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.
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.
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.
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.
