Remote Sensing and Digital Image Processing with R - Lab Manual
Autor Marcelo de Carvalho Alves, Luciana Sanchesen Limba Engleză Paperback – 30 iun 2023
Features
- 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.
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Specificații
ISBN-13: 9781032461243
ISBN-10: 1032461241
Pagini: 188
Ilustrații: 7 Tables, black and white; 4 Line drawings, black and white; 40 Halftones, color; 11 Halftones, black and white; 40 Illustrations, color; 15 Illustrations, black and white
Dimensiuni: 178 x 254 x 10 mm
Greutate: 0.42 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
ISBN-10: 1032461241
Pagini: 188
Ilustrații: 7 Tables, black and white; 4 Line drawings, black and white; 40 Halftones, color; 11 Halftones, black and white; 40 Illustrations, color; 15 Illustrations, black and white
Dimensiuni: 178 x 254 x 10 mm
Greutate: 0.42 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
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Public țintă
Postgraduate, Professional, and Undergraduate AdvancedCuprins
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
Notă biografică
Marcelo de Carvalho Alves
Dr. Alves is an 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 predominantly funded by CNPq, CAPES, FAPEMIG, and FAPEMAT. Over the years, he has built 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 change.
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. Her education includes postgraduate degreees in Workplace Safety Engineering at Federal University of Mato Grosso, Brazil, and in Project Development and Management for Municipal Water Resources Management by the National Water Agency, Brazil. She is currently an associate professor at the Federal University of Mato Grosso, and worked for more than 20 years in research on atmosphere-biosphere interaction, hydrometeorology in various temporal-spatial scales with interpretation based in environmental modeling and remote sensing. She has been applying remote sensing in teaching and research activities to support the interpretation of environmental dynamics.
Dr. Alves is an 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 predominantly funded by CNPq, CAPES, FAPEMIG, and FAPEMAT. Over the years, he has built 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 change.
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. Her education includes postgraduate degreees in Workplace Safety Engineering at Federal University of Mato Grosso, Brazil, and in Project Development and Management for Municipal Water Resources Management by the National Water Agency, Brazil. She is currently an associate professor at the Federal University of Mato Grosso, and worked for more than 20 years in research on atmosphere-biosphere interaction, hydrometeorology in various temporal-spatial scales with interpretation based in environmental modeling and remote sensing. She has been applying remote sensing in teaching and research activities to support the interpretation of environmental dynamics.
Descriere
A companion to Remote Sensing and Digital Image Processing with R, this lab manual covers examples of natural resource data analysis applications including practical, problem-solving exercises and case studies that use the free and open-source platform R.