Salt Marsh Mapping from Landsat Multispectral Data Using Machine Learning Techniques

Salt marshes are crucial coastal ecosystems that support biodiversity and provide a range of ecosystem services, including nutrient retention, shoreline erosion protection, and carbon storage, through the processes they maintain. However, their dynamic nature, combined with human pressures, has led to their rapid decline, making ongoing monitoring essential. This study presents a pixel-based classification framework for mapping and generating spatially explicit time series of salt marsh extent from satellite imagery, leveraging reflectance values and multi-spectral indices to improve accuracy and temporal consistency. The approach accounts for seasonal and hydrological variability to ensure robust detection. Four machine learning models were evaluated, with the Multi-Layer Perceptron achieving the best performance. The final model was combined with a majority filter and reached a 90% true positive rate for salt marshes, 96% for land and 97% for water, demonstrating its effectiveness for long-term ecological monitoring and conservation. Using the model, seasonal patterns and long-term trends in the salt marsh area were identified, showing strong correlations with tidal cycles and sea level changes.