Introduction to Spatial Data Analysis

by Martin Wegmann, Jakob Schwalb-Willmann, Stefan Dech

Overview

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions.

An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques.

This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data.

The software used will be the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org.

This book covers specific methods including:

• what to consider before collecting in situ data

• how to work with spatial data collected in situ

• the difference between raster and vector data

• how to acquire further vector and raster data

• how to create relevant environmental information

• how to combine and analyse in situ and remote sensing data

• how to create useful maps for field work and presentations

• how to use QGIS and R for spatial analysis

• how to develop analysis scripts

Product Details

ISBN-13:	9781784272135
Publisher:	Pelagic Publishing
Publication date:	11/07/2020
Pages:	300
Product dimensions:	6.76(w) x 9.68(h) x 0.43(d)

About the Author

Martin Wegmannhas a PhD in remote sensing focusing on time-series analysis on land cover change and fragmentation in Africa. He is an assistant professor at the Global Change Ecology Msc program at the University of Würzburg, Germany and runs courses in remote sensing analysis for biodiversity and conservation.

Stefan Dech is director of the German Remote Sensing Data Center (DFD) since 1998, and current spokesman of the Earth Observation Center (EOC) at the German Aerospace Center (DLR). Since 2001 he has held the Chair for Remote Sensing at the Institute of Geography and Geology of the University of Würzburg. 

Table of Contents

Preface viii

1 Introduction and overview 1

1.1 Spatial data 1

1.2 First spatial data analysis 9

1.3 Next steps 35

Part I Data acquisition, data preparation and map creation

2 Data acquisition 38

2.1 Spatial data for a research question 38

2.2 AOI 40

2.3 Thematic raster map acquisition 43

2.4 Thematic vector map acquisition 44

2.5 Satellite sensor data acquisition 47

2.6 Summary and further reading 53

3 Data preparation 54

3.1 Deciding on a projection 54

3.2 Reprojecting raster and vector layers 56

3.3 Clipping to an AOI 59

3.4 Stacking raster layers 61

3.5 Visualizing a raster stack as RGB 62

3.6 Summary and further reading 63

4 Creating maps 64

4.1 Maps in QGIS 67

4.2 Maps for presentations 74

4.3 Maps with statistical information 80

4.4 Common mistakes and recommendations 83

4.5 Summary and further reading 83

Part II Spatial field data acquisition and auxiliary data

5 Field data planning and preparation 86

5.1 Field sampling strategies 87

5.2 From GIS to global positioning system (GPS) 91

5.3 On-screen digitization 92

5.4 Summary and further reading 96

6 Field sampling using a global positioning system (GPS) 97

6.1 GPS in the field 98

6.2 GPX from GPS 101

6.3 Summary 102

7 From global positioning system (GPS) to geographic information system (GIS) 103

7.1 Joint coordinates and measurement sheet 104

7.2 Separate coordinates and measurement sheet 105

7.3 Point measurement to information 106

7.4 Summary 108

Part III Data Analysis and new spatial information

8 Vector data analysis 110

8.1 Percentage area covered 114

8.2 Spatial distances 118

8.3 Summary and further analyses 121

9 Raster analysis 122

9.1 Spectral landscape indices 122

9.2 Topographic indices 128

9.3 Spectral landscape categories 128

9.4 Summary and further analysis 133

10 Raster-vector intersection 134

10.1 Point statistics 135

10.2 Zonal statistics 136

10.3 Summary 138

Part IV Spatial Coding

11 Introduction to coding 140

11.1 Why use the command line and what is 'R'? 140

11.2 Getting started 142

11.3 Your very first command 142

11.4 Classes of data 144

11.5 Data indexing (subsetting) 145

11.6 Importing and exporting data 147

11.7 Functions 148

11.8 Loops 149

11.9 Scripts 149

11.10 Expanding functionality 150

11.11 Bugs, problems and challenges 151

11.12 Notation 152

11.13 Summary and further reading 152

12 Getting started with spatial coding 153

12.1 Spatial data in R 153

12.2 Importing and exporting data 158

12.3 Modifying spatial data 162

12.4 Downloading spatial data from within R 166

12.5 Organization of spatial analysis scripts 170

12.6 Stunmary 171

13 Spatial analysis in R 172

13.1 Vegetation indices 172

13.2 Digital elevation model (DEM) derivatives 174

13.3 Classification 175

13.4 Raster-vector interaction 179

13.5 Calculating and saving aggregated values 182

13.6 Summary and further reading 184

14 Creating graphs in R 185

14.1 Aggregated environmental information 185

14.2 Non-aggregated environmental information 189

14.3 Finalizing and saving the plot 194

14.4 Summary and further reading 195

15 Creating maps in R 196

15.1 Vector data 197

15.2 Plotting study area data 202

15.3 Summary and further reading 206

Afterword and acknowledgements 207

References 209

Index 210