Here we have learned how to conduct supervised classification with remote-sensing data. We have learned the basics of classification and the implementation details of this in QGIS using the SCP. We have used landsat images and a CIR image to do so. Land cover classification is a very useful technique to know and it must be remembered that we have used only one type of band combination for classification; there are many other band combinations of landsat that can generate useful results for different objectives, such as studying urban areas or vegetation study. In the next chapter, we will use both QGIS and R for landslide susceptibility mapping using the knowledge and skill we have gained so far.
Hands-On Geospatial Analysis with R and QGIS
By :
Hands-On Geospatial Analysis with R and QGIS
By:
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)
Preface
Free Chapter
Setting Up R and QGIS Environments for Geospatial Tasks
Fundamentals of GIS Using R and QGIS
Creating Geospatial Data
Working with Geospatial Data
Remote Sensing Using R and QGIS
Point Pattern Analysis
Spatial Analysis
GRASS, Graphical Modelers, and Web Mapping
Classification of Remote Sensing Images
Landslide Susceptibility Mapping
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