The use of Unmanned Aerial Vehicles (UAVs) has become increasingly important for high-resolution remote sensing applications, particularly for mapping coastal and shallow-water environments. Benthic habitats in shallow marine environments include seagrass meadows, macroalgae, live coral reefs, and degraded coral communities associated with sandy, muddy, and coral rubble substrates. This study mapped benthic habitats on Panggang Island, Indonesia, using multispectral imagery acquired by a DJI Phantom 4 Multispectral UAV with a spatial resolution of 6 cm per pixel. Seven benthic habitat classes were identified; sand , seagrass, live coral, dead coral with algae, coral with algae, rubble, and macroalgae. Habitat classification was performed using a pixel-based Support Vector Machine (SVM) algorithm. Classification accuracy was evaluated using a confusion matrix, yielding an overall accuracy of 87% and a Kappa coefficient of 0.84 The results demonstrate that integrating high-resolution UAV multispectral imagery with pixel-based SVM classification provides an effective approach for detailed benthic habitat mapping in small-island shallow-water environments.
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