Ecology and forest management often require information across large areas and through time. Forests change because of seasonal phenology, growth, harvesting, fire, windthrow, insects, flooding, regeneration, and land-use change. Many of these processes 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 explore forest and ecological questions without downloading and processing large image archives locally.
Why this workshop?¶
This workshop introduces the potential of GEE through practical applications in ecology and forest management.
The goal is to show what kinds of analyses are possible, how typical GEE workflows are structured, and how satellite data can support ecological interpretation and forest monitoring.
We move from basic visualization to applied examples:
terrain analysis using elevation data
seasonal vegetation dynamics using Sentinel-2 NDVI
long-term forest disturbance detection using Landsat NBR
supervised classification using Random Forest
These examples are not meant to cover every detail of GEE. They are designed to provide a clear starting point for understanding the platform and adapting similar workflows to other questions.
Key concepts¶
Ecological and forest processes often vary across both space and time.
Satellite imagery can help describe vegetation condition, terrain context, and landscape change.
GEE allows users to access, filter, analyze, visualize, and export large geospatial datasets in the cloud.
Spectral indices such as NDVI and NBR are useful indicators, but they are not direct ecological measurements.
Interpretation requires field knowledge, reference data, and validation.
The same GEE workflow logic can be adapted to many applications in ecology and forest management.
Learning questions¶
What kinds of ecological and forest management questions can be explored with GEE?
How can satellite imagery help us observe forest conditions and forest change through time?
What is the difference between detecting a spatial pattern and explaining its ecological cause?
How does cloud-based processing make large-scale geospatial analysis easier?
What are the limits of using spectral indices such as NDVI or NBR to interpret ecological and management processes?