Marcelo de Carvalho Alves, Luciana Sanches
Remote Sensing and Digital Image Processing with R (eBook, ePUB)
120,95 €
120,95 €
inkl. MwSt.
Sofort per Download lieferbar
60 °P sammeln
120,95 €
Als Download kaufen
120,95 €
inkl. MwSt.
Sofort per Download lieferbar
60 °P sammeln
Jetzt verschenken
Alle Infos zum eBook verschenken
120,95 €
inkl. MwSt.
Sofort per Download lieferbar
Alle Infos zum eBook verschenken
60 °P sammeln
Marcelo de Carvalho Alves, Luciana Sanches
Remote Sensing and Digital Image Processing with R (eBook, ePUB)
- Format: ePub
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung

Bitte loggen Sie sich zunächst in Ihr Kundenkonto ein oder registrieren Sie sich bei
bücher.de, um das eBook-Abo tolino select nutzen zu können.
Hier können Sie sich einloggen
Hier können Sie sich einloggen
Sie sind bereits eingeloggt. Klicken Sie auf 2. tolino select Abo, um fortzufahren.

Bitte loggen Sie sich zunächst in Ihr Kundenkonto ein oder registrieren Sie sich bei bücher.de, um das eBook-Abo tolino select nutzen zu können.
This new textbook on remote sensing and digital image processing of natural resources includes numerous practical, problem-solving exercises, emphasizing free and open-source platform R. It explains basic concepts of remote sensing and multidisciplinary applications and engages students in learning theory through hands-on, real-life projects.
- Geräte: eReader
- mit Kopierschutz
- eBook Hilfe
Andere Kunden interessierten sich auch für
- Marcelo de Carvalho AlvesRemote Sensing and Digital Image Processing with R - Lab Manual (eBook, ePUB)52,95 €
- Marcelo de Carvalho AlvesRemote Sensing and Digital Image Processing with R - Lab Manual (eBook, PDF)52,95 €
- Advances in Geospatial Technologies for Natural Resource Management (eBook, ePUB)187,95 €
- Marcelo de Carvalho AlvesSurveying with Geomatics and R (eBook, ePUB)48,95 €
- A. Stewart FotheringhamMultiscale Geographically Weighted Regression (eBook, ePUB)99,95 €
- Kasturi KanniahGeospatial Technology for Sustainable Oil Palm Industry (eBook, ePUB)125,95 €
- Cheng WangIntroduction to LiDAR Remote Sensing (eBook, ePUB)96,95 €
-
-
-
This new textbook on remote sensing and digital image processing of natural resources includes numerous practical, problem-solving exercises, emphasizing free and open-source platform R. It explains basic concepts of remote sensing and multidisciplinary applications and engages students in learning theory through hands-on, real-life projects.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis eBooks
- Seitenzahl: 536
- Erscheinungstermin: 30. Juni 2023
- Englisch
- ISBN-13: 9781000895414
- Artikelnr.: 68149081
- Verlag: Taylor & Francis eBooks
- Seitenzahl: 536
- Erscheinungstermin: 30. Juni 2023
- Englisch
- ISBN-13: 9781000895414
- Artikelnr.: 68149081
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
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.
1. Introduction to Remote Sensing with R 2. Remote Sensing of
Electromagnetic Radiation 3. Remote Sensing Sensors and Satellite Systems
4. Remote Sensing of Vegetation 5. Remote Sensing of Water 6. Remote
Sensing of Soils, Rocks, and Geomorphology 7. Remote Sensing of the
Atmosphere 8. Scientific Applications of Remote Sensing and Digital
Processing for Project Design 9. Visual Interpretation and Enhancement of
Remote Sensing Images 10. Unsupervised Classification of Remote Sensing
Images 11. Supervised Classification of Remote Sensing Images 12.
Uncertainty and Accuracy Analysis in Remote Sensing and Digital Image
Processing 13. Scientific Applications of Remote Sensing and Digital Image
Processing to Elaborate Articles
Electromagnetic Radiation 3. Remote Sensing Sensors and Satellite Systems
4. Remote Sensing of Vegetation 5. Remote Sensing of Water 6. Remote
Sensing of Soils, Rocks, and Geomorphology 7. Remote Sensing of the
Atmosphere 8. Scientific Applications of Remote Sensing and Digital
Processing for Project Design 9. Visual Interpretation and Enhancement of
Remote Sensing Images 10. Unsupervised Classification of Remote Sensing
Images 11. Supervised Classification of Remote Sensing Images 12.
Uncertainty and Accuracy Analysis in Remote Sensing and Digital Image
Processing 13. Scientific Applications of Remote Sensing and Digital Image
Processing to Elaborate Articles
1. Introduction to Remote Sensing with R 2. Remote Sensing of
Electromagnetic Radiation 3. Remote Sensing Sensors and Satellite Systems
4. Remote Sensing of Vegetation 5. Remote Sensing of Water 6. Remote
Sensing of Soils, Rocks, and Geomorphology 7. Remote Sensing of the
Atmosphere 8. Scientific Applications of Remote Sensing and Digital
Processing for Project Design 9. Visual Interpretation and Enhancement of
Remote Sensing Images 10. Unsupervised Classification of Remote Sensing
Images 11. Supervised Classification of Remote Sensing Images 12.
Uncertainty and Accuracy Analysis in Remote Sensing and Digital Image
Processing 13. Scientific Applications of Remote Sensing and Digital Image
Processing to Elaborate Articles
Electromagnetic Radiation 3. Remote Sensing Sensors and Satellite Systems
4. Remote Sensing of Vegetation 5. Remote Sensing of Water 6. Remote
Sensing of Soils, Rocks, and Geomorphology 7. Remote Sensing of the
Atmosphere 8. Scientific Applications of Remote Sensing and Digital
Processing for Project Design 9. Visual Interpretation and Enhancement of
Remote Sensing Images 10. Unsupervised Classification of Remote Sensing
Images 11. Supervised Classification of Remote Sensing Images 12.
Uncertainty and Accuracy Analysis in Remote Sensing and Digital Image
Processing 13. Scientific Applications of Remote Sensing and Digital Image
Processing to Elaborate Articles