Google Earth Engine (GEE) is a cloud-based platform designed to process and analyze vast amounts of geospatial data. With access to a rich archive of remote sensing imagery, climate datasets, and geographic information, GEE empowers researchers, conservationists, and policymakers to address critical environmental and biodiversity challenges. This primer introduces GEE, its Python API, and how GBIF users can leverage these tools for biodiversity informatics.

What is Google Earth Engine (GEE)?

GEE is a powerful platform that combines:

  • Massive Geospatial Data Archive: GEE hosts petabytes of Earth observation data, including imagery from satellites like Landsat, Sentinel, and MODIS.
  • High-Performance Cloud Computing : GEE allows users to perform complex analyses directly in the cloud, removing the need for high-end local hardware.
  • Tools for Analysis and Visualization: GEE provides an interactive environment for exploring, analyzing, and visualizing geospatial data.

Key features of GEE include:

  • Preprocessed Datasets: Ready-to-use collections, such as vegetation indices, land cover maps, and climate layers.
  • Global Scale: Perform analyses at local, regional, or global scales. Temporal Coverage: Access decades of historical data to monitor changes over time.

Why Use GEE for Biodiversity Informatics?

For GBIF users, GEE offers unique advantages for biodiversity-related research, such as:

  • Mapping Habitat Suitability: Combine species occurrence data from GBIF with environmental layers to model suitable habitats.
  • Analyzing Land Use Changes: Study the impact of deforestation, urbanization, or agriculture on species distributions.
  • Monitoring Phenology: Track seasonal changes in vegetation and their relationship to species life cycles.
  • Scaling Up: Analyze datasets over large geographic areas without the constraints of local computing power.

What is the GEE Python API?

The GEE Python API provides programmatic access to the platform, enabling automation and integration into data science workflows. Python users can:

  • Access GEE Data Collections: Query and filter datasets programmatically.
  • Perform Geospatial Analyses: Compute statistics, generate maps, and run algorithms.
  • Export Results: Save outputs as GeoTIFFs, CSVs, or other formats for use in external applications.

How to Get Started with GEE and the Python API

  1. Sign Up for GEE Access
  1. Set Up the Python Environment
  • Install the GEE Python API:
pip install earthengine-api
  • Authenticate your account by running:
import ee
ee.Authenticate()
ee.Initialize('your-project-name')
  1. Explore Data Collections Use the Python API to explore and visualize data. For example, to access Landsat 8 imagery:
import ee
ee.Initialize('your-project-name')

# Load a Landsat 8 image
image = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_044034_20140318')

# Select the Near-Infrared band
nir_band = image.select('B5')

# Print basic info
print(nir_band.getInfo())
  1. Combine GBIF Data Integrate GBIF occurrence data with environmental layers in GEE for biodiversity analysis:
  • Use GBIF’s API or export occurrence data.
  • Overlay species data on habitat layers to analyze distributions or trends.

Resources and Next Steps

By combining GEE’s robust geospatial capabilities with GBIF’s biodiversity data, researchers can unlock powerful insights to tackle ecological challenges. With just a few lines of Python code, the possibilities are vast, from tracking species distributions to monitoring habitat changes and beyond.

For further resources on integrating remote sensing data with GBIF records, check this tutorial on combining Landsat 8 imagery and GBIF records here.