This Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest.
- Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages.
- Engages students in learning theory through hands-on real-life projects.
- All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments.
- Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer.
- Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution information.
Undergraduate- and graduate-level students will benefit from the exercises in this Lab Manual, because they are applicable to a variety of subjects including environmental science, agriculture engineering, as well as natural and social sciences. Students will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources.
1. Principles of R Language in Remote Sensing and Digital Image Processing 2. Introduction to Remote Sensing and Digital Image Processing with R 3. Remote Sensing of Electromagnetic Radiation 4. Remote Sensing Sensors and Satellite Systems 5. Remote Sensing of Vegetation 6. Remote Sensing of Water 7. Remote Sensing of Soils, Rocks, and Geomorphology 8. Remote Sensing of the Atmosphere 9. Scientific Applications of Remote Sensing and Digital Image Processing for Project Design 10. Visual Interpretation and Enhancement of Remote Sensing Images 11. Unsupervised Classification of Remote Sensing Images 12. Supervised Classification of Remote Sensing Images 13. Uncertainty and Accuracy Analysis in Remote Sensing and Digital Image Processing 14. Scientific Applications of Remote Sensing and Digital Image Processing to Elaborate Articles