Learn how to harness the combined power of Google Earth Engine (GEE) and QGIS to visualize and analyze satellite imagery—right from your desktop. In this hands-on, one-day course, you’ll work with real satellite data to explore geospatial workflows for environmental monitoring, land use planning, and more. There’s no need for advanced coding or high-performance hardware—just practical tools and step-by-step guidance to help you work smarter with remote sensing and spatial data.
Google Earth Engine QGIS
GoogleGoogle Earth Engine and QGIS
Google Earth Engine (GEE) is a cloud-based platform built for large-scale geospatial analysis. It offers instant access to a vast archive of satellite imagery and global Earth observation data. As a result, users can analyze changes on the planet’s surface over time. Whether for environmental monitoring or disaster response, GEE allows you to run advanced remote sensing tasks without local hardware or complex code.
When combined with QGIS — one of the most popular open-source desktop GIS tools — GEE becomes even more powerful. A dedicated plugin enables QGIS users to access GEE’s cloud-based tools directly from the QGIS interface. In this way, it blends cloud processing with desktop visualization in a seamless experience.
With this setup, you can visualize satellite data such as Landsat-8, apply NDVI calculations to assess vegetation health, and track land cover changes. You’ll also be able to monitor ecosystems over time and space. Consequently, you can generate accurate, detailed visuals that support planning, research, and environmental decisions.
Best of all, the process is designed for usability. The GEE plugin creates a smooth connection between platforms. This means you can work faster, uncover insights more easily, and export high-quality maps and results — all without leaving QGIS. Whether you’re mapping urban growth or assessing forest decline, this integration helps you get more from your spatial data in less time.
What will you learn
This course provides a hands-on introduction to satellite imagery using the combined power of GEE and QGIS. To begin with, you’ll complete guided exercises based on real-world environmental scenarios. These activities will show you how to apply geospatial data in meaningful ways.
First, you’ll learn the fundamentals of GEE and how it connects with QGIS. Then, you’ll install and configure the GEE plugin. After that, you’ll dive into Landsat-8 imagery — visualizing data with false-color composites and performing NDVI analysis to assess vegetation health.
Along the way, you’ll build confidence in interpreting satellite data for land use and environmental monitoring. Additionally, you’ll learn to generate and export results for mapping, reporting, or deeper analysis. By the end, you’ll have a complete, repeatable workflow you can use well beyond the course.
Why choose this course
At Geo-ICT, we don’t just teach theory—we show you how to use tools the way professionals do in the field. Whether you’re a GIS beginner or already familiar with QGIS, this course equips you with future-proof skills in geospatial analysis.
Why learners choose this course:
- It connects GEE’s cloud power with QGIS’s visualization strengths
- It uses real-world examples like vegetation monitoring and land use planning
- It’s designed to be fast, practical, and beginner-friendly
- It gives you tools you can apply across industries—from urban planning to environmental science
With growing demand for spatial analysis in research, policy, and environmental work, the skills you gain here are both relevant and in-demand.
Course structure
Day 1
This one-day course is designed to give you a complete, hands-on experience—from installation to analysis.
You’ll begin by learning the fundamentals of Google Earth Engine: what it is, how it works, and the kinds of satellite data it gives you access to. From there, you’ll shift into QGIS, where you’ll install and configure the GEE plugin to connect both platforms.
Once you’re set up, you’ll dive into practical exercises. You’ll import Landsat-8 imagery, explore both RGB and false-color visualizations, and begin identifying key landscape features. In the afternoon, you’ll calculate NDVI to assess vegetation health and apply that analysis to real-world scenarios like environmental monitoring and sustainable land planning.
By the end of the day, you’ll have completed a full geospatial workflow—from setup and image import to analysis and visualization—using the combined power of GEE and QGIS.
Learning Outcomes
After completing this course, you’ll be able to:
- Explain how Google Earth Engine integrates with QGIS for geospatial analysis
- Install and use the GEE plugin within the QGIS interface
- Import and work with Landsat-8 imagery to create composite visualizations
- Perform remote sensing calculations such as NDVI
- Apply geospatial data to solve real-world problems in environmental and urban planning
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
Frequently Asked Questions about Google Earth Engine QGIS
Applying Google Earth Engine together with the GIS package QGIS is a powerful tool for geospatial analyses and visualizations, allowing you to analyze and visualize satellite images and geospatial datasets using the QGIS plugin.
This course is ideal for aspiring geo-specialists, experienced professionals, companies in the geospatial sector, career switchers, and educational institutions looking to improve their skills in geospatial analyses.
You will learn how to integrate the Google Earth Engine plugin with QGIS, process Landsat-8 satellite images, perform spatial analyses, and apply environmental monitoring and land-use planning.
Yes, it is recommended that you have basic knowledge of QGIS. The QGIS Basics Course is a good preparation.
This course offers a unique combination of theory and practice, focused on using Google Earth Engine within QGIS for advanced geospatial analyses and visualizations.