Forest Resilience, Precipitation, and Ecosystem Service Value: A Correlation and Trend Analysis

Ecosystem service value (ESV) is critical for understanding ecosystems' economic benefits and their responses to environmental change. This study uses Earth Observation (EO) data, statistics, and machine learning methods to evaluate ESV trends across multiple continents from 2000 to 2024. Three key datasets were used: MODIS NDVI for vegetation monitoring, CHIRPS precipitation data, and the ESVD database. Data preprocessing includes data cleaning, feature engineering, and outlier detection. We compared Random Forest, XGBoost, and ensemble stacking models to predict key variables and their relationships, such as kNDVI, as a proxy of forest resilience and ESV trends, precipitation patterns, and biome-specific variables. Our results show that kNDVI changes across continents reveal various patterns in vegetation dynamics that are in-line with precipitation patterns and are weakly correlated with ESV changes for forest biomes. Our study emphasizes the significance of time interdependence and climate variability in ESV predictive modelling. Future efforts focus on refining the time/space granularity of data collection and aggregation techniques and incorporating more environmental indicators to improve model robustness and application in policy-making.