## 2016 QUARTER 03

##### 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