An Introduction to Spatial Data Analysis

Martin Wegmann works on remote sensing for biodiversity and conservation applications at the Department of Remote Sensing, University of Würzburg. He also teaches remote sensing within the applied Earth Observation EAGLE M.Sc. program and the AniMove.org science school. He has more than 15 years of experience in working with spatial data for ecological applications using Open Source software. Jakob Schwalb-Willmann is a scientist at the University of Würzburg with an academic background in Earth observation and spatial data science. His research focuses on the machine-learning-driven analysis and exploitation of integrated movement tracking and remote sensing data for geoanalytical applications. He has extensive experience in using and developing Open Source software tools for advanced image and spatial data anaylsis. 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. 

Preface

1. Introduction and overview

1.1 Spatial data

1.2 First spatial data analysis

1.3 Next steps

Part I.

Data acquisition, data preparation and map creation

2. Data acquisition

2.1 Spatial data for a research question

2.2 AOI

2.3 Thematic raster map acquisition

2.4 Thematic vector map acquisition

2.5 Satellite sensor data acquisition

2.6 Summary and further reading

3. Data preparation

3.1 Deciding on a projection

3.2 Reprojecting raster and vector layers

3.3 Clipping to an AOI

3.4 Stacking raster layers

3.5 Visualizing a raster stack as RGB

3.6 Summary and further reading

4. Creating maps

4.1 Maps in QGIS

4.2 Maps for presentations

4.3 Maps with statistical information

4.4 Common mistakes and recommendations

4.5 Summary and further reading

Part II.

Spatial field data acquisition and auxiliary data

5. Field data planning and preparation

5.1 Field sampling strategies

5.2 From GIS to global positioning system (GPS)

5.3 On-screen digitization

5.4 Summary and further reading6.

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 15212.

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

Summary 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