Ecology and forest management often require information across large areas and through time. Forests change because of phenology, growth, harvesting, fire, windthrow, insects, flooding, regeneration, and land-use change. Many of these changes leave spatial and temporal signals that can be explored with satellite imagery.
Google Earth Engine (GEE) provides access to large geospatial datasets and cloud-based processing tools. This makes it possible to filter, analyze, visualize, and export satellite data without downloading large image archives locally.
Why this workshop?¶
This workshop introduces the potential of GEE through practical, beginner-friendly examples.
The goal is to show how GEE workflows are structured and how satellite data can support ecological analysis and forest monitoring.
We move from basic visualization to applied examples:
terrain analysis with SRTM elevation data
seasonal vegetation monitoring with Sentinel-2 NDVI
forest disturbance detection with Landsat NBR
land-cover classification with Random Forest
These examples are not meant to cover every detail of GEE. They provide a starting point for adapting similar workflows to other ecological and forest management questions.
Key concepts¶
GEE allows users to access, filter, analyze, visualize, and export large geospatial datasets in the cloud.
Satellite imagery can describe vegetation condition, terrain context, and landscape change.
Spectral indices such as NDVI and NBR are useful indicators, but they are not direct ecological measurements.
Interpretation requires ecological knowledge, reference data, and validation.
The same GEE workflow logic can be adapted to many applications.
Learning questions¶
What kinds of ecological and forest management questions can be explored with GEE?
How does GEE make satellite image analysis easier?
What is the difference between detecting a spatial pattern and explaining its cause?
What are the limits of using indices such as NDVI or NBR?