Modeling the Potential Distribution of Seagrass Beds in the Joal-Fadiouth Marine Protected Area (Senegal) Using Satellite Data and Environmental Parameters

Amadou Sadio DIALLO^1*Bocar SY¹Dieynaba SECK²

• ¹Universite Amadou Mahtar Mbow, Dakar, Senegal
• ²Centre de Suivi Ecologique, Dakar, Senegal

Seagrass beds are critical coastal ecosystems that play a fundamental role in carbon sequestration, sediment stabilization, and marine biodiversity contributing directly to UN Sustainable Development Goal 14 (SDG 14), particularly target 14.5 which aims to conserve at least 10% of coastal and marine areas by 2030 (UN, 2015). Along the West African coast, and particularly in Senegal, their spatial distribution remains poorly documented. To address this gap, this study, based on 2022 satellite data and conducted under the SEDAD (Ecosystem solutions Ecosystem for sustainable adaptation) project framework. It proposes an integrated approach combining remote sensing, environmental modeling, and participatory knowledge. Satellite data (Sentinel-2 and Landsat 9) were processed in Google Earth Engine to extract spectral indices used as proxies of environmental gradients, including the Normalized Difference Turbidity Index (NDTI), the Normalized Difference Chlorophyll Index (NDCI), and the Normalized Difference Salinity Index (NDSI), as well as sea surface temperature derived from Landsat thermal data. These indices were integrated into a binary logistic regression model to predict the probability of seagrass presence. Additionally, field and participatory surveys were used to guide data interpretation. Spectral indices such as NDVI, EVI, MSAVI2, and NDWI were computed to enhance underwater vegetation detection. The logistic regression model achieved a strong predictive performance, with 81.1% overall accuracy and 76.0% recall. Model robustness was confirmed through a 5-fold cross-validation, ensuring the stability of predictive performance. Among the predictors, NDTI and surface temperature emerged as the most influential variables (38% and 34% relative importance, respectively), while NDCI and NDSI played a secondary role. These findings underscore the value of combining satellite-derived spectral indices with environmental spectral indices to map seagrass meadows in shallow coastal waters. The resulting probability map provides an operational basis for monitoring and managing priority habitats in Senegal's Marine Protected Areas, and represents a replicable framework for coastal ecosystem assessment across West Africa.