Scalable NDVI Monitoring Using geeLite and Google Earth Engine
Monitoring vegetation changes over time is essential for understanding environmental health. A new approach combines satellite imagery and lightweight data processing tools to make this task more accessible and scalable. This architecture uses Google Earth Engine (GEE), geeLite, and SQLite to streamline NDVI (Normalized Difference Vegetation Index) data extraction and analysis.
At the heart of this system is Google Earth Engine, a cloud-based platform that offers global satellite data and advanced geospatial computing capabilities. Using custom queries, users can extract NDVI values, which indicate the presence and health of vegetation across targeted regions.
This data is passed through geeLite, a simplified interface that bridges GEE and local data storage. It processes and formats the extracted NDVI information before transferring it into SQLite, a lightweight, embedded database that works without the need for a dedicated server. SQLite stores NDVI readings efficiently and supports easy querying and local data management.
The stored data is then used to generate time-series graphs, showing changes in average NDVI from 2010 to 2025 across selected hexagonal grids. These visualizations help identify vegetation trends over time, including patterns linked to climate, agriculture, or deforestation. Alongside the graphs, hexagonal spatial maps illustrate NDVI distribution, using color gradients to represent vegetation density in regions such as Somalia and Djibouti.
This solution is especially useful for researchers, conservationists, and policy-makers in areas with limited computing resources. It allows for fast, scalable, and localized monitoring of vegetation without relying on expensive infrastructure. By automating the data pipeline and providing visual insights, this method empowers better environmental decision-making at regional and national levels.
