Integrated Geospatial Education &                   Technology Training
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    • Collaborators
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    • Participants >
      • COHORT 1
      • COHORT 2
      • COHORT 3
      • COHORT 4
  • Resources for Instruction
    • Start Here
    • Student Exercises >
      • Remote Sensing Intro with Apps and Web Browsers
      • Basic
      • Basic
      • Intermediate
      • Intermediate
      • Intermediate
    • Concept Module Videos >
      • Introduction to Remote Sensing Concepts for GIS Users >
        • Introduction to Remote Sensing Concepts for GIS Users
        • Remote Sensing and Spatial Thinking
        • Why are Pixels Square and Lenses Round
        • Remote Sensing for Ocean Assessments
      • RS Imagery-Imagery Format, Resolutions and Pixel Values >
        • Imagery Resolution and Landsat Basics
        • Photogrammetry and Aerial Imagery
        • Spectral Signatures
        • Infrared Radiation
      • Finding and Selecting Data >
        • Decision Flow Chart for Finding and Downloading Landsat Scenes
      • Visualization of Imagery Data >
        • Band Combinations
        • Map Design for Color Deficient Users
      • Data Preparation >
        • Spectral Resolution Part 1
        • Ground Truth Remote Sensing Imagery
        • Landsat 8 Inttro to Top of Atmosphere Radiance and Reflectance Part 1
        • Landsat 8 Intro to Top of the Atmosphere Radiance and Reflectance Part 2
        • Solar Radiance and Reflectance for Landsat 5 and 7 Part 1
        • Solar Radiance and Reflectance for Landsat 5 and 7 Part 2
      • Basic Analysis Techniques >
        • Introduction to Band Ratios Part 1
        • Introduction to Band Ratios Part 2
        • Object Recognition on Aerial Imagery (using forestry)
        • Image Analysis using NDV_to Assess Vegetation Greenness
      • Intermediate Analysis Techniques 1 >
        • Thermal Infrared Remote Sensing- Part 1 of 2
        • Spatial Filters in Remote Sensing -Part 1
        • Why are Pixels Square and Lenses Round
        • Spatial Filters in Remote Sensing Part 2
        • Spatial Filters in Remote Sensing Part 3
        • Maximum Likelihood Classification
        • Surpervised Classification using Paint by Number Analogy
        • Supervised vs. Unsupervised Cassification
  • Web Resources
    • iGETT Scoop it
    • Landsat On-Line Resources
    • Remote Sensing Background Information
    • Images and Movies
    • Software and Tutorials
  • Photo Gallery
    • Cohorts 1 and 2 >
      • Group Photos
      • Photos in the Field
      • Photos in the Lab
    • Cohorts 3 and 4 >
      • Group Photos
      • Photos in the Field
      • Photos in the Lab
  • Contact Us
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Collaborators who work together to manage and support the iGETT project:  

  • National Council for Geographic Education, Washington DC  " Grant Recipient"
  • U.S. Geological Survey Land Remote Sensing Program and Earth Resources Observation and Science Center (EROS), Sioux  Falls, SD
  • Sigma Space Corporation, Lanham, MD
  • NASA Goddard Space Flight Center, Greenbelt, MD
  • West Valley College, Saratoga, CA
  • National GeoTech Center at Jefferson Community and Technical College, Louisville, KY
  • Black Hills State University, South Dakota
    
   Other Contributors include: 


  • ESRI (Environmental Systems Research Institute) GIS refresher course; summer institute curriculum materials; software and lab licenses for summer institutes and participating colleges)
  • Harris Geospatial Solutions and Leica Geosystems (software and lab licenses for summer institutes and participating colleges)
  • American Society for Photogrammetry and Remote Sensing (ASPRS memberships for participants, support for development of curriculum materials)
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