An Open Geomatics Textbook
Preface
0.1
Contacts
0.2
Project Wiki
0.3
Style Guide
0.3.1
Audience
0.3.2
General Style
0.3.3
Learning Objectives
0.3.4
Summary
0.3.5
Key Terms
0.3.6
Headings and Labels
0.3.7
Formulae
0.3.8
Units
0.3.9
Numbers
0.3.10
Dates and times
0.3.11
Tables
0.3.12
Code blocks
0.3.13
Abbreviations
0.3.14
Initialisms
0.3.15
Acronyms
0.3.16
Punctuation
0.3.17
Citations
1
What is Geomatics?
2
Mapping Data
Key Terms
2.1
Introduction to geodesy
2.2
Models of Earth
2.2.1
Geodetic vertical datums
2.2.2
Tidal Vertical Datums
2.2.3
Gravimetric Vertical Datums
2.3
Referencing Location
2.3.1
Cartesian Coordinate Systems
2.3.2
Celestial Coordinate Systems
2.3.3
Geographic Coordinate Systems
2.3.4
Projected Coordinate Systems
2.3.5
Measuring map projection distortion
2.3.6
Map projections for environmental management
2.4
Summary
Reflection Questions
Practice Questions
Recommended Readings
3
Data Types and Spatial Data Models
Key Terms
3.1
Types of Phenomena
3.1.1
Discrete Objects
3.1.2
Continuous Fields
3.1.3
Imperfect Distinctions
3.2
Types of Data
3.2.1
Qualitative Data
3.2.2
Quantitative Data
3.2.3
Summary of Data Types
3.3
Spatial is Special
3.4
Spatial Data Models
3.4.1
Raster Data Model
3.4.2
Vector Data
3.5
Choice of Spatial Data Model
3.5.1
Comparing Data Models
3.5.2
Advantages and Disadvantages
3.6
Summary
Reflection Questions
Practice Questions
Recommended Readings
4
Collecting and Editing Data
Learning Objectives
Key Terms
4.1
Open data
4.2
Finding Data
4.3
Data in Academia
4.4
Government Data
Your Turn!
4.5
Census Data
4.6
Census of Canada Geographic Levels
Call Out
4.7
Accessing Census Data
4.8
Non-Governmental Organization Data
4.9
Citizen Science
Your Turn!
4.10
International Data
4.11
Unpublished Data and the Data Request
4.12
Metadata
4.13
Historical Data Collections
4.14
Historical Aerial Photographs
4.15
Accessing Historical Aerial Photograph Collections
Your Turn!
4.16
Natural Resource Administrative Data
4.17
Historical Maps
4.18
Georeferencing Historical Maps
4.19
Control Points on Maps with Grids or Graticule.
4.20
Grid and Graticule as Control Points
4.21
Rubbersheeting
4.22
Documenting Georeferencing
4.23
Data Transformations
4.24
Affine
4.25
Similarity
4.26
Projective
4.27
Reflection Questions
4.28
Practice Questions
4.29
Summary
4.30
References
5
Relational Databases
Key Terms
5.1
Relational database management systems
5.2
Relational databases
5.3
Relational algebra
5.3.1
Selection
5.3.2
Projection
5.3.3
Rename
5.3.4
Set Union
5.3.5
Set Intersection
5.3.6
Set Difference
5.3.7
Cartesian Product
5.3.8
Set Divison
5.4
Boolean algebra
5.4.1
Equality operators
5.4.2
Conditional operators
5.5
Joining relations
5.5.1
Keys
5.5.2
Natural Join
5.6
Outer Join
5.6.1
Right and Left Outer Join
5.6.2
Theta Join
5.6.3
Cardinality of Joins
5.6.4
Structured Query Language
5.7
Summary
Reflection Questions
Practice Questions
Recommended Readings
6
Overlay and Proximity Analysis
Learning Objectives
Key Terms
6.1
Cartographic Modelling
6.2
Geoprocessing
6.3
Capability Modelling
6.4
Suitability modelling
6.5
Overlay Methods
6.6
Attribute Transfer
6.7
Boolean Algebra
6.8
Spatial Join
6.9
Clip
6.10
Intersect
6.11
Line Intersection
6.12
Union
6.13
Identity
6.14
Erase
6.15
Split
6.16
Symmetrical Difference
6.17
Update
6.18
Proximity Methods
6.19
Buffer
6.20
Near Distance
6.21
Summary
Reflection Questions
Practice Questions
Recommended Readings
7
Topology and Geocoding
Learning Objectives
Key Terms
7.1
Topology
7.2
Planar vs. Non-Planar Topology
7.3
Implementing Planar Topology
7.4
Adjacency and Overlap
7.5
Intersect and Connect
7.6
Coincident and Disjoint
7.7
Cover
7.7.1
Multipart geometries
7.8
3D topologies
7.8.1
Multipatch geometries
7.8.2
Convex hull
7.8.3
Delauney triangles
7.8.4
Data models supporting planar topology
7.9
Geocoding
7.10
Geocoding Services
7.11
Case Study: Working with Canadian Census Data
7.12
Summary
Reflection Questions
Practice Questions
Recommended Readings
8
Network Analysis
Key Terms
8.1
Introduction to graph theory
8.1.1
Nodes
8.1.2
Edges
8.2
Connectivity and order
8.2.1
Direct
8.2.2
Undirect
8.3
Network topologies
8.3.1
Physical versus logical topology
8.3.2
Lines
8.3.3
Rings
8.3.4
Meshes
8.3.5
Stars
8.3.6
Trees
8.3.7
Buses
8.3.8
Fully connected
8.4
Spatial Network Analysis
8.4.1
Network tracing
8.4.2
Linear referencing
8.4.3
Routing
8.4.4
Least cost paths
8.4.5
Least cost corridors
8.5
Network Centrality
8.5.1
Degree centrality
8.5.2
Closeness centrality
8.5.3
Betweenness centrality
9
Raster Analysis and Terrain Modelling
Learning Objectives
Key Terms
9.1
Raster Analysis
9.2
Digital Vertical Models
9.3
Digital Elevation Models (DEM)
9.4
Digital Terrain Models (DTM)
9.5
Digital Surface Models (DSM)
9.6
Contours
9.7
Raster Functions
9.8
Local
9.9
Focal
9.10
Global
9.11
Zonal
9.12
Derivatives of Elevation Models
9.13
Slope
9.14
Aspect
9.15
Heat Load Index
9.16
Hillshade
9.17
Sinks, Peaks, and Saddles Oh My!
