Mangrove forests, while one of the most productive and endangered ecosystems in the world, are also some of the most difficult to access and study due to tidal inundation and thick understory. Along the pacific coast of Costa Rica, where 99% of the country’s mangroves occur, many of these forests reside in remote and difficult to access coastal regions. Recently, Unmanned Aerial Vehicles (UAVs) have become a commercially available and popular tool for the exploration and environmental assessment of natural areas. However few studies exist on the use of UAVs for the study of mangrove ecosystems, especially in the neotropics. This study aimed to assess forest structure on a remote mangrove forest in northwestern Costa Rica using a small UAV over a 6-month period encompassing both dry and wet seasons.The UAVs built-in camera along with an additional commercially available precision-agriculture normalized difference vegetation index (NDVI) sensor were used to create orthomosaic maps and digital surface models (DSMs) from aerial photography. From these, detailed measurements of the mangrove forest structure and diversity were derived. Structural parameters such as height, canopy coverage, species assemblage and other metrics were then compared to field-based measurements on canopy mangrove trees (≥ 5cm DBH) from 22 fourteen-meter diameter circular plots spread throughout the forest.
Results/Conclusions
UAV-derived measurements of plot maximum canopy height and canopy coverage aligned closely with field measurements, along with plot-dominant species distributions. Canopy height and canopy coverage models were then used to determine broader forest structure of the entire 1 km2 forest and a separate un-studied neighboring small mangrove forest. This study provides a comprehensive guideline from which further studies on remote, fragmented mangrove ecosystems using UAVs and precision agriculture NDVI imagery can be built, and provides new insights on the development of UAVs as a tool for measuring these rapidly disappearing ecosystems.