Protocol for establishing a map of mangrove species distribution using multispectral unmanned aerial vehicle data.

Unmanned aerial vehicle (UAV) or drone image data are useful tools for creating machine learning-based mangrove categorization maps. Here, we present a protocol for creating a taxonomy map of mangrove species using machine learning and multispectral UAV images. We describe steps for gathering and analyzing UAV images and field data and categorizing mangroves.

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

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.

Free satellite image data application for monitoring land use cover changes in the kon ha nung plateau, vietnam.

Remote sensing imagery is the most suitable tool for monitoring, managing, and evaluating land-use overlay fluctuations, especially forest cover for large areas. Free- and medium-resolution satellite imagery is a useful tool that allows scientific researchers and management organizations to monitor forest development in developing countries, such as Vietnam. In this study, we used SPOT 4 and Planet remote sensing data to assess land-use status fluctuations in the Kon Ha Nung Plateau area, Vietnam, between 2000 and 2021 (the overall accuracy was 90.