Using Remote Sensing Data in the Cloud to Monitor Climate Change in Senegal Regions Based on Seasonal Variables from 2000 to 2020. An Opportunity to Sustainable Policies

Abstract

Satellite remote sensing offers an alternative method to assess the impact of climate change in high-risk regions with limited resources. Senegal, an African country, is one of the countries most vulnerable to climate change. This study aims to find an alternative way to monitor and adapt to climate change. By evaluating correlations between vegetation, using NDVI, land surface temperature (LST), mean temperature, and precipitation from remote sensing data collected over the last 20 years (2000 to 2020) through Google Earth Engine, we have discovered a high negative correlation between NDVI and LST, a high positive correlation between NDVI and precipitation, and the lowest correlation between NDVI and mean temperature. These findings have practical implications, helping us understand the limitations and adaptations required for climate-risk countries. They can guide decisions and policies in the climate change sector, making them more relevant and applicable.