9.18
Landform Classification
9.19
Profile and Planform Curvature
9.20
Topographic Position Index
9.21
Hydrology Work “flows”
9.22
Flow Direction and Flow Accumulation
9.23
Stream Delineation
9.24
Flow Length
9.25
Topographic Wetness Index
9.26
Case Study: Topographic Indices for Wetland Mapping
9.27
DEM Derivatives and Classification
9.28
3D Geovisualization
9.29
Anaglyphs
9.30
Summary
Reflection Questions
Practice Questions
Recommended Readings
10
Spatial Estimation
Learning Objectives
Key Terms
10.1
Introduction
Recall This
10.2
Geostatistics
10.3
Spatial Autocorrelation
10.4
Sampling
10.5
Prediction
10.6
Estimation
10.7
Classical vs. Geostatistical Inferences
10.8
Sampling
Recall This
10.9
Population
10.10
Sampling Design
10.11
Sampling Unit
10.12
Probability Sampling
10.13
Simple Random Sampling
10.14
Stratified Random Sampling
10.15
Systematic Sampling
10.16
Cluster Sampling
10.17
Non-probability Sampling
10.18
Representative Sampling
10.19
Unique Case Sampling
10.20
Sequential Sampling
10.21
Spatial Autocorrelation
Recall This
10.22
Domain
10.23
Attributes
10.24
Moran’s I
10.25
Case Study: Title of Case Study Here
Calculating Moran’s I
Using Contiguity
10.26
Geary’s C
10.27
Semivariogram Modeling
10.28
Case Study: Title of Case Study Here
10.28.1
Gaussian
10.29
Spherical
10.30
Exponential
10.31
Circular
10.32
Spatial Interpolation
10.33
Case Study: Title of Case Study Here
10.34
Methods Without Using Semi-variogram
10.35
Nearest Neighbor
10.36
Thiessian Polygon
10.37
Methods Using Semi-variogram
10.38
Kriging
10.38.1
Linear Kriging
10.39
Case Study: Title of Case Study here
10.40
Simple Kriging
10.41
Ordinary Kriging
10.42
Universal Kriging
Which method is the best given our data?
10.43
Co-Kriging
10.44
Non-Linear Kriging
10.45
Indicator Kriging
10.46
Probability Kriging
10.47
Disjunctive Kriging
10.48
Spatial Regression Models
10.49
Case Study: Title of case study here
10.50
Spatial Lag Model
10.51
Steps in Fitting Spatial Lag Model:
10.52
Spatial Error Model
10.53
Steps in Fitting Spatial Error Model:
10.54
Selection Between Lag and Error Model
Remember This?
Reflection Questions
11
Fundamentals of remote sensing
Key Terms
11.1
What is remote sensing?
11.2
Types of Energy
11.2.1
Introduction
11.2.2
Scientific Notation
11.2.3
Electromagnetic Spectrum
11.2.4
Radiation Types
11.3
Physical laws of radiation
11.4
The Four Resolutions
11.4.1
Spatial Resolution
11.4.2
Temporal Resolution
11.4.3
Spectral Resolution
11.4.4
Radiometric Resolution
11.5
Key Applications
11.6
Summary
11.6.1
Reflection Questions
12
Remote Sensing Systems
Key Terms
12.1
Optical System Basics
12.1.1
Lenses
12.1.2
Focal Length
12.1.3
Principle Points
12.1.4
Field of View
Did You Know?
