satellite and shipboard remote sensing

DC-18 - Algorithms and processing
  • Differentiate supervised classification from unsupervised classification
  • Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
  • Compare pixel-based image classification methods with segmentation techniques
  • Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
  • Describe an application of hyperspectral image data
  • Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
  • Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
  • Describe a situation in which filtered data are more useful than the original unfiltered data
  • Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
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
DC-19 - Ground verification and accuracy assessment
  • Evaluate the thematic accuracy of a given soils map
  • Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset
DC-18 - Algorithms and processing
  • Differentiate supervised classification from unsupervised classification
  • Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
  • Compare pixel-based image classification methods with segmentation techniques
  • Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
  • Describe an application of hyperspectral image data
  • Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
  • Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
  • Describe a situation in which filtered data are more useful than the original unfiltered data
  • Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
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
DC-19 - Ground verification and accuracy assessment
  • Evaluate the thematic accuracy of a given soils map
  • Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset
DC-18 - Algorithms and processing
  • Differentiate supervised classification from unsupervised classification
  • Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
  • Compare pixel-based image classification methods with segmentation techniques
  • Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
  • Describe an application of hyperspectral image data
  • Produce pseudocode for common unsupervised classification algorithms, including chain method, ISODATA method, and clustering
  • Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
  • Describe a situation in which filtered data are more useful than the original unfiltered data
  • Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
DC-17 - Platforms and sensors
  • Compare and contrast common sensors by spatial resolution, spectral sensitivity, ground coverage, and temporal resolution (e.g., AVHRR, MODIS [intermediate resolution ~500 m, high temporal] Landsat, commercial high resolution [Ikonos and Quickbird]
  • Evaluate the advantages and disadvantages of acoustic remote sensing versus airborne or
  • satellite remote sensing for seafloor mapping
  • Select the most appropriate remotely sensed data source for a given analytical task, study area, budget, and availability
  • Differentiate between “push-broom” and “cross-track” scanning technologies
  • Evaluate the advantages and disadvantages of airborne remote sensing versus satellite remote sensing
  • Differentiate between “active” and “passive” sensors, citing examples of each
  • Explain the principle of multibeam bathymetric mapping
  • [Radarsat
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
DC-19 - Ground verification and accuracy assessment
  • Evaluate the thematic accuracy of a given soils map
  • Explain how U.S. Geological Survey scientists and contractors assess the accuracy of the National Land Cover Dataset

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