Document Details

Title Using Machine Learning Classification and ESA Sentinel 2 Multispectral Imager Data to Delineate Marsh Vegetation and Measure Ecotone Movement in Coastal Georgia
Archive All Files / Documents / Publications / Theses - Dissertations
Abstract

Tidal marshes are unique communities that are subjected to environmental stressors including sea level rise, salinity change, and drought, resulting in constant change. It is important to monitor these changing areas because of the ecosystem services they provide to us, such as protection from storms and carbon sequestration. The Georgia coast is home to a large section of marsh on the Atlantic coast of the United States. This thesis project focused on the study of tidal marshes, and the dynamics between the vegetation species within them, on Broughton Island, Georgia. The aim of this project was to use geospatial technology and analyses, along with machine learning classification methods, to monitor change in these valuable ecosystems. The two objectives of this study are to 1) examine multiple machine learning algorithms to determine the best supervised classification method for the Broughton Island, Georgia, and 2) quantify the relationship between species-specific aboveground biomass of vegetation with ecotone movement between the three tidal marsh domains. Objective one of this study compared two different supervised classification methods, Random Forest and Artificial Neural Networks, to determine which supervised classification performs best in mapping vegetation species and ground cover within the study area. In objective 2, the most accurate classifier will be used to examine ecotone movement over time and quantify the relationship between aboveground biomass, using vegetation indices as a proxy, of vegetation and ecotone movement.

Contributor Thomas Pudil
Citation

Pudil, T. 2023. Using Machine Learning Classification and ESA Sentinel 2 Multispectral Imager Data to Delineate Marsh Vegetation and Measure Ecotone Movement in Coastal Georgia. M.S. Thesis. Georgia Southern University, Statesboro, GA. 94 pages.

Key Words Ecosystem change, Habitat mapping, Machine learning, Remote sensing, Satellite imagery, Student Publication, Temporal change, Tidal marshes
File Date 2023
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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.