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Mangroves are critical ecosystems providing essential environmental services, making their conservation crucial. High-resolution remote sensing, particularly through unmanned aerial vehicles (UAVs), offers unprecedented resolution in ecosystem mapping. This study utilizes a multispectral sensor mounted on a UAV to map mangroves at Cardoso Island on Brazils southeastern coast and Suape on the northeastern coast. Using Geographic Object-Based Image Analysis (GEOBIA) for segmentation and classification, we employed machine learning algorithms to classify the primary mangrove species: Laguncularia racemosa, Rhizophora mangle, and Avicennia schaueriana. Two flight altitudes were used: a higher altitude for broad-scale mapping and a lower altitude for classification training, supplemented by field measurements for calibration. This method enabled the precise delineation of tree crowns and provided insights into species distribution and zonation, influenced by the distinct estuarine conditions of tides and sediment input. The high-resolution mapping achieved in this study enhances detailed analyses of mangroves, improving ecological models and biophysical estimations such as biomass and blue carbon stocks. This work highlights the potential of UAV-based multispectral imaging in advancing mangrove research and conservation efforts.
Conti et al. (Thu,) studied this question.