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Environmental Analysis with QGIS & Google Earth Engine

Name: Environmental Analysis with QGIS & Google Earth Engine
Rating: 4.2 (412 reviews)

Conduct applied environmental analysis using Remote Sensing and GIS in QGIS and Google Earth Engine

Created byKatie Alison

Last updated 1/2026

English

What you'll learn

Students will gain access to and a thorough knowledge of the QGIS, TrendsEarth and Google Earth Engine platform
To apply a range of environmental & time series analysis in QGIS and Earth Engine
Implement land degradation mapping, flood monitoring, land cover analyis and more on the cloud
Get introduced and advance JavaScript skills on Google Earth Engine platform
Learn basics of Remote Sensing and how to apply Remote Sensing analysis in open-source tools
You'll have a copy of the codes used in the course for your reference
Complete your independent environmental application project using geospatial approaches in QGIS and Google Earth Engine

Course content

9 sections • 50 lectures • 6h 3m total length

Introduction2:31
Explore geospatial analysis with Google Earth Engine and open-source tools, mastering remote sensing, JavaScript basics, and sustainable development goals indicators like land degradation, land productivity, and carbon stock changes.
Course Motivation - Sustainable Development Agenda2:48
EO for environmental applications and sustainable development

About open-source QGIS software5:47
QGIS version information2:21
Lab 1: QGIS installation12:39
A note on QGIS versions and it's plug-ins8:44
Trends.Earth Plugin for QGIS2:53
Installation of Trends.Earth Plugin2:53
Why to work with Google Earth Engine?7:27
Lab: Sign up for Google Earth Engine3:37
The updated procedure for signing in to Google Earth Engine in 2026 is provided in the Resources section of this video.

Section Overview0:59
Introduction to satellite images11:21
Learn how satellite images work, including pixels and multispectral and hyperspectral data, and compare spatial, temporal, spectral, and radiometric resolutions to choose suitable imagery for environmental applications.
Sensors and Platforms5:01
How to work with the Remote Sensing images: preprocessing9:56
Installation of Semi-Automated LCassification PlugIn2:56
Lab 2: Layerstacking, True and False Colour composites15:15
Lab 3: Image preprocessing - atmospheric correction7:50
Sources of Remote Sensing images for LULC mapping11:20

Introduction to sustainable agenda and Land Degradation7:32
Register in Trends.Earth Plugin1:35
Downloading geospatial data with Trends.Earth Plugin -Example of Land Cover Data7:04
How to load downloaded data to QGIS - Example of Land Cover Product from ESA4:24
Load the ESA land cover data in QGIS via Google Earth Engine tasks for the Berlin study area. Visualize the 300-resolution LaCava product and apply a map mask for analysis.
Where to get help on Trends.Earth2:05
Discover where to get help on Trends.Earth and access data sources, sub-indicator calculations for land degradation and land use efficiency, plus tool installation and documentation.
Lab: Land degradation assessment in QGIS with Trends.Earth - Hands-On14:53
Computation of SDG 15.3.1 Land Degradation Indicator, statistics & reporting5:04
Compute SDG 15.3.1 land degradation indicator in QGIS using land cover, soil organic carbon, and land productivity indicators with trend earth plugin. Access the resulting layer and summary for reporting.
Time series trend analysis with NDVI MODIS data in QGIS15:16
Your Independent Project1:16

Interface of Google Earth Engine: Code Editor & Explorer8:56
Explore the Google Earth Engine code editor, which writes JavaScript, visualizes maps, and manages assets, docs, and shareable scripts for environmental workflows.
Lab: Introduction to Javascript14:06
Explore JavaScript basics for geospatial analysis in Google Earth Engine, including variable declaration, objects, dictionaries, functions, comments, strings, lists, indexing, and dot and bracket access.
Lab: Declaring variables in Javascript in GEE5:17
Lab: Mapping and Reducing Collection - Landsat Example13:34
Import the Landsat collection in Google Earth Engine, reduce to a specific time window and area, and map a cloud-free Landsat composite with visualization parameters.
Lab: Working with image collections and image visualization21:54
Lab: Image Calculations - Create a composite and calculate NDVI5:42
Create a composite and compute ndvi in Google Earth Engine, using band math, the built-in ndvi function, and expression to handle raster data and visualization.
Lab: Short introduction to functions - Maximum NDVI Example10:37
Lab: Export image data from Google Earth Engine4:13

