Mapping tree species of wetlands using multispectral images of UAVs and machine learning: A case study of the Dong...

Wetlands provide resources, regulate the environment, and stabilize shorelines; however, they are among the most vulnerable ecosystems in the world. The classification of mangrove species allows the determination of the habitat of each species, thereby serving as a basis for determining protection solutions and planning plans for mangrove conservation and restoration according to each environmental condition. We used Phantom 4 multispectral unmanned aerial vehicles (UAVs) to collect data from wetland areas in the Dong Rui Commune, which is one of the most diverse and valuable wetland ecosystems in northern Vietnam. A tree-species classification map was constructed through a combination of the object-based image analysis method and spectral reflectance values of each plant species, and the characteristic distributions of mangrove plants, including Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, were determined with an overall accuracy of 91.11 % and a kappa coefficient (K) of 0.87. The overall accuracy for Rhizophora stylosa was the highest (94.23 %), followed by Kandelia obovata (93.61 %) and Bruguiera gymnorrhiza (85.50 %). An experiment was conducted to map plant taxonomy in the same area based only on a graph of spectral reflectance values at five single-spectral bands, and normalized difference vegetation index values were constructed, resulting in an overall accuracy of 78.22 % and a K of 0.67. The constructed map is useful for classifying, monitoring, and evaluating the structure of each group of mangroves, thereby serving as a basis for determining the distribution of each mangrove species according to natural conditions and contributing to the formulation of policies for afforestation and mangrove conservation in Dong Rui commune.