Student learning outcomes for intro remote sensing course

As with many universities, UNR is seeking to make explicit each course’s student learning outcomes (SLOs).   I teach an advanced-undergrad/grad introductory remote sensing course here.   Students come from a variety of backgrounds but tend to be from geography, geological sciences and engineering, ecology, atmospheric sciences, and natural resources and environmental sciences.  A few have some calc under their belt, but most don’t.  I want all students to have an appreciation for the physical basis for remote sensing, its capabilities and limitations, a sense of the diversity of applications, and the relationship between these and a variety of sensors, platforms, and systems.  Mostly, I want them to be able to intelligently use remote sensing information and be prepared for more advanced classes (e.g. imaging spectroscopy/hyperspectral).

Anyway, here is a draft of my SLOs along with course objectives for “Remote Sensing: Principles and Applications”.

I welcome any feedback from remote sensing (or non-remote sensing) folks via e-mail or Twitter ( @AlbrightLCB).

Course Objectives:

  • Attain a foundational knowledge and comprehension of the physical, computational, and perceptual basis for remote sensing.
  • Gain familiarity with a variety of physical, biological, and human geographic applications of remote sensing.
  • Gain basic experience in the hands-on application of remote sensing data through visual interpretation and digital image processing exercises.
  • Analyze and synthesize understanding by identifying and developing a research and application proposal using remote sensing.

Student Learning Outcomes (SLOs):

  1. Students will be able to recognize and explain at a basic level fundamental physical principles of remote sensing, including the electromagnetic spectrum; the emission, scattering, reflection, and absorption of electromagnetic (EM) radiation; how EM radiation interactions vary across a limited number of substances, geometries, and temperatures; and geometric properties of photographs and imagery.
  2. Students will be able to recognize and explain basic computational properties of remote sensing data acquisition, storage, and processing.
  3. Students will be able to apply mathematical relationships (at a pre-calculus level) describing fundamental physical, geometric, and computational principles relevant to remote sensing.
  4. Students will be able to identify key applications of land, marine, aquatic, and atmospheric remote sensing and relate them to the properties of historical, current, and planned remote sensing instruments, approaches, and datasets.
  5. Students will demonstrate proficiency and conceptual understanding in using software or manual techniques to carry out remote sensing image processing and analysis through a series of laboratory exercises and reports.
  6. Students will describe a remote sensing application and assemble and summarize relevant literature in a written assignment.
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About LCB

This is the blog for the Laboratory for Conservation Biogeography at the University of Nevada, Reno.
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