{ "abstract": "Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.", "abstract_shown": true, "affiliations": [ "Center for Geospatial Analytics, North Carolina State University, Raleigh, North Carolina, United States of America", "Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 4, "authors": [ { "affs": [ 0 ], "name": "Titilayo T. Tajudeen" }, { "affs": [ 1 ], "name": "Marcelo Ardon" }, { "name": "Mirela Tulbure" }, { "name": "Katherine L. Martin" } ], "bibtex": "https://research.draconitic.com/paper/5fgcs1agpt40h.bib", "cited_by": 0, "doi": "10.1371/journal.pone.0357346", "fulltext_url": null, "id": "5fgcs1agpt40h", "issns": [ "1932-6203" ], "landing_url": "https://doi.org/10.1371/journal.pone.0357346", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2214|Remote-Sensing Image Classification" ], [ "plos", "/Biology and life sciences/Ecology/Ecosystems/Forests" ], [ "plos", "/Biology and life sciences/Ecology/Forest ecology" ], [ "plos", "/Biology and life sciences/Organisms/Eukaryota/Animals/Wildlife" ], [ "plos", "/Biology and life sciences/Organisms/Eukaryota/Plants/Shrubs" ], [ "plos", "/Biology and life sciences/Zoology/Animals/Wildlife" ], [ "plos", "/Earth sciences/Geomorphology/Topography/Landforms/Wetlands" ], [ "plos", "/Earth sciences/Marine and aquatic sciences/Aquatic environments/Freshwater environments/Marshes" ], [ "plos", "/Earth sciences/Marine and aquatic sciences/Aquatic environments/Freshwater environments/Wetlands" ], [ "plos", "/Earth sciences/Marine and aquatic sciences/Oceanography/Sea level change/Sea level rise" ], [ "plos", "/Ecology and environmental sciences/Aquatic environments/Freshwater environments/Marshes" ], [ "plos", "/Ecology and environmental sciences/Aquatic environments/Freshwater environments/Wetlands" ], [ "plos", "/Ecology and environmental sciences/Ecology/Ecosystems/Forests" ], [ "plos", "/Ecology and environmental sciences/Ecology/Forest ecology" ], [ "plos", "/Ecology and environmental sciences/Terrestrial environments/Forests" ], [ "plos", "/Physical sciences/Chemistry/Chemical properties/Salinity" ], [ "plos", "/Physical sciences/Chemistry/Physical chemistry/Chemical properties/Salinity" ] ], "openalex": "W7213246924", "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357346&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-15", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/5fgcs1agpt40h.ris", "sources": [ { "first_seen": "2026-10-30T12:17:50Z", "native_id": "10.1371/journal.pone.0357346", "source": "plos", "url": "https://doi.org/10.1371/journal.pone.0357346" } ], "subjects": [ { "field": "biology", "origin": "plos:ecology", "subfield": "ecology" } ], "title": "Mapping coastal forest retreat using convolutional neural networks and different satellite imagery", "title_text": "Mapping coastal forest retreat using convolutional neural networks and different satellite imagery", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:50Z", "url": "https://research.draconitic.com/paper/5fgcs1agpt40h", "venue": "PLOS One", "versions": [] }