Remote sensing and digital image processing with R

• Verfasst von: Alves, Marcelo de Carvalho [VerfasserIn]
• Verfasst von: Sanches, Luciana [VerfasserIn]
• Titel: Remote sensing and digital image processing with R
• Titelzusatz: Lab manual
• Verf.angabe: Marcelo de Carvalho Alves, Luciana Sanches
• Verlagsort: Boca Raton
• Verlag: CRC Press
• Jahr: 2023
• Umfang: 1 online resource (184 pages)
• Illustrationen: illustrations (black and white, and colour).
• Fussnoten: Print version record
• ISBN: 978-1-003-38041-2
• ISBN: 1-003-38041-7
• ISBN: 978-1-000-89539-1
• ISBN: 1-000-89539-4
• ISBN: 1-000-89544-0
• ISBN: 978-1-000-89544-5
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: TECHNOLOGY / Remote Sensing
• Sach-SW: SCIENCE / Earth Sciences / General
• Sach-SW: R (Computer program language)
• Sach-SW: Remote sensing ; Data processing
• K10plus-PPN: 1859055532
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4373583692
• Lokale URL Inst.: Zum Volltext

Abstract

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.