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EEG 260 – GIS & Remote Sensing
David Harbor, Washington and Lee University
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All Course Notes
Introduction and Data Types
What is a GIS?
What does a GIS do?
Course Goals
GIS Components
3 types of data
Selecting data types
Topology of vector datasets
Maps: Projections and Datums
Where did you say you were calling from?
Projections
Geoid and reference ellipsoids
A datum
UTM
Spatial Overlays and Querying
Map Overlays and Boolean Logic
Overlay Analysis
Feature Overlay
simplification
complexity of combinations
reclassification
types of combinations
Overlay Querying
Digital Terrain Analyses
Digital Terrain Analysis
Digital Terrain Analysis 2
Digital Terrain Analysis 3
Digital Terrain Analysis 4
Digital Terrain Analysis 5
Digital Terrain Analysis 6
Digital Terrain Analysis 7
Modeling and Algorithms
Analysis Algorithms
Location-related calculations
buffers, distance, & proximity
rubber rulers
Friction and least-cost paths
Patch simplification and “clumping”
location and nearness…..
Density
Neighborhood Analyses
Filters
Creating surfaces by interpolation
Shape Analyses
Lines: length, azimuth, sinuosity
Distribution of points, lines, and polygons
Patch size, shape, connectivity
Fuzzy Logic: Fuzzy Sets, Conditional Inclusion and Bayes Theorem
A “fuzzy” boundary
Fuzzy Inclusion set using data
Bayesian Probability Modeling
Remote Sensing Data
The electromagnetic spectrum
Spectral signatures
Sensor Types
Landsat
LIDAR
Image Processing
Enhancement and Visualization
Illumination
Haze Correction
Ratios
decorrelation
Principal Component Analysis
Geolocating Images
Image Classification
General Principles
Simple Discriminants
Unsupervised Classification
Supervised Classification
using classification
GPS / GNSS (global positioning system / Global Navigation Satellite System)
The Satellite System
GPS receivers and signal corrections
Using GPS
Map Composition
Map Composition
Remote Sensing Data
The electromagnetic spectrum
Spectral signatures
Sensor Types
Landsat
LIDAR