Synergistic use of Sentinel-2 and UAV-derived data for Plant Community Cover distribution mapping of coastal meadows with Digital Elevation Models
Abstract. Coastal wetlands provide a range of ecosystem services, yet are currently under threat from global change impacts. Thus, monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small scale studies using drones or national/regional/global scale studies using satellite derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board Unoccupied Aerial Vehicles (UAV) to calculate the Plant Fractional Cover (PFC) in Sentinel-2 MultiSpectral Instrument sensor (MSI) grids. A Random Forest algorithm was trained and tested with vegetation indices (VI) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a Digital Elevation Model (DEM). After comparing the models, results show that using DEM with VI can increase the prediction accuracy of PFC up to two times (R2 58–70 %). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes.
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