2026-10-13T16:29:39Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33815874 2026-09-15T18:10:15Z category_272 category_39 category_45 category_734 portal_5 item_type_3 month_year_09_2026

<p>Potential predictors of forest change.</p> Titilayo T. Tajudeen (24950850) Marcelo Ardon (3292782) Mirela Tulbure (2905247) Katherine L. Martin (21170100) Environmental Sciences not elsewhere classified Ecology Sociology Biological Sciences not elsewhere classified situated among salt sea level rise land cover classification e ., multispectral deep learning models convolutional neural networks coastal ecosystem degradation land cover change key environmental drivers elevation upland forests term data record topographical indices enhances healthy coastal forests 5 times higher forecasting future changes outperformed landsat images best available data ghost forests class 968 ha lost forest landscape conversions term change coastal plain 2 data ghost forests environmental stressors best method available year 876 ha 2 images wetland forests landsat remains landsat 8 ghost forest vegetation classes tolerant shrubs study reveals storm surges saturated soil saltwater intrusion rslr ), rising salinization phenological metrics open water main objective landscapes dominated increasingly threatened incorporating phenology identify patterns <div><p>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.</p></div> 2026-09-15T18:09:50Z Dataset Dataset 10.1371/journal.pone.0357346.t003 https://figshare.com/articles/dataset/_p_Potential_predictors_of_forest_change_p_/33815874 CC BY 4.0