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?
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
- Binary change detection
- Monitoring – extract time-series data (e.g. NDVI)
- Machine learning for species distribution modelling and land cover classification within GEE
- 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
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 – 23, 2021.
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.