Remote Sensing and Digital Image Processing with R - Textbook and Lab Manual by de Carvalho Alves, Marcelo; Sanches, Luciana;

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Product details:

Edition number 1
Publisher CRC Press
Date of Publication 1 July 2023

ISBN 9781032461236
Binding Hardback
No. of pages714 pages
Size 254x178 mm
Weight 453 g
Language English
Illustrations 91 Illustrations, black & white; 245 Illustrations, color; 76 Halftones, black & white; 243 Halftones, color; 15 Line drawings, black & white; 2 Line drawings, color; 104 Tables, black & white

Short description:

This new textbook and a lab manual on remote sensing and digital image processing of natural resources includes numerous practical, problem-solving exercises that use the free and open-source platform R. It explains remote sensing and its multidisciplinary applications using R and R packages through hands-on real-life projects.

Long description:

This new textbook and lab manual on remote sensing and digital image processing of natural resources includes numerous practical, problem-solving exercises, and case studies that use the free and open-source platform R. It explains the basic concepts of remote sensing and its multidisciplinary applications using R language and R packages, and engages students in learning theory through hands-on real-life projects.

Features

1. Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages.

2. Engages students in learning theory through hands-on real-life projects.

3. All chapters are structured with solved exercises and homework and encourages readers to understand the potential and the limitations of the environments.

4. Covers data analysis in free and open-source (FOSS) R platform, which makes remote sensing accessible to anyone with a computer.

5. 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.

Students in upper-level undergraduate or graduate programs with Remote Sensing Course and Geoprocessing Course, civil and environmental engineering, geosciences, and environmental sciences, electrical engineering, biology, hydrology, agriculture Engineering. Professionals in different areas who use remote sensing and image processing.

Students in upper-level undergraduate or graduate programs taking courses in Remote Sensing and Geoprocessing, civil and environmental engineering, geosciences, and environmental sciences, electrical engineering, biology, hydrology, agricultural engineering, as well as professionals in different areas who use remote sensing and image processing, 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. Introduction to Remote Sensing with R 2. Remote Sensing of Electromagnetic Radiation 3. Remote Sensing Sensors and Satellite Systems 4. Remote Sensing of Vegetation 5. Remote Sensing of Water 6. Remote Sensing of Soils, Rocks, and Geomorphology 7. Remote Sensing of the Atmosphere 8. Scientific Applications of Remote Sensing and Digital Processing for Project Design 9. Visual Interpretation and Enhancement of Remote Sensing Images 10. Unsupervised Classification of Remote Sensing Images 11. Supervised Classification of Remote Sensing Images 12. Uncertainty and Accuracy Analysis in Remote Sensing and Digital Image Processing 13. Scientific Applications of Remote Sensing and Digital Image Processing to Elaborate Articles

Remote Sensing and Digital Processing with R - Lab Manual

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