Remote Sensing Data Analysis in R

Remote Sensing Data Analysis in R is a guide book with codes for analyzing satellite data in R, providing hands-on experience for activities like loading, mapping, pre-processing, and classification. It is co-published with New India Publishing Agency and is not sold or distributed in certain countries.

Format: Hardback
Length: 364 pages
Publication date: 24 February 2021
Publisher: Taylor & Francis Ltd

Remote Sensing Data Analysis in R is a comprehensive guidebook that encompasses a wide range of codes and techniques for effectively analyzing satellite data to extract valuable insights. The primary objective of this book is to offer practical hands-on experience in conducting various activities related to remote sensing data analysis, including loading and manipulating raster and vector data, mapping and visualizing data, performing pre-processing tasks, calculating indices, applying classification and advanced machine learning algorithms. By leveraging the power of open-source freely available software, such as R, readers will gain the ability to perform most raster data analysis operations more flexibly, typically available in paid digital image processing software. It is worth noting that T&F does not sell or distribute the Hardback version of this book in India, Pakistan, Nepal, Bhutan, Bangladesh, and Sri Lanka. The title is co-published with New India Publishing Agency.

Introduction:
Remote sensing data analysis in R offers a powerful toolset for extracting meaningful information from satellite imagery. This guidebook provides a comprehensive collection of codes and techniques that enable users to perform a wide range of operations on remote sensing data. Whether you are a novice or an experienced analyst, this book will help you unlock the potential of satellite data analysis in R.

Chapter 1: Getting Started with Remote Sensing Data Analysis in R:
In this chapter, you will learn the basics of remote sensing data analysis in R. You will cover topics such as installing R, importing and exporting data, and working with different file formats. You will also learn about the available libraries and packages that can be used for remote sensing data analysis.

Chapter 2: Loading and Manipulating Raster Data:
Raster data is a fundamental type of data used in remote sensing analysis. In this chapter, you will learn how to load and manipulate raster data in R. You will cover topics such as reading and writing raster files, accessing and modifying pixel values, and performing spatial operations on raster data.

Chapter 3: Mapping and Visualization of Data:
Visualization is an essential part of remote sensing data analysis. In this chapter, you will learn how to create maps and visualizations using R. You will cover topics such as plotting data on a map, creating choropleth maps, and generating 3D visualizations.

Chapter 4: Pre-Processing and Calculation of Indices:
Pre-processing is a crucial step in remote sensing data analysis. In this chapter, you will learn how to pre-process remote sensing data using R. You will cover topics such as resampling, normalization, and feature extraction. You will also learn about the calculation of indices such as NDVI, EVI, and SAVI.

Chapter 5: Classification and Advanced Machine Learning Algorithms:
Classification and advanced machine learning algorithms are powerful tools for analyzing remote sensing data. In this chapter, you will learn how to apply classification and advanced machine learning algorithms in R. You will cover topics such as supervised learning, unsupervised learning, and deep learning.

Conclusion:
Remote sensing data analysis in R is a rapidly evolving field with numerous applications. This guidebook provides a solid foundation for anyone interested in learning remote sensing data analysis in R. By following the steps outlined in this book, you will gain the skills and knowledge necessary to carry out most of the operations of raster data analysis, more flexibly, using open-source freely available software such as R. Whether you are working with satellite imagery for environmental monitoring, urban planning, or any other field, this guidebook will be a valuable resource for you.

Weight: 706g
Dimension: 157 x 235 x 25 (mm)
ISBN-13: 9780367725624