This talk discusses leveraging global, historical, and high-frequency remote sensing data to monitor and analyze environmental variables, particularly focusing on ecosystem resilience in forests. Forest ecosystems' importance in the carbon cycle and climate change mitigation strategies is highlighted. The study emphasizes the need to account for climate-related confounding factors in analyzing vegetation anomalies and predicting resilience. A machine learning model is introduced to explore relationships between environmental metrics and forest resilience indicators. The workflow involves processing time-series of vegetation, climate, and other environmental data, addressing challenges like deseasonalization, detrending, and confounding effects removal. It emphasizes open data and tools, using Google Earth Engine and the Joint Research Centre Big Data Analytics Platform for data processing. The study showcases the diversity of data sources, formats, and tools employed. The ultimate goal is to present a workflow for handling vegetation-related time-series data in a geospatial context, emphasizing the role of open data and open-source tools in enabling such analyses.