Mangrove forests are considered some of the most carbon-rich and productive ecosystems in the world and serve as an important factor in coastal protection. However, extreme climate events, and sea level rise, together with anthropogenic activities have been increasing the pressure on these important ecosystems, globally.

In Colombia, close to 80% of mangrove forests in the country are found along the pacific coastline. These mangroves are considered among the wettest in the world and are highly developed. Being located in a humid tropical region poses a challenge for the extraction of accurate information with optical sensors, which are affected by cloud presence.

Considering the advantage that radar data, which is not affected by cloudy conditions, and free high-resolution optical data could bring to the table, this study investigates the potential benefits of using Sentinel-1 (radar) and Sentinel-2 (optical) imagery within the GEE environment to generate accurate estimations of mangrove forest extents, while summarizing a reproducible methodology. The derived classification model will be applied to the different departments of the region and the classification outputs will be integrated into a web map application to facilitate access to the results



Acces to the GEE repository containing the scripts used to classify the mangrove cover. A GEE account is required


Access to the web map application presenting mangrove cover ditribution along the Colombian Pacific coastline.

Data download

Access to the output products from the classification, the thesis text, and poster