Machine Learning Projections of Climate Change Impacts on Global Vegetation Dynamics

Understanding the potential impacts of climate change on global vegetation dynamics is crucial for effective environmental management and biodiversity conservation. This study employs a machine learning-based framework to analyze historical NDVI data and project future vegetation growth under different climate scenarios. Utilizing the GIMMS NDVI dataset (1981–2000) for model training and CMIP6 climate projections (2021–2100) for scenario analysis, the study evaluates changes in vegetation growth across four Shared Socioeconomic Pathways (SSPs). Results indicate a significant near-term increase in global mean NDVI (2021–2040) under all scenarios, followed by divergent trends. While SSP126 and SSP245 sustain modest increases, SSP370 and SSP585 show sharp declines in NDVI over the long term, driven by adverse temperature effects. Regional analyses reveal contrasting patterns: NDVI values in Africa, South America, and Oceania decline under most scenarios, while North America, Europe, and Asia exhibit potential increases, except under high-emission scenarios like SSP585. These findings underscore the importance of targeted interventions to mitigate climate impacts and highlight the role of machine learning in predicting vegetation responses to environmental changes. The study provides actionable insights for policymakers, emphasizing the need for sustainable land management practices and greenhouse gas reduction strategies to preserve global ecosystems.