Term of Award
Summer 2023
Degree Name
Master of Science, Applied Geography
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Geology and Geography
Committee Chair
Christine Hladik
Committee Member 1
Chester Jackson
Committee Member 2
Munshi Rahman
Committee Member 3
John Schalles
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 proposed work for this thesis project is focused on the study of tidal marshes and the dynamics between the vegetation species within them. The aim of this project is to use geospatial technology and analyses, along with machine learning classification methods, to monitor change in these valuable ecosystems. The Georgia coast is home to a large section of marsh on the Atlantic coast of the United States, and this project will take advantage of the benefits provided by previous work in this area. The two objectives of this study are to 1) examine multiple machine learning algorithms to determine the best supervised classification method for the Georgia coast, 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 will compare 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 of vegetation and ecotone movement.
OCLC Number
1413970384
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916562042502950
Recommended Citation
Pudil, Thomas A., "Using Machine Learning Classification and ESA Sentinel 2 Multispectral Imager Data to Delineate Marsh Vegetation and Measure Ecotone Movement in Coastal Georgia" (2023). Electronic Theses and Dissertations. 2642.
https://digitalcommons.georgiasouthern.edu/etd/2642
Research Data and Supplementary Material
No
