Google Earth Engine for Ecology and Conservation

Status:Accepting Applications
When:2022 dates to be announced soon
Where:Online Course
Duration:2 weeks
Deadline:November 5th, 2021
Program Guide:
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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 standard 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 an 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) to 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
    • Atmospheric effects, corrections, and its implications
    • Raster vs. vector data models
    • Resolution and their trade-offs: spatial, spectral, temporal, and radiometric
  • Introduction to Google Earth Engine
    • Data catalog – satellite products useful for conservation science and ecology
    • Earth Engine editor
    • Imagery manipulation
    • Imagery visualization
  • Google Earth Engine Fundamentals
    • Understanding and developing your code
    • Indices and atmospheric correction
    • Uploading data
    • Custom functions
    • Map functions
    • Display charts
    • Export data
  • Applications
    • Binary change detection
    • Monitoring – extract time-series data (e.g. NDVI)
    • Machine learning for species distribution modelling and land cover classification  within GEE

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
  • Operational understanding of supervised classification
  • Perform change analysis

Basic familiarity with remote sensing and/or coding background (for example QGIS/ArcGIS, R, Python or JavaScript) is highly recommended. This course will be challenging for candidates who are new to both domains (i.e.,  remote sensing and scripting). A background in biological sciences will be beneficial for 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.


Course Dates: November 10 – 232021
Sessions: 2-hour daily live sessions (Monday – Friday)
Session times: 9am – 11 am (Eastern Time)  / 4pm – 6pm (Standard South African Time)

Each session will consist of a theoretical introduction, demonstration of code, and self-learning practicals. Recordings of the live sessions will be made available to course participants. 


Tuition is $600.  Limited partial scholarships are available for students with demonstrated financial need. If you are interested in being considered for a partial scholarship from OTS, please make sure to include a scholarship motivation in your application. We will assess your situation individually and determine your eligibility for a scholarship if you are selected for the course.

Please note, seats are limited.


Dr Sandra MacFadyen 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 with BioMath, 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. 


Dr 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 machine learning to earth observation data to gain ecological insight. and is currently working on earth observation-based monitoring to aid in the management of water hyacinth across South Africa.


“This course was absolutely amazing. Each workshop was different and highly informative, and made following along incredibly easy. I would highly recommend this course to every person considering it or in need of remote sensing help.”   –  October 2020 participant

“Instructors were very knowledgeable about both theoretical and applied uses of the technology. They had each had unique and helpful experiences and backgrounds to inform and answer questions. Each was kind and welcoming, and facilitated well despite the challenges of a course across so many areas and time zones! Course material was challenging but still allowed leeway for each student to bring their own level of expertise to bear. The ability to incorporate personal data was a great opportunity for many. “ – April 2021 participant

“Outstanding training from instructors, excellent course materials, exactly what I was hoping for. Overall exceeded my expectations. “ –  October 2020 participant

“I learned a lot of amazing applications of GEE, and even some methodologies/analyses that I had never heard of before (e.g. harmonic regression). It bolstered the analyses I had heard of before (e.g. random forest modelling, time-series analysis), by explaining these in detail and walking through examples of code for these analyses. It was a steep learning curve at the start for me, as I had never used GEE or any Java Script programme. But I think it was nice to focus on the more advanced applications of GEE rather than spending too much time on the basics, because basics are something we can learn about and brush up on in our own time, while it is much harder to teach yourself advanced analyses by searching for help online. Therefore, I didn’t mind the steep learning curve, and I appreciate the broad scope the lecturers were able to cover in two weeks. By covering such a variety of topics, it also means most students would have covered something relevant to their own projects. “  – April 2021 participant

“This was an excellent online course! The instructors were organized and well-prepared, and did an excellent job teaching an exciting new conservation tool. I’m really inspired and hope to connect with OTS for further online courses. “ – October 2020 participant

“I think the course was well balanced between the necessary theory and the practicals. The practicals had extensive documentation associated that helped a lot revising them after the lessons. Considering the limited amount of time, the choice of topics was good” – April 2021 participant

“The course gave me a great overview of the potential of Google Earth Engine in Ecology. The lecturers explained and demonstrated the content in a very clear and understandable way. I am very excited to dive into GEE and learn more about it now. “ – Oct 2020 participant