Environmental and climate variability in Senegal over the last two decades: a remote sensing approach to assessing climate vulnerability in a Sahelian country
- This study examines how environmental, and climate variability has evolved in Senegal over the past two decades, using a remote sensing framework focused on vegetation dynamics, surface temperature, and hydrological change. By integrating MODIS and ERA5-Land datasets within the Google Earth Engine platform, we assessed spatial and temporal trends in NDVI, NDWI, LST, temperature, and precipitation from 2001 to 2023. Our approach employs linear regression, coefficient of variation (CV), and geospatial mapping to detect climate-driven patterns and ecosystem responses at the regional scale. The results reveal increasing climate pressure in central and northern Senegal, evidenced by declining vegetation indices, rising surface temperatures, and rainfall variability exceeding 160%. In contrast, southern Senegal retains more vegetation and climate stability. Our findings emphasize strong positive correlations between vegetation and precipitation (r = 0.81 for NDVI), and a negative correlationThis study examines how environmental, and climate variability has evolved in Senegal over the past two decades, using a remote sensing framework focused on vegetation dynamics, surface temperature, and hydrological change. By integrating MODIS and ERA5-Land datasets within the Google Earth Engine platform, we assessed spatial and temporal trends in NDVI, NDWI, LST, temperature, and precipitation from 2001 to 2023. Our approach employs linear regression, coefficient of variation (CV), and geospatial mapping to detect climate-driven patterns and ecosystem responses at the regional scale. The results reveal increasing climate pressure in central and northern Senegal, evidenced by declining vegetation indices, rising surface temperatures, and rainfall variability exceeding 160%. In contrast, southern Senegal retains more vegetation and climate stability. Our findings emphasize strong positive correlations between vegetation and precipitation (r = 0.81 for NDVI), and a negative correlation between vegetation and LST (r = -0.79), suggesting the cooling effect of healthy ecosystems. This research advances regional-scale climate vulnerability mapping and supports targeted land management strategies. The methodology demonstrates the value of cloud-based remote sensing for climate monitoring in data-scarce regions, with practical applications for policymakers, environmental planners, and resilience initiatives.…
