Book Image

Hands-On Geospatial Analysis with R and QGIS

By : Shammunul Islam, Brad Hamson
Book Image

Hands-On Geospatial Analysis with R and QGIS

By: Shammunul Islam, Brad Hamson

Overview of this book

Managing spatial data has always been challenging and it's getting more complex as the size of data increases. Spatial data is actually big data and you need different tools and techniques to work your way around to model and create different workflows. R and QGIS have powerful features that can make this job easier. This book is your companion for applying machine learning algorithms on GIS and remote sensing data. You’ll start by gaining an understanding of the nature of spatial data and installing R and QGIS. Then, you’ll learn how to use different R packages to import, export, and visualize data, before doing the same in QGIS. Screenshots are included to ease your understanding. Moving on, you’ll learn about different aspects of managing and analyzing spatial data, before diving into advanced topics. You’ll create powerful data visualizations using ggplot2, ggmap, raster, and other packages of R. You’ll learn how to use QGIS 3.2.2 to visualize and manage (create, edit, and format) spatial data. Different types of spatial analysis are also covered using R. Finally, you’ll work with landslide data from Bangladesh to create a landslide susceptibility map using different machine learning algorithms. By reading this book, you’ll transition from being a beginner to an intermediate user of GIS and remote sensing data in no time.
Table of Contents (12 chapters)
8
GRASS, Graphical Modelers, and Web Mapping

Remote Sensing Using R and QGIS

In this chapter, we delve deep into working with raster data in both R and QGIS. So far, we have covered vector data, and this chapter will focus on raster data manipulation and analysis. We'll learn how to use raster data provided by different satellites and how to get meaningful information from this data. Going over reading raster data; changing its projection; and visualizing multispectral images to computing slope, aspect, hillshade, and clipping, we'll cover a lot in this chapter, using both R and QGIS. We'll also cover reclassifying rasters, masking raster by a vector layer, and so on.

The topics covered in this chapter are as follows:

  • Basics of remote sensing
  • Working with raster data in R
  • Working with raster data in QGIS