Using EO browser for image download, spectral indices & land cover analysis19:15
Working with spatial data and remote sensing images - repeat2:43
Explore how to work with spatial data and remote sensing images to map land use and land cover, including pre-processing steps such as cosmetic operations, atmospheric correction, and geometric corrections.
Floods mapping Part 1 - Image preprocessing6:50
Learn cloud masking and cloud shadow masking for sentinel-2 optical images, then apply the masks to an image collection for a study area and visualize the masked results.
Using NDWI for flood monitoring6:31
Flood Mapping Part 2: NDWI6:25
Assignment: Flood Mapping2:12

Requirements

A working computer with internet connection
An interest in working with geospatial data

Description

This course provides a complete, practical introduction to environmental geospatial analysis using QGIS, Google Earth Engine (GEE), and open-source Remote Sensing tools. It is designed for learners with basic GIS or Remote Sensing knowledge who want to develop advanced skills for environmental applications using cloud computing and Big Data.

You will learn how to analyze land degradation, monitor land cover change, map floods, assess land productivity, and perform other key environmental Remote Sensing workflows using QGIS and GEE. The course also introduces SDG-related environmental indicators using the TrendsEarth plug-in and cloud-based analysis through EO Browser.

Course Highlights

This course blends theoretical concepts with real-world environmental applications. You will work directly with satellite imagery, geospatial datasets, QGIS tools, and Google Earth Engine JavaScript code to perform modern spatial analysis at scale. You will also learn how cloud computing supports large-scale environmental monitoring and decision-making.

What You Will Learn

• Foundational Remote Sensing concepts using open-source tools
• Basics of JavaScript for geospatial analysis on Google Earth Engine
• Working with QGIS, Google Earth Engine, TrendsEarth, and the Semi-Automated Classification Plugin
• Land degradation monitoring and land productivity assessment
• Flood mapping and change detection workflows
• Land cover and land cover change analysis using satellite imagery
• Computation of SDG environmental indicators with TrendsEarth
• Practical cloud-based environmental Remote Sensing using EO Browser
• Integration of QGIS and GEE for applied environmental analysis

Course Objectives

By the end of the course, you will be able to:

• Understand and apply Remote Sensing and JavaScript basics for cloud-based spatial analysis
• Implement environmental applications on Google Earth Engine using Big Data
• Perform environmental GIS and Remote Sensing workflows in QGIS
• Use TrendsEarth in QGIS to compute land degradation and SDG indicators
• Build complete environmental analysis workflows using open-source geospatial tools
• Confidently apply geospatial methods to real environmental case studies

Practical Hands-On Experience

The course includes fully guided exercises with clear instructions, sample code, and downloadable datasets. You will perform your own environmental analyses directly in Google Earth Engine and QGIS, allowing you to build strong, practical skills for research and professional work.

Course Inclusions

Upon enrollment, you gain access to all datasets, scripts, and future resources. This course provides the tools, skills, and confidence to perform advanced environmental geospatial analysis using QGIS and Google Earth Engine.

Who this course is for:

Geographers, Programmers, geologists, biologists, social scientists, or every other expert who deals with GIS maps in their field

Environmental Analysis with QGIS & Google Earth Engine

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 5min

Introduction to software used in this course: QGIS and Google Earth Engine8 lectures • 46min

Remote Sensing Crash Course8 lectures • 1hr 5min

QGIS for Land Applications & Indicators assessment9 lectures • 59min

Drought Monitoring based on MODIS time series in QGIS and Google Earth Engine3 lectures • 24min

Basics of Jave Scrips for Google Earth Engine and first steps in image analysis8 lectures • 1hr 24min

Environmental Applications on the cloud: Google Earth Engine & EO browser6 lectures • 44min

Time Series Trend Analysis in Google Earth Engine2 lectures • 14min

Drought & Urban Heat Island analysis based on time series in Earth Engine4 lectures • 22min

Requirements

Description

Who this course is for: