Monitoring Water and Vegetation Change in the Great Green Wall of Africa Using NASA's FLDA Products and Machine Learning

Abstract

The Great Green Wall (GGW) is an international effort to grow a vast belt of trees, vegetation, and fertile land across the drylands of the Sahel in Africa. The Great Green Wall (GGW) initiative, launched by the African Union in 2007, aims to combat degraded lands, enhance food security, and build climate change resilliance in the Sahel region. This study leverages multi-dimensional data from FLDAs (Field Level Data Aggregators) to analyze the progress and impact of the GGW from 2007 to 2024. The use of FLDAs’ products has enabled precise, localized data collection and analysis, offering valuable insights into the interactions between vegetation growth and climatic factors. Monitoring key indicators such as evapotranspiration, soil moisture, air temperature, and Normalized Difference Vegetation Index (NDVI) provides a comprehensive assessment of ecological and climatic changes associated with the GGW. By
analyzing spatio-temporal features, we can better understand the dynamics and patterns of climate and environment changes over time and across different regions. The research underscores the importance of this project in addressing critical challenges within the Africa Water-Energy-Food-Health (AWEFH) Nexus, highlighting its role in the effective management of natural resources. Despite the progress made, a significant gap remains in understanding the long-term socio-economic impacts and the scalability of the GGW across different regions. Additionally, the initiative faces a lack of funding, which threatens its sustainability and
expansion. This study not only contributes to the scientific understanding of large-scale environmental initiatives but also supports policy-making and strategic planning for sustainable development in Africa. Furthermore, it aims to promote awareness and advocate  for increased funding to ensure the continued success and expansion of the GGW. Further research is neededto bridge the gap in socio-economic assessments and explore the broader implications of the GGW on local communities and economies.