Remote Sensing and Digital Image Processing with R - Lab Manual
- Available for pre-order on June 9, 2023. Item will ship after June 30, 2023
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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.
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.
Undergraduate and graduate level students will benefit from the exercises in this lab manual, as 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.
Table of Contents
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
Marcelo de Carvalho Alves Dr. Alves is associate professor at the Federal University de Lavras, Brazil. His education includes master’s, doctoral, and post-doctoral degrees in Agricultural Engineering at Federal University of Lavras, Brazil. He has varied research interests and has published on surveying, remote sensing, geocomputation and agriculture applications. He has over 20 years of extensive experience in data science, digital image processing and modeling using multiscale, multidisciplinary, multispectral and multitemporal concepts applied to different environments. Experimental field-sites included a tropical forest, savanna, wetland and agricultural fields in Brazil. His research has been pre- dominantly funded by CNPq, CAPES, FAPEMIG and FAPEMAT. Over the years, he has built up a large portfolio of research grants mostly relating to applied and theoretical remote sensing, broadly in the context of vegetation cover, plant diseases and related impacts of climate changes.
Luciana Sanches Dr. Sanches graduated with a degree in Sanitary Engineering from the Federal University of Mato Grosso, Brazil, a master’s degree in Sanitation, Environment and Water Resources from the Federal University of Minas Gerais, a PhD in Road Engineering, Hydraulic Channels and Ports from Universidad de Cantabria, Spain, a post-doctorate degree in Environmental Physics, Brazil, and a post-doctorate degree in Environmental Sciences from the University of Reading, United Kingdom. She specialized in workplace safety engineering and in project development and management for the Municipal Water Resources Management by the National Water Agency. She is currently associate professor at the Federal University of Mato Grosso, and worked for more than 20 years in research on atmosphere-biosphere interaction, hydrometeorology in meant temporal-spatial scales with interpretation based in environmental modeling and remote sensing. She has been applying geomatics in teaching and research activities to support the interpretation of environmental dynamics.