QGIS for Land Degradation Assessment & SDG monitoring in GIS

Explore QGIS, a geospatial information system for desktop mapping with plugins and cartographic tools. Learn its desktop structure, interface, cross-platform capabilities, and essential resources for GIS analysis and visualization.

Explore QGIS version information by comparing long term releases with experimental builds, and download stable versions for Windows, macOS, Linux, and Android, such as 3.20.2 and Hanover.

Explore how to manage multiple QGIS versions and plug-ins for land degradation assessment and SDG monitoring, including selecting stable releases and installing older semi-automatic classification plugin versions from zip.

Visit the QGIS website, download the installer for your platform, and choose between latest and long term releases. Run the installer, accept the license, and launch QGIS with grass integration.

Explore the Trends.Earth plugin for QGIS to monitor land degradation and land productivity using sub indicators of land cover and soil organic carbon, generating maps and SDG 15.3.1 reports.

Install the Trends.Earth plugin in QGIS using manage and install plugins, then access it from the raster menu to analyze sustainable land management and the SDG for GEF and UNCCD.

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

Explore satellite images and digital image concepts, including spatial, spectral, temporal, and radiometric resolutions, multispectral and hyperspectral bands, true and false color composites, and how vegetation appears in near infrared.

Understand active and passive sensing sensors, including radar and lidar, and multispectral versus hyperspectral imagery, and how platforms such as ground, airborne, and satellite carry them for land use classification.

Explore preprocessing of remote sensing images for land use and land cover mapping, including cosmetic operations, radiometric calibration, atmospheric correction, and geometric correction for accurate classification.

Explore the semi-automatic classification plugin for qgis, enabling supervised land cover classification, image preprocessing, change detection, and accuracy assessment using downloaded landsat and sentinel imagery.

Create a multilayer Landsat 8 image stack in Kuji software, then visualize true color and false color composites to highlight vegetation and deforestation in the Amazon rainforest.

Perform Landsat 8 radiometric correction to convert digital numbers to top-of-atmosphere reflectance and apply atmospheric correction (dark object subtraction) for multi-temporal analysis using the semi-automatic classification plugin in QGIS.

Explore diverse remote sensing sources for LULC mapping, including Landsat and Sentinel data and open portals like Google Earth Engine, Landsat Explorer, and Copernicus Open Access Hub.

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

Register with the trends.earth plugin to enable calculations and download results; complete your profile in settings, confirm your email, and log in before starting analysis with the plugin.

Use the Trends.Earth plugin to download raw environmental data, including land cover maps from the European Space Agency, for SDG land degradation indicators such as 15.3.1.

show how to load and download the LaCava land cover product from ESA in QGIS using Google Earth Engine tasks for Berlin, organizing results and viewing with base maps.

Discover how to get help with trends.earth, access data sources and datasets, and consult installation and documentation for registration, settings, quality indicators, data download/upload, and visualization tools.

Learn to calculate land degradation with the trend RSE plugin in QGIS, using three sub indicators: soil organic carbon degradation, land cover degradation, and land productivity dynamics.

Compute the sdg 15.3.1 land degradation indicator in qgis, generating a final results layer and an excel summary with spatial statistics for reporting, including productivity sub indicators.

Develop a project to compute land degradation indicators: land productivity dynamics, soil organic carbon, and land carbon changes, using default settings for 2001–2015, then map degraded, improved, and stable areas.

Explore additional remote sensing, data science, and machine learning courses on the instructor's Udemy page and Jio World YouTube channel, with free videos and updates.