Course Title: Remote Sensing for Change Detection
Course Description: This course introduces the principles, methodologies, and applications of remote sensing for detecting and analyzing changes in the Earth’s surface over time. Students will gain a comprehensive understanding of multi-temporal satellite imagery and aerial data, learning how to identify, quantify, and interpret environmental, urban, and landscape transformations.
The course covers fundamental concepts of remote sensing, including electromagnetic spectrum interactions, image acquisition, preprocessing, and radiometric and geometric corrections. Emphasis is placed on change detection techniques, such as image differencing, vegetation indices, post-classification comparison, principal component analysis, and machine learning-based approaches.
Practical sessions guide students through the use of modern GIS and remote sensing software, enabling hands-on experience in processing, analyzing, and visualizing temporal data for applications in land use and land cover monitoring, urban expansion, deforestation, disaster assessment, and environmental management.
By the end of the course, students will be able to design and implement change detection workflows, critically evaluate results, and communicate findings effectively for research, policy, and operational purposes. This course is ideal for students, professionals, and researchers in geography, environmental science, forestry, urban planning, and related fields seeking to leverage remote sensing for monitoring dynamic landscapes.
Learning Outcomes:
- Understand the theoretical foundations of remote sensing and multi-temporal analysis.
- Apply a range of change detection techniques to diverse datasets.
- Process and analyze satellite and aerial imagery using GIS and remote sensing tools.
- Interpret change detection results and assess their accuracy.
- Develop practical solutions for monitoring environmental and urban changes.
Prerequisites: Basic knowledge of GIS and remote sensing is recommended.
Course Duration: 12 weeks (or adaptable to institutional schedule).
Schedule table for a Remote Sensing for Change Detection:
| Week | Topic | Content / Activities | Deliverables / Notes |
|---|---|---|---|
| 1 | Introduction to Remote Sensing | Fundamentals of remote sensing, EM spectrum, sensors, platforms | Reading assignment |
| 2 | Multi-temporal Data | Satellite and aerial imagery, temporal resolution, data sources | Quiz on sensor types |
| 3 | Image Preprocessing | Radiometric and geometric corrections, cloud masking, normalization | Lab exercise: preprocess sample imagery |
| 4 | Basics of Change Detection | Concepts, applications, types of change | Discussion: case studies |
| 5 | Image Differencing & Vegetation Indices | NDVI, image differencing, thresholding | Lab exercise: vegetation change detection |
| 6 | Post-classification Comparison | Land cover classification, comparison methods | Assignment: land cover change map |
| 7 | Principal Component Analysis | PCA for change detection, interpretation | Lab: PCA on multi-temporal imagery |
| 8 | Advanced Techniques | Machine learning, object-based change detection | Project proposal: selected site and data |
| 9 | Accuracy Assessment | Confusion matrices, validation techniques | Lab: accuracy assessment of previous work |
| 10 | Application Case Studies | Urban expansion, deforestation, disaster monitoring | Group discussion & presentations |
| 11 | Workflow Integration | Designing end-to-end change detection workflow | Draft report submission |
| 12 | Final Project Presentations | Present results, interpretation, recommendations | Final report and presentation |
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