Google Earth Engine for Ecology and Conservation

Status:Applications closed
When:October 19 - October 30, 2020
Where:Online Course
Duration:2 weeks
Deadline:New dates to be announced soon
Program Guide:
SKU: G-CR-FESSB-2019-1-1

Course Overview

The application of satellite-derived datasets and geospatial analysis techniques in ecology and conservation has grown substantially over the last decade. With the emergence of cloud computing platforms that facilitate big data analysis, researchers, resource managers, and remote sensing enthusiasts are now able to interrogate petabyte-scale datasets with ease. Owing to new server-based infrastructure built by Google, anyone with an internet connection and a basic computer can conduct sophisticated spatial analyses. Via online lectures, hands-on practicals, and discussion sessions, this short course will teach you the foundations of applying Google Earth Engine to answer a range of ecological and conservation questions.

What is Google Earth Engine?

Google Earth Engine is a cloud-based computing platform, which primarily uses JavaScript commands to access and analyze planetary-scale geospatial datasets drawn from a variety of platforms. Through internet-accessible application programming interface and associated web-based interactive development environment, Google Earth Engine users are able to mine a massive collection of geospatial data for change detection, resource qualification, and trend mapping on the Earth’s surface like never before.


This course aims to train students, researchers, and practitioners in the application of Google Earth Engine (GEE) in conservation science. Specifically, it seeks to familiarize participants with the basic operation of the GEE environment, focusing on visualization, analysis, and automated detection of biological patterns and processes. The course will begin with a brief review of the fundamental theory behind remote sensing and geospatial analyses, followed by a series of tutorials on the following topics: 

  • Introduction to remote sensing   
    • What is remote sensing? 
    • Approaches of capture and associated data characteristics 
    • The process of optical image capture   
    • Atmospheric effects, corrections, and its value 
    • Raster vs. vector data models 
    • Resolution and the trade-offs: spatial, spectral, temporal, and radiometric  
  • Introduction to Google Earth Engine 
    • Data catalogsatellite products useful for conservation science and ecology 
    • Earth Engine editor 
    • Imagery manipulation 
    • Imagery visualization  
  • Google Earth Engine Fundamentals 
    • Indices and atmospheric correction 
    • Uploading data 
    • Custom functions 
    • Map functions 
    • Display charts 
    • Export data 
  • Applications 
    • Binary change detection 
    • Extract time series data (e.g. NDVI) 
    • Classification  
      • What is supervised classification and how is it applied it using GEE? 
      • Training data vs. test data vs. validation data

Learning outcomes 

  • Understand the capture of optical satellite imagery 
  • Understand the trade-off between spatial, spectral, and temporal resolution 
  • Compute and interpret spectral indices 
  • Search, filter, visualize, upload, and download geospatial data using GEE 
  • Obtain time-series environmental data for further analysis 
  • General understanding of supervised classification  
  • Perform binary change detection and demonstrate the use of GEE in a conservation science framework 
  • Demonstrate software for integration 

Basic familiarity with remote sensing or coding background (for example QGIS/ArcGIS, or R) is recommended. A background in biological sciences will be beneficial for the practical examples and case studies. However, this is not essential.  Before the course begins, participants will need to create a Google Earth Engine account (at least two weeks prior).

Further details of how to prepare for the course will be sent directly to registered applicants.



This course will begin on October 19, 2020 and end on October 30, 2020. There will be a total of 10 sessions, each from 6pm-8pm (SAST). 

Each session will consist of a theoretical introduction, demonstration of code, and self-learning practicals.


OTS member institutions:  $ 500
Non-member institutions: $ 600

Additional scholarships may be available for students with demonstrated financial need. If you are interested in being considered for a partial scholarship, please make sure to include a request for a partial scholarship in your application. Successful applicants will be individually assessed to determine scholarship eligibility.

Please note seats are limited.


Sandra MacFadyen, Ph.D. is a landscape ecologist interested in macroscale ecosystem dynamics with an emphasis on applied spatial statistics for biodiversity conservation. Based in the Kruger National Park as a postdoctoral researcher (Mathematical Bioscience Hub, Mathematical Sciences, Stellenbosch University), her research interests focus on exploring the links between patterns and processes to develop a more holistic understanding of ecosystem dynamics in large protected areas.


Joseph White is a postdoctoral researcher at the University of the Witwatersrand, South Africa, working on species distribution shifts and disrupted ecosystem services in response to global change using occupancy models and remote sensed products. He is interested in spatial ecology and using earth observation to provide ecological and conservation insights.


Geethen Singh is a Ph.D. candidate at the University of the Witwatersrand, South Africa. He is interested in applying earth observation and machine learning within the domain of ecology and is currently working on better understanding invasive species spread and the invasive risk posed using earth observation-based monitoring.