Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.

Extreme low tides that expose corals to air are damaging for coral reefs. The best developed coral reef of the continental Colombian Pacific was exposed at spring tide during consecutive days in 2019. Consumer-grade drone imagery revealed the extent of the area affected. These corals have high conservation and research value.

The Ciénaga Grande de Santa Marta (CGSM), Colombia's largest coastal wetland, has been monitored for more than 25 years due to its high ecological and socioeconomic importance. Since 2020, a freshening process has been observed that likely facilitated the recent invasion of the aquatic macrophyte Hydrilla verticillata in the Pajarales Complex, the only subsystemwhere this invasion has been documented so far.This study presents a rapid and quantitative assessment of the spatial and temporal expansion of aquatic vegetation using Sentinel-2 imagery and Google Earth Engine. Monthly NDVI composites were generated for the period July 2024–January 2026, classifying water surfaces with NDVI ≥ 0.40 as vegetation-covered.Vegetation cover increased from less than 2 km² in July 2024 to more than 48 km² in January 2026 within a study area of approximately 60.7 km². Field verification conducted between September 2025 and January 2026 confirmed that the detected vegetation mainly corresponded to dense stands of H. verticillata and Eichhornia crassipes. The applied workflow provides areproducible approach for monitoring invasive aquatic vegetation in tropical coastal wetlands. VER VIDEO