Remote Sensing in QGIS: Essentials of Satellite Image Analys

Introduction to Qgis, a free open source software, focusing on its desktop interface, plugins, cartography, 3d and analysis tools for remote sensing in this course.

Learn how to choose QGIS versions, compare long-term releases with experimental builds, and use stable long-term versions across Windows, Mac, Linux, and Android.

install QGIS on Windows by downloading the latest or long-term releases from the official qgis.org site, run the installer, accept the license, and launch the desktop version.

Navigate QGIS versions and plug-ins to support reliable remote sensing analysis. Learn how to install stable releases, work with older versions, and install plugins via zip or the plugin manager.

Learn to install and manage QGIS plug-ins, including Sentinel and semi-automatic classification plugins, and customize your interface across multiple versions to enhance remote sensing analysis.

Explore the semiautomatic classification plugin for QGIS to perform supervised image classification, create training data from regions of interest or polygons, convert to reflectance, and assess accuracy.

Understand how electromagnetic waves produce spectral signatures through reflectance, absorption, and transmission, with water, vegetation, and soil showing distinct patterns used for land use classification, vegetation indices, and infrared analysis.

Differentiate active and passive remote sensing sensors, with radar and lidar as active examples and Landsat and Sentinel as passive, and explain multispectral and hyperspectral imaging and bands.

Explore three remote sensing platforms: ground-based, airborne, and satellite, and how satellite-mounted sensors enable image classification for QGIS-based satellite image analysis.

Explore satellite images and their characteristics, and define digital images and their key properties. Examine spatial, spectral, temporal, and radiometric resolutions.

Discover how a digital remote sensing image consists of pixels with brightness radiance values, and how multispectral pixels carry multiple band values like blue, green, red, and infrared.

Learn to download satellite imagery in QGIS using the semi-automatic classification plugin, including Landsat data, data portals, coordinates, dates, and cloud-cover settings.

Explore spatial, temporal, spectral, and radiometric resolutions, their roles in selecting satellite data, and how true color and false color composites reveal vegetation and surface features.

Explore Landsat sensors and data from 1972 to Landsat 8, covering spectral channels, 80-meter resolution, and level 1 to 2 products with Masferrer correction.

Use qgis to create a Landsat 8 image stack and visualize true and false color composites for vegetation mapping and forest area visualization.

Explore sentinel sensors and data in QGIS: learn about Sentinel-2A and Sentinel-2B, their multispectral instrument, processing levels, spatial and temporal resolutions, and access via the Sentinel Hub.

Plan a satellite image analysis workflow for land use and land cover mapping in QGIS, covering image selection, acquisition, preprocessing (cosmetic operations, radiometric, atmospheric, geometric corrections), and initial classification.

Explore essential remote sensing definitions such as bands, single-band files, false color composites, layer stacks, mosaics, image series, subset, and their use in Landsat imagery and area-of-interest cropping.

Apply radiometric and atmospheric corrections to convert radiance to surface reflectance, accounting for illumination, sensor response, and atmospheric effects, with cloud masking like f mask for Landsat images.

Learn to perform atmospheric and radiometric correction of Landsat 8 imagery in QGIS, converting digital numbers to reflectance with dark object subtraction and creating color composites.

Identify geometric distortions in satellite imagery caused by sensor altitude, velocity, and atmospheric effects. Use geometric correction as a pre-processing step to transform images into a standard coordinate system.

Enhance image visualization with cosmetic enhancement that increases apparent feature distinction without changing the underlying pixel values, using preprocessing and display-focused techniques like contrast manipulation, data stretching, and histogram analysis.

Learn how to enhance satellite imagery in QGIS using contrast, spatial and spectral techniques, including histogram stretching, with Landsat 5 data to reveal crops, roads, and deserts.

Explore spectral indices from satellite imagery to estimate land surface properties and vegetation greenness, using ndvi, simple ratio, and normalized burn area ratio index with near infrared and red bands.

Compute the normalized difference vegetation index (NDVI) from Landsat near infrared and red bands in QGIS using Raster Calculator and semi automatic classification plugin to map vegetation and water.

Calculate the normalize difference vegetation index in QGIS using Landsat bands 4 and 3, then threshold to classify water, bare soil, low vegetation, and high vegetation.

Create a report-ready map in QGIS using print layout, adding map, legend, title, coordinates and scale bar, then export as an image for slides or reports.

Learn to perform change detection in QGIS by using image differencing with Landsat data, computing infrared–red band indices, and reclassifying negative and positive vegetation changes between 1985 and 2013.

Explore how the EO browser cloud platform, powered by the European Space Agency, computes and visualizes spectral indices from Sentinel and Landsat imagery for vegetation monitoring and land applications.

Compare supervised and unsupervised satellite image classification, including training areas and interpretation keys, and discuss how clustering and label assignment map land cover in remote sensing.

Explore unsupervised machine learning and classification in QGIS using the semiautomatic classification plugin's ISODATA algorithm on Sentinel-2 imagery; load band sets, configure parameters, visualize results.

Apply supervised land use and land cover classification in QGIS with Landsat TM data, using the semi-automatic classification plugin, building training data, and fitting a maximum likelihood classifier.

Learn to perform accuracy assessment of a land use and land cover map created with maximum likelihood classification, using independent validation data and confusion metrics.

Explore visual change detection with Sentinel-2 imagery in the earth observation browser. Use true color and false color composites, cloud-free July 2020 data, and on-the-fly classification maps.

Apply the normalized burn ratio index to pre- and post-fire imagery to map burn severity from unburned to high and monitor vegetation recovery.

Compute the normalized burn ratio (nbr) from Sentinel-2 data using near infrared and shortwave infrared bands 8a and 12 to map burn severity in Chile with a QGIS workflow.

Train to monitor burn severity with the normalized burn ratio index in QGIS using two images and a study-area shapefile, reclassify results, and produce a final map for forest management.

Explore cloud-based image analysis with the EO Browser from the European Space Agency, compute spectral indices on the fly, visualize Sentinel-2 data, and download customized results for vegetation monitoring.

Explore remote sensing, data science, and machine learning opportunities through my Udemy page, YouTube channel Jio World, and social media for tips, updates, and discounts.