An Introduction to Spatial Data Analysis: Remote Sensing and GIS with Open Source Software(Data in the Wild)

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 is 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

Table of Contents:
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