Remote Sensing with QGIS Course

Lesson 1 • Remote Sensing Workflow Overview

Maps the end-to-end process from data acquisition to thematic output. Orients students to the full pipeline before diving into individual steps.

Lesson 2 • Electromagnetic Spectrum Basics

Covers wavelength bands, energy interactions with surfaces, and spectral signatures. Establishes the physical basis for interpreting satellite and aerial imagery.

Lesson 3 • Remote Sensing System Components

Examines platforms, sensors, and data transmission pipelines. Connects hardware architecture to the imagery characteristics students will process later.

Lesson 4 • Key Satellite Sensor Families

Surveys optical, radar, and thermal sensor families and their typical applications. Gives students a reference framework for selecting appropriate data sources.

Lesson 1 • Project Setup and CRS Management

Covers project properties, coordinate reference system assignment, and on-the-fly reprojection. Correct CRS handling prevents spatial misalignment in all subsequent work.

Lesson 2 • Interface Layout and Navigation

Introduces panels, toolbars, map canvas, and status bar functions. Students gain fluency in navigating the interface efficiently during practical exercises.

Lesson 3 • QGIS Installation and Configuration

Guides installation, plugin manager setup, and profile configuration. Ensures a consistent working environment before any analytical tasks begin.

Lesson 4 • Layer Types and Symbology Basics

Distinguishes vector and raster layer types and applies basic symbology rules. Builds visual communication skills essential for map production.

Lesson 1 • Geographic Coordinate Systems

Explains datums, ellipsoids, and latitude-longitude grids. Provides the mathematical foundation for understanding all coordinate reference systems.

Lesson 2 • CRS Assignment and Reprojection in QGIS

Demonstrates assigning CRS to layers and reprojecting vector and raster data. Directly applies CRS theory to practical QGIS data management tasks.

Lesson 3 • Projected Coordinate Systems

Covers map projections, distortion properties, and zone systems. Students select appropriate projections for area, distance, and shape-preserving analyses.

Lesson 1 • Raster Data Import and Formats

Explains GeoTIFF, IMG, and cloud-optimized raster import procedures. Prepares students to load satellite imagery and DEMs for remote sensing workflows.

Lesson 2 • Vector Data Import and Formats

Covers Shapefile, GeoJSON, GeoPackage, and KML import workflows. Students handle the most common vector formats encountered in professional GIS environments.

Lesson 3 • Data Organization and Project Hygiene

Establishes folder structures, relative paths, and layer group conventions. Prevents broken links and ensures reproducibility across team environments.

Lesson 4 • Web Services and Live Data Connections

Connects QGIS to WMS, WFS, and WCS services for live data access. Extends data sourcing beyond local files to authoritative online repositories.

Lesson 1 • Radiometric Calibration Concepts

Covers DN-to-radiance and radiance-to-reflectance conversion steps. Ensures imagery is physically meaningful before spectral analysis begins.

Lesson 2 • Geometric Correction and Orthorectification

Demonstrates GCP collection, polynomial warping, and DEM-based orthorectification. Aligns imagery to ground coordinates for accurate overlay with vector data.

Lesson 3 • Atmospheric Correction Methods

Explains dark object subtraction, image-based, and model-driven correction approaches. Removes atmospheric haze to improve spectral accuracy across scenes.

Lesson 4 • Clipping, Mosaicking, and Reprojecting Rasters

Applies clip by extent or mask, merges adjacent scenes, and reprojects outputs. Produces study-area-specific raster datasets ready for classification.

Lesson 1 • Accuracy Assessment and Validation

Constructs confusion matrices, computes overall accuracy, and calculates kappa. Validates classification quality against independent reference samples.

Lesson 2 • Supervised Classification Workflow

Guides training sample collection, classifier selection, and map production. Produces a labeled land cover raster driven by analyst-defined reference data.

Lesson 3 • Unsupervised Classification Methods

Applies k-means and ISODATA clustering to group spectrally similar pixels. Provides a rapid exploratory view of scene composition before supervised training.

Lesson 4 • Post-Classification Refinement

Applies majority filtering, sieve, and manual editing to reduce classification noise. Improves map quality before final product delivery.

Lesson 1 • Processing Toolbox and Model Builder

Introduces the Processing Toolbox interface and graphical modeler for workflow automation. Reduces manual repetition and ensures reproducible analytical pipelines.

Lesson 2 • Change Detection Analysis

Compares multi-temporal classified maps and raster difference layers to quantify change. Applies classification and geoprocessing skills to a real-world monitoring scenario.

Lesson 3 • Vector Geoprocessing Fundamentals

Covers clip, intersect, union, buffer, and dissolve operations on vector layers. Builds the core spatial manipulation skills needed for overlay analysis.

Lesson 4 • Spatial Queries and Attribute Selection

Uses select by location, select by expression, and field calculator for data subsetting. Enables targeted extraction of features meeting spatial or attribute criteria.

Lesson 1 • Cartographic Design Principles

Applies visual hierarchy, color theory, and typography to map layout design. Ensures maps communicate spatial information clearly to diverse audiences.

Lesson 2 • Data Export and Format Conversion

Exports vector and raster layers to standard delivery formats for client handoff. Ensures data interoperability with GIS platforms outside QGIS.

Lesson 3 • QGIS Print Layout Workflow

Guides map frame setup, item addition, and atlas generation in the Print Layout. Produces multi-page and atlas-driven map series efficiently.

Lesson 4 • Metadata and Documentation Standards

Covers spatial metadata elements, lineage documentation, and data quality reporting. Supports data discoverability and reuse by downstream users.