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Fundamentals of Remote Sensing

Fundamentals of Remote Sensing. The Interaction of Electromagnetic Radiation with Matter. Electromagnetic Radiation (EMR). “Light” Can originate from the sun (passive remote sensing) or from a man-made instrument (active remote sensing) Can behave like a particle or a wave

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Fundamentals of Remote Sensing

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  1. Fundamentals of Remote Sensing The Interaction of Electromagnetic Radiation with Matter

  2. Electromagnetic Radiation (EMR) • “Light” • Can originate from the sun (passive remote sensing) or from a man-made instrument (active remote sensing) • Can behave like a particle or a wave • Is a form of energy • Interacts with materials in unique ways

  3. Interesting fact: • Light reaching the earth now was generated in the core of the sun ~100,000 years ago. It takes that long for the photons to work their way out to the sun’s surface and zoom off to earth at c = 298,000,000 m/s (takes about 8.5 minutes).

  4. Remote Sensing Uses EMR!So…you need to understand: • Basic physics of EMR • Units used to describe EMR • Divisions of EMR spectrum • Behavior of EMR interacting with the atmosphere and other materials

  5. All objects warmer than absolute zero emit EMR • Most objects reflect EMR emitted by other objects • Key basis of remote sensing because objects of interest interact in unique ways with EMR

  6. How do we describe EMR? • Wavelength • Frequency • Energy λ (lamda) ν (nu)

  7. Structure of electromagnetic radiation

  8. Wavelength

  9. Wavelength Units (Length!) • Meters (m) • Centimeters (cm) • Millimeters (mm) • Micrometers (µm) • Nanometers (nm) • Angstroms (Ǻ)

  10. Frequency • The number of waves that pass through an imaginary plane in a specific amount of time (e.g., 1 second)

  11. Frequency Units (A Rate!) • Hertz (Hz) (1 Hz = 1 cycle/sec) • Kilohertz (KHz) • Megahertz (MHz) • Gigahertz (GHz) • Etc.

  12. c = wavelength x frequency (λν) c = 3 x 108 m/sec (the speed of light) = 186,000 miles/sec Velocity of Light (c)

  13. Energy (Q) Q = h * ν Q = Energy of a quantum (joules) h = Planck’s constant (6.626 * 10-34 J s/cycle) ν= Frequency So… Energy is proportional to frequency Energy is inversely proportional to wavelength

  14. Frequency-Wavelength-Energy Web Demo – Electromagnetic Waves

  15. The EMR Spectrum

  16. Electromagnetic Spectrum

  17. Visible Light • Wavelengths that dominate radiation given off by the sun • Most animals evolved to “see” these wavelengths • Captured by your digital camera

  18. Additive Primaries (Color Theory) (Add together in different proportions to make all other colors) Red + Blue = Magenta Red + Green = Yellow Blue + Green = Cyan Red + Blue + Green = White

  19. Why is Color Theory Important? • Your computer screen uses