2023. Utilizing Repeat UAV Imagery to Evaluate the Spatiotemporal Patterns and Environmental Drivers of Wrack in a Coastal Georgia Salt Marsh. Estuaries and Coasts. (DOI: https://doi.org/10.1007/s12237-023-01265-z)

Downloads: Information

GIS Vector Data: GIS-GCE-2208_classified_wrack_polygon (Shapefile containing wrack patches classified using UAV imagery. Data are from Dean Creek marsh and span from January 2020 - December 2021.)

Access: Public (released 30-Aug-2023)

Metadata: XML (Ecological Metadata Language)

Data Formats: Zip archive [1952 kb]


Data Table: GIS-GCET-2308 (Classified wrack data as points. Includes how frequently a pixel was classified as wrack., 2001730 records)

Access: Public (released 30-Aug-2023)

Metadata: Text (ESA FLED), XML (Ecological Metadata Language)

Data Formats: Spreadsheet (CSV) [89801.02kb],  Text File [89800.71kb],  MATLAB (GCE Toolbox) [15774.46kb],  MATLAB (Variables) [15771.37kb],  Text Report [104371.56kb]

Column List:(display)


Text Data file: GIS-GCET-2308_image_dates (Drone flight dates for the images used in this study)

Access: Public (released 30-Aug-2023)

Metadata: XML (Ecological Metadata Language)

Data Formats: ASCII text [1 kb]


GIS Vector Data: GIS-GCET-2308_study_area (Shapefile containing polygon of study area)

Access: Public (released 30-Aug-2023)

Metadata: XML (Ecological Metadata Language)

Data Formats: Zip archive [3 kb]

Statistics: Generate script code to retrieve data tables for analysis in: MATLAB, R, SAS, SPSS
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

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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.