[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128013-en":3,"doc-seo-128013-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128013,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Using Machine Learning Classification and ESA Sentinel-2 Multispectral Instrument Data to Delineate Marsh Vegetation and Measure Ecotone Movement in Coastal Georgia","Tidal marshes are dynamic ecosystems exposed to sea level rise, salinity change, and drought, leading to continual shifts in community composition and structure. Monitoring these areas is essential because they support storm protection and carbon sequestration. Focusing on Broughton Island, Georgia, the study applies geospatial workflows and supervised machine learning to track change in marsh vegetation and quantify ecotone movement among three tidal marsh domains. It evaluates multiple classifiers using remote-sensing inputs and uses vegetation indices as proxies for linking aboveground biomass to temporal ecotone movement.","Georgia Southern University  \nDigital Commons@Georgia Southern  \n\n| Electronic Theses and Dissertations | Jack N. Averitt College of Graduate Studies |\n| --- | --- |\n| Summer 2023\u003Cbr>Using Machine Learning Classification and ESA Sentinel 2 Multispectral Imager Data to Delineate Marsh Vegetation and Measure Ecotone Movement in Coastal Georgia\u003Cbr>Thomas A. Pudil\u003Cbr>Follow this and additional works at: [https://digitalcommons.georgiasouthern.edu/etd](https://digitalcommons.georgiasouthern.edu/etd)\u003Cbr> Part of the Other Environmental Sciences Commons |  |\n\nRecommended Citation  \nPudil, 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.  \n[https://digitalcommons.georgiasouthern.edu/etd/2642](https://digitalcommons.georgiasouthern.edu/etd/2642)  \nThis thesis (open access) is brought to you for free and open access by the Jack N. Averitt College of Graduate Studies at Digital Commons@Georgia Southern. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons@Georgia Southern. For more information, please [contact](contact digitalcommons@georgiasouthern.edu)[ digitalcommons@georgiasouthern.edu](contact digitalcommons@georgiasouthern.edu).  \nUSING MACHINE LEARNING CLASSIFICATION AND ESA SENTINEL-2 MULTISPECTRAL INSTRUMENT DATA TO DELINEATE MARSH VEGETATION AND MEASURE ECOTONE MOVEMENT IN COASTAL GEORGIA  \nby  \nTHOMAS PUDIL  \n(Under the direction of Christine M. Hladik)  \nABSTRACT  \nTidal 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 ofthis 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.  \nINDEX WORDS: Remote sensing, Machine learning, Tidal marshes, Satellite imagery, Temporal change, Ecosystem change, Habitat mapping  \nUSING MACHINE LEARNING CLASSIFICATION AND ESA SENTINEL-2 MULTISPECTRAL INSTRUMENT DATA TO DELINEATE MARSH VEGETATION AND MEASURE ECOTONE MOVEMENT IN COASTAL GEORGIA  \nby  \nTHOMAS PUDIL  \nB.S.EVS., Creighton University, 2021  \nA Dissertation Submitted to the Graduate Faculty of Georgia Southern University in Partial  \nFulfillment of the Requirements for the Degree  \nMASTER OF SCIENCE IN APPLIED GEOGRAPHY  \n© 2023  \nTHOMAS PUDIL  \nAll Rights Reserved  \nUSING MACHINE LEARNING CLASSIFICATION AND ESA SENTINEL-2 MULTISPECTRAL INSTRUMENT DATA TO DELINEATE MARSH VEGETATION AND MEASURE ECOTONE MOVEMENT IN COASTAL GEORGIA  \nby  \nTHOMAS PUDIL  \nMajor Professor: Ch","cbCaijA1cJulhR1W","https://ap.wps.com/l/cbCaijA1cJulhR1W","pdf",4924394,2,1,98,"English","en",105,"# ACKNOWLEDGMENTS\n# LIST OF FIGURES\n# LIST OF TABLES\n# CHAPTER 1: INTRODUCTION\n## Overview of Tidal Marshes\n## Threats to Tidal Marshes\n## Tidal Marsh Community Structure\n## Stud","[{\"question\":\"What is the main goal of the thesis on Broughton Island?\",\"answer\":\"To monitor tidal marsh change by using geospatial analyses and supervised machine learning to delineate marsh vegetation and quantify ecotone movement among marsh domains.\"},{\"question\":\"Which supervised machine learning methods are compared in the study?\",\"answer\":\"Random Forest and Artificial Neural Networks are compared to determine which supervised classification best maps vegetation species and ground cover.\"},{\"question\":\"How is ecotone movement related to vegetation biomass in the analysis?\",\"answer\":\"The study uses vegetation indices as a proxy for vegetation aboveground biomass and then quantifies the relationship between that biomass and ecotone movement over time using the most accurate classifier.\"}]","Using Machine Learning Classification and ESA Sentinel-2 Multispectral Instrument Data to Delineate Marsh Vegetation and Measure Ecotone Movement in Coastal Georgia | 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