Authors:
- Is designed to be a workbook for students, researchers, and practitioners
- Includes step-by-step reference tutorials for processing optical and SAR data
- Uses free and open source software such as QGIS and R
Part of the book series: Springer Geography (SPRINGERGEOGR)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
This book introduces remotely sensed image processing for urban areas using optical and synthetic aperture radar (SAR) data and assists students, researchers, and remote sensing practitioners who are interested in land cover mapping using such data. There are many introductory and advanced books on optical and SAR remote sensing image processing, but most of them do not serve as good practical guides. However, this book is designed as a practical guide and a hands-on workbook, where users can explore data and methods to improve their land cover mapping skills for urban areas. Although there are many freely available earth observation data, the focus is on land cover mapping using Sentinel-1 C-band SAR and Sentinel-2 data. All remotely sensed image processing and classification procedures are based on open-source software applications such QGIS and R as well as cloud-based platforms such as Google Earth Engine (GEE).
The book is organized into six chapters. Chapter 1 introduces geospatial machine learning, and Chapter 2 covers exploratory image analysis and transformation. Chapters 3 and 4 focus on mapping urban land cover using multi-seasonal Sentinel-2 imagery and multi-seasonal Sentinel-1 imagery, respectively. Chapter 5 discusses mapping urban land cover using multi-seasonal Sentinel-1 and Sentinel-2 imagery as well as other derived data such as spectral and texture indices. Chapter 6 concludes the book with land cover classification accuracy assessment.
Authors and Affiliations
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Machida, Japan
Courage Kamusoko
About the author
Courage Kamusoko is an independent geospatial consultant based in Japan. His expertise includes land use and cover change modeling, and the design and implementation of geospatial database management systems. His primary research involves analyses of remotely sensed images, land use and cover modeling, and machine learning. In addition to his focus on geospatial research and consultancy, he has dedicated his time to teaching practical machine learning for geospatial analysis and modeling. Recently, he published the book Remote Sensing Image Classification in R (Springer).
Bibliographic Information
Book Title: Optical and SAR Remote Sensing of Urban Areas
Book Subtitle: A Practical Guide
Authors: Courage Kamusoko
Series Title: Springer Geography
DOI: https://doi.org/10.1007/978-981-16-5149-6
Publisher: Springer Singapore
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Hardcover ISBN: 978-981-16-5148-9Published: 03 December 2021
Softcover ISBN: 978-981-16-5151-9Published: 04 December 2022
eBook ISBN: 978-981-16-5149-6Published: 02 December 2021
Series ISSN: 2194-315X
Series E-ISSN: 2194-3168
Edition Number: 1
Number of Pages: XI, 119
Number of Illustrations: 13 b/w illustrations, 90 illustrations in colour
Topics: Computer Applications, Geography, general, Human Geography