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E-raamat: Introduction to Spatial Data Analysis: Remote Sensing and GIS with Open Source Software

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  • Formaat: 300 pages
  • Sari: Data in the Wild
  • Ilmumisaeg: 14-Sep-2020
  • Kirjastus: Pelagic Publishing
  • Keel: eng
  • ISBN-13: 9781784272159
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  • Hind: 42,89 €*
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Introduction to Spatial Data Analysis: Remote Sensing and GIS with Open Source SoftwareSuurem pilt
  • Formaat: 300 pages
  • Sari: Data in the Wild
  • Ilmumisaeg: 14-Sep-2020
  • Kirjastus: Pelagic Publishing
  • Keel: eng
  • ISBN-13: 9781784272159
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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
Preface viii
1 Introduction and overview
1(37)
1.1 Spatial data
1(8)
1.2 First spatial data analysis
9(26)
1.3 Next steps
35(3)
Part I Data acquisition, data preparation and map creation
2 Data acquisition
38(16)
2.1 Spatial data for a research question
38(2)
2.2 AOI
40(3)
2.3 Thematic raster map acquisition
43(1)
2.4 Thematic vector map acquisition
44(3)
2.5 Satellite sensor data acquisition
47(6)
2.6 Summary and further reading
53(1)
3 Data preparation
54(10)
3.1 Deciding on a projection
54(2)
3.2 Reprojecting raster and vector layers
56(3)
3.3 Clipping to an AOI
59(2)
3.4 Stacking raster layers
61(1)
3.5 Visualizing a raster stack as RGB
62(1)
3.6 Summary and further reading
63(1)
4 Creating maps
64(22)
4.1 Maps in QGIS
67(7)
4.2 Maps for presentations
74(6)
4.3 Maps with statistical information
80(3)
4.4 Common mistakes and recommendations
83(1)
4.5 Summary and further reading
83(3)
Part II Spatial field data acquisition and auxiliary data
5 Field data planning and preparation
86(11)
5.1 Field sampling strategies
87(4)
5.2 From GIS to global positioning system (GPS)
91(1)
5.3 On-screen digitization
92(4)
5.4 Summary and further reading
96(1)
6 Field sampling using a global positioning system (GPS)
97(6)
6.1 GPS in the field
98(3)
6.2 GPX from GPS
101(1)
6.3 Summary
102(1)
7 From global positioning system (GPS) to geographic information system (GIS)
103(7)
7.1 Joint coordinates and measurement sheet
104(1)
7.2 Separate coordinates and measurement sheet
105(1)
7.3 Point measurement to information
106(2)
7.4 Summary
108(2)
Part III Data analysis and new spatial information
8 Vector data analysis
110(12)
8.1 Percentage area covered
114(4)
8.2 Spatial distances
118(3)
8.3 Summary and further analyses
121(1)
9 Raster analysis
122(12)
9.1 Spectral landscape indices
122(6)
9.2 Topographic indices
128(1)
9.3 Spectral landscape categories
128(5)
9.4 Summary and further analysis
133(1)
10 Raster-vector intersection
134(6)
10.1 Point statistics
135(1)
10.2 Zonal statistics
136(2)
10.3 Summary
138(2)
Part IV Spatial coding
11 Introduction to coding
140(13)
11.1 Why use the command line and what is `R'?
140(2)
11.2 Getting started
142(1)
11.3 Your very first command
142(2)
11.4 Classes of data
144(1)
11.5 Data indexing (subsetting)
145(2)
11.6 Importing and exporting data
147(1)
11.7 Functions
148(1)
11.8 Loops
149(1)
11.9 Scripts
149(1)
11.10 Expanding functionality
150(1)
11.11 Bugs, problems and challenges
151(1)
11.12 Notation
152(1)
11.13 Summary and further reading
152(1)
12 Getting started with spatial coding
153(19)
12.1 Spatial data in R
153(5)
12.2 Importing and exporting data
158(4)
12.3 Modifying spatial data
162(4)
12.4 Downloading spatial data from within R
166(4)
12.5 Organization of spatial analysis scripts
170(1)
12.6 Summary
171(1)
13 Spatial analysis in R
172(13)
13.1 Vegetation indices
172(2)
13.2 Digital elevation model (DEM) derivatives
174(1)
13.3 Classification
175(4)
13.4 Raster-vector interaction
179(3)
13.5 Calculating and saving aggregated values
182(2)
13.6 Summary and further reading
184(1)
14 Creating graphs in R
185(11)
14.1 Aggregated environmental information
185(4)
14.2 Non-aggregated environmental information
189(5)
14.3 Finalizing and saving the plot
194(1)
14.4 Summary and further reading
195(1)
15 Creating maps in R
196(11)
15.1 Vector data
197(5)
15.2 Plotting study area data
202(4)
15.3 Summary and further reading
206(1)
Afterword and acknowledgements 207(2)
References 209(1)
Index 210
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. 
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