12.2
Perspectives
12.3
Aerial Perspective
12.4
Nadir Perpsective
12.5
Oblique Perspective
12.6
Hemispherical Perspective
12.7
Sensors
12.8
Detectors
12.9
Analog-to-Digital Converters
12.10
Panchromatic Sensors
12.11
Multispectral Sensors
12.12
Hyperspectral Sensors
12.13
Platforms
12.14
Terrestrial Systems
12.15
Aerial systems
12.16
Satellite Systems
12.17
Orbital Physics
12.18
Low Earth Orbit
12.19
Medium Earth Orbit
12.20
Polar and Sun-synchronous Orbit
12.21
Geostationary Orbit
12.22
Lagrange Points
12.23
Important Satellite Systems for Environmental Management
12.24
LANDSAT
12.25
RADARSAt
12.26
Advanced Very High Resolution Radiometer (AVHRR)
12.27
Moderate Resolution Spectroradiometer (MODIS)
12.28
ICESat
12.29
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)
12.30
Defense Meteorology Satellite Program (DMSP)
12.31
Visible Infrared Imaging Radiometer Suite (VIIRS)
12.32
WildfireSat
12.33
Case Study: Title of Case Study Here
Case Study Title Max of Forty Characters
12.34
Summary
Reflection Questions
Practice Questions
Recommended Readings
13
Image Processing
Key Terms
13.1
Overview
13.2
Geometric correction
13.2.1
Relief displacement
13.2.2
Georeferencing
13.2.3
Georegistration (georectification)
13.2.4
Resampling
13.3
Atmospheric correction
13.3.1
Atmospheric windows
13.3.2
Clouds and shadows
13.3.3
Smoke and Haze
13.4
Radiometric correction
13.4.1
Signal-to-noise
13.4.2
Radiometric normalization
13.5
Image enhancement
13.5.1
Stretching
13.5.2
Sharpening
13.6
Summary
Reflection Questions
14
Image Analysis
15
LiDAR Acquisition and Analysis
Key Terms
15.1
What is LiDAR?
15.1.1
How does it Work?
15.2
Components of LiDAR Systems
15.2.1
Lasers
15.2.2
Position and Orientation
15.2.3
Platform
15.3
Types of LiDAR
15.3.1
Discrete Return
15.3.2
Full Waveform
15.3.3
Multispectral
15.3.4
Single Photon Lidar
15.4
LiDAR Derivatives and Analysis
15.4.1
Bare Earth Elevation
15.4.2
Canopy Height Model
15.4.3
Canopy Surface Model
15.4.4
Grid Metrics
15.4.5
Tree Segmentation
15.4.6
Software
15.5
Summary
Reflection Questions
Practice Questions
16
Chapter Title
Key Terms
16.1
The GIS Research Process
16.2
Problems with data integration
16.3
Frame the Problem
16.3.1
Identify and Acquire Data
16.4
Data Resolution
16.5
Integrating vector and raster data
16.5.1
Rasterization
16.5.2
Vectorization
16.5.3
Zonal Statistics
16.5.4
Smoothing
16.5.5
Simplifying
16.6
Spatial data errors
16.6.1
Accuracy vs. Precision
16.6.2
Vagueness and Ambiguity
16.6.3
Quantifying spatial errors RMSE, Euclid’s distance
16.6.4
Logical Errors
16.6.5
Ecological Fallacy, Atomistic Fallacy, MAUP etc. Its important to include these, whether here or elsewhere?
16.6.6
Other Errors?
16.7
Making Beautiful Maps
Learning Objectives
Key Terms
16.8
Types of Maps
16.9
Thematic Maps
16.10
Choropleth Maps
16.11
Dot Density Maps
16.12
Isoline Maps
16.13
Diagrammatic Maps
16.14
Cartograms
16.15
Additional Resources on Types of Maps
16.16
Map Composition
16.17
Figure
16.18
Ground
16.19
Frame
16.20
Elements of Maps
16.21
Text
16.22
Legend
16.23
Scale and North Arrow
16.24
Measured Grid
16.25
Citation
16.26
Symbolization
16.27
Separable
16.28
Integral
16.29
Graduated
16.30
Configurable
16.31
Proportional
16.32
Line Weight
16.33
Additional Resources
16.34
Colour
16.35
Hue
16.36
Chroma
16.37
Lightness
16.38
Bivariate Colour Schemes
16.39
Colour Pickers
16.40
Additional Resources
16.41
Classification Schemes
16.42
Qualitative
16.43
Sequential
16.44
Intervals
16.45
Quantiles
16.46
Natural Breaks (Jenna)
16.47
Standard Deviation
16.48
Additional Resources
16.49
Generalization
16.50
Select
16.51
Amalgamate
16.52
Exaggerate
16.53
Displace
16.54
Refine
16.55
Simplify
16.56
Aggregate
16.57
Typify
16.58
Smooth
16.59
Enhance
16.60
Collapse
16.61
Merge
16.62
Additional Resources
16.63
Map Design
16.64
Subject
16.65
Projection and Orientation
16.66
Hierarchy
16.67
Balance
16.68
Summary
Reflection Questions
Practice Questions
Recommended Readings
Published with bookdown
An Open Geomatics Textbook
Chapter 14
Image Analysis