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portada Remote Sensing and Digital Image Processing With r - lab Manual
Type
Physical Book
Publisher
Author
Language
English
Pages
188
Format
Paperback
ISBN13
9781032461243
Edition No.
1

Remote Sensing and Digital Image Processing With r - lab Manual

Marcelo De Carvalho Alves; Luciana Sanches (Author) · Crc Press · Paperback

Remote Sensing and Digital Image Processing With r - lab Manual - Marcelo De Carvalho Alves; Luciana Sanches

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Synopsis "Remote Sensing and Digital Image Processing With r - lab Manual "

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. 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.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.

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