Environmental Analysis with QGIS & Google Earth Engine

Discover open-source QGIS, a free cross-platform geographic information system with desktop, browser, server, web client, and Android components; explore its plugin ecosystem and cartography tools.

Discover how to choose a stable long-term release of QGIS, avoid experimental builds, and ensure cross-platform compatibility (Windows, Mac, Linux, and Android), with minor interface differences noted.

learn to install QGIS on Windows, choosing between latest and long-term releases, running the installer, agreeing to the license, and launching QGIS (with grass integration).

Choose stable QGIS versions and manage plugin compatibility for environmental analysis. Learn to install older QGIS versions and plugins from zip, and to enable the semi-automatic classification plugin.

Explore the Trends.Earth plugin for QGIS to plot land degradation indicators, monitor land conditions, and generate SDG 15.3.1 maps using global data and baselines.

Install the Trends.Earth plugin in qgis via the plugins manager, then use the new Trends.Earth menu under raster to access options and the built-in user guide.

Explore how Google Earth Engine enables global-scale geospatial analysis for environmental applications on a cloud platform with Python and JavaScript APIs, a code editor, and Explorer.

Explore the basics of remote sensing, including satellite images, sensor types, and key processing steps from pre-processing to analysis. Learn true and false color composites, atmospheric correction, and data sources.

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.

Explore active and passive remote sensing sensors, radar and LiDAR, on ground-based, airborne, and satellite platforms carrying Landsat and Sentinel imagery for multispectral and hyperspectral data in land use classification.

Preprocess remote sensing images for land use and land cover mapping by applying cosmetic operations, radiometric and atmospheric corrections, and geometric correction. Check cloud masking and image quality before analysis.

Install and explore the semi-automatic classification plugin for QGIS, enabling image download, preprocessing, training data creation, classification, change detection, and accuracy assessment with case studies and documentation.

Learn to create a multilayer layer stack from Landsat 8 bands and generate true color and false color composites for visualizing vegetation and deforestation.

Learn radiometric and atmospheric correction of Landsat 8 data using semi-automatic classification to convert digital numbers to reflectance, create a color composite, and generate a corrected band stack.

Explore diverse remote sensing image sources for land use and land cover mapping, including Landsat Explorer, Sentinel Hub, Google Earth Engine, Earth Explorer, and the Copernicus Open Access Hub.

Learn to compute sustainable development goal indicator 15.3.1 for land degradation using land cover change, productivity, and carbon stocks with baselines 2000–2015 via trend urse plugin and Google Earth Engine.

Register with the Trends.Earth plugin by opening settings and entering your email, name, organization, and country. Verify your email, then sign in or reset your password to start analysis.

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.

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.

Learn to calculate land degradation indicators: land productivity decline, land degradation, and soil organic carbon degradation using the trend RSE plugin in QGIS with unsubsidised data and a 2001–2015 baseline.

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.

Use the Trend Earth tool in QGIS to perform time series trend analysis with NDVI MODIS data. Apply linear regression to assess vegetation productivity changes.

Compute land degradation indicators—land productivity dynamics, soil, organic carbon, and land carbon changes—using 2001–2015 data, then map with Kuzyayev and assess degraded, improved, and stable areas.

Monitor drought with MODIS time series in QGIS. Use 250-meter imagery and NDVI anomalies to compare July 2015 against July 2001–2010 in California.

Continue drought monitoring with MODIS time series in QGIS, cropping to California and clipping rasters by extent, saving layers for later anomaly calculations.

Calculate an average MODIS image from 2001–2010 over cropped California, subtract it from July 2015 to produce a normal anomaly, identifying distressed vegetation as drought indicators.

Explore the Google Earth Engine code editor, which writes JavaScript, visualizes maps, and manages assets, docs, and shareable scripts for environmental workflows.

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.

Discover the basics of JavaScript in Google Earth Engine by declaring and using numbers, strings, lists, and objects, and learn proper syntax, scoping, and the print function.

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.

Import Landsat eight collection of top-of-atmosphere reflectance into Google Earth Engine, filter by region geometry and date, and compute a median image for visualization over the study area.

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.

Learn to write a custom JavaScript function in Google Earth Engine to compute a normalized difference vegetation index on Landsat data and map the per-pixel maximum NDVI over time.

Export imagery from Google Earth Engine to Google Drive or cloud storage by configuring region of interest, scale, and projection, then run the export task.

Explore the EO browser for cloud-based image access and on-the-fly spectral indices analysis. Use Sentinel and Landsat data for land cover visualization and vegetation monitoring.

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.

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.

Apply the ndwi with sentinel-2 imagery in Google Earth Engine to map water bodies and monitor floods using green-near infrared and near infrared-shortwave infrared variations with thresholds.

Compute the normalized difference water index (ndwi) from sentinel images after cloud masking, then apply a maximum ndwi and a water threshold to map floods.

Practice flood mapping with QGIS and Google Earth Engine, adjusting Sentinel date ranges, thresholds, and the normalized different water index to determine optimal monitoring parameters for your study area.

Apply linear regression to MODIS ndvi time series in Google Earth Engine to derive slope maps of vegetation trends, with year-since-2000 bands for study areas.

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