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Remote Sensing and Digital Image Processing with R - Lab Manual
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!
| Lieferstatus: |
 Auf Bestellung (Lieferzeit unbekannt) |
| Veröffentlichung: |
Juni 2023
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| Genre: |
Naturwissensch., Medizin, Technik |
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advanced R image classification workflow / Aster GDEM / atmospheric monitoring / Coffee Crops / COMPUTERS / Optical Data Processing / Dn Image / Earth observation satellites / ESRI Shapefile |
| ISBN: |
9781032461243 |
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EAN-Code:
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9781032461243 |
| Verlag: |
Taylor and Francis |
| Einband: |
Kartoniert |
| Sprache: |
English
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| Dimensionen: |
H 254 mm / B 178 mm / D 9 mm |
| Gewicht: |
420 gr |
| Seiten: |
178 |
| Illustration: |
schwarz-weiss Illustrationen, farbige Illustrationen, Raster,schwarz-weiss, Raster, farbig, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss |
| Bewertung: |
Titel bewerten / Meinung schreiben
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| Inhalt: |
| 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. |
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