Remote Sensing and Digital Image Processing with R - Textbook and Lab Manual Multiple-component retail product

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. Features1. 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. .