Course Notes

GIS and Remote Sensing Lecture Notes

  1. Introduction and Data Types
    1. what is a GIS?
    2. what a GIS does
    3. course goals
    4. GIS components
    5. 3 types of data
    6. selecting data types (raster vs vector)
    7. topology
  2. Maps: Projections and Datums
    1. Where did you say you were calling from?
    2. Projections create distortion
    3. Spheroids
    4. A datum
    5. UTM
  3. Spatial Overlays and Querying
    1. overlay analysis (overview)
    2. map algebra
    3. feature overlay
    4. simplification
    5. complexity of combinations
    6. reclassification
    7. types of combinations
    8. Overlay querying (hind-casting or inverse
      modeling)
  4. Digital Terrain Analyses
    1. Data sources
    2. DEM, TIN, DLG, DTM , point cloud
    3. projecting grids and imagery – resampling
    4. manipulating and moving between DTMs
    5. map slope, aspect, & curvature
    6. profiles and viewsheds
    7. perspectives and hillshade 
    8. Watershed Analyses
    9. Typical QGIS hydrology workflow
  5. Modeling and Algorithms
    1. modeling vs. analysis
    2. spatial modeling principles
    3. thinking through an analysis using an algorithm
    4. types of models
    5. multicriterion models
    6. testing models
    7. vocab
  6. Distance-related calculations & Neighborhood Analyses
    1. Buffers
    2. “Rubber rulers” (dynamically-scaled buffers)
    3. Friction/Least Cost Paths
    4. Proximity and “near”ness (7.2)
    5. Density (7.3)
    6. Patch simplification and “clumping” (6.3)
    7. Filters (7.3-8.1)
    8. Creating surfaces by interpolation (8.1,2)
    9. distance & neighborhood vocabulary
  7. Shape Analyses
    1. Lines; length, azimuth, sinuosity (8.2)
    2. Distribution of points, lines, and polygons (8.2)
    3. Patch size, shape, connectivity (8.3)
  8. Fuzzy Logic: Fuzzy Sets, Conditional Inclusion and Bayes Theorem
    1. A “fuzzy” boundary (9.1)
    2. Fuzzy Inclusion set using data (9.1)
    3. Bayesian Probability Modeling (9.1)
  9. Remote Sensing Data
    1. The electromagnetic spectrum
    2. Spectral signatures
    3. Sensor Types
    4. Landsat
    5. LIDAR
  10. Image Processing
    1. Enhancement and Visualization
    2. What passive sensors mostly “see”
    3. Atmospheric Correction
    4. Ratios
    5. decorrelation
    6. Principal Component Analysis
    7. geolocating images
    8. other enhancements
  11. Image Classification
    1. general principles
    2. simple discriminants
    3. unsupervised classification
    4. supervised classification
    5. transferring a supervised classification
    6. using classification
  12. GPS / GNSS (global positioning system / Global Navigation Satellite System)
    1. The Satellite System
    2. Receivers and measurement types
    3. Using GPS
  13. Map Composition
    1. map composition