2016 QUARTER 03

A B C D E F G H I K L M N O P R S T U V W
DC-16 - Nature of multispectral image data
  • Explain the concepts of spatial resolution, radiometric resolution, and spectral sensitivity
  • Draw and explain a diagram that depicts the bands in the electromagnetic spectrum at which Earth’s atmosphere is sufficiently transparent to allow high-altitude remote sensing 
  • Illustrate the spectral response curves for basic environmental features (e.g., vegetation, concrete, bare soil)
  • Describe an application that requires integration of remotely sensed data with GIS and/or GPS data
  • Explain the concept of “data fusion” in relation to remote sensing applications in GIS&T
  • Draw and explain a diagram that depicts the key bands of the electromagnetic spectrum in relation to the magnitude of electromagnetic energy emitted and/or reflected by the Sun and Earth across the spectrum
AM-05 - Neighborhoods
  • Discuss the role of Voronoi polygons as the dual graph of the Delaunay triangulation
  • Explain how Voronoi polygons can be used to define neighborhoods around a set of points
  • Outline methods that can be used to establish non-overlapping neighborhoods of similarity in raster datasets
  • Create proximity polygons (Thiessen/Voronoi polygons) in point datasets
  • Write algorithms to calculate neighborhood statistics (minimum, maximum, focal flow) using a moving window in raster datasets
  • Explain how the range of map algebra operations (local, focal, zonal, and global) relate to the concept of neighborhoods
FC-19 - Networks defined
  • Define different interpretations of “cost” in various routing applications
  • Describe networks that apply to specific applications or industries
  • Create a data set with network attributes and topology
  • Define the following terms pertaining to a network: Loops, multiple edges, the degree of a vertex, walk, trail, path, cycle, fundamental cycle
AM-63 - Non-linearity relationships and non-Gaussian distributions
  • Understand how some machine learning methods might be more adept at modeling or representing such distributions
  • Define non-linear and non-Gaussian distributions in a geospatial data environment
  • Exemplify non-linear and non-Gaussian distributions in a geospatial data environment