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
Author(s): Martin Wegmann, Jakob Schwalb-Willmann, Stefan Werner Dech
Edition: 1
Publisher: Pelagic Publishing
Year: 2020
Language: English
Commentary: o7 thank you for all that you do libgen and co.
Pages: 222
Tags: GIS, QGIS
Half Title Page
Title Page
Copyright
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 reading
6. Field sampling using a global positioning system (GPS)
6.1 GPS in the field
6.2 GPX from GPS
6.3 Summary
7. From global positioning system (GPS) to geographic information system (GIS)
7.1 Joint coordinates and measurement sheet
7.2 Separate coordinates and measurement sheet
7.3 Point measurement to information
7.4 Summary
Part III. Data analysis and new spatial information
8. Vector data analysis
8.1 Percentage area covered
8.2 Spatial distances
8.3 Summary and further analyses
9. Raster analysis
9.1 Spectral landscape indices
9.2 Topographic indices
9.3 Spectral landscape categories
9.4 Summary and further analysis
10. Raster-vector intersection
10.1 Point statistics
10.2 Zonal statistics
10.3 Summary
Part IV. Spatial coding
11. Introduction to coding
11.1 Why use the command line and what is ‘R’?
11.2 Getting started
11.3 Your very first command
11.4 Classes of data
11.5 Data indexing (subsetting)
11.6 Importing and exporting data
11.7 Functions
11.8 Loops
11.9 Scripts
11.10 Expanding functionality
11.11 Bugs, problems and challenges
11.12 Notation
11.13 Summary and further reading
12. Getting started with spatial coding
12.1 Spatial data in R
12.2 Importing and exporting data
12.3 Modifying spatial data
12.4 Downloading spatial data from within R
12.5 Organization of spatial analysis scripts
12.6 Summary
13. Spatial analysis in R
13.1 Vegetation indices
13.2 Digital elevation model (DEM) derivatives
13.3 Classification
13.4 Raster-vector interaction
13.5 Calculating and saving aggregated values
13.6 Summary and further reading
14. Creating graphs in R
14.1 Aggregated environmental information
14.2 Non-aggregated environmental information
14.3 Finalizing and saving the plot
14.4 Summary and further reading
15. Creating maps in R
15.1 Vector data
15.2 Plotting study area data
15.3 Summary and further reading
Afterword and acknowledgements
References
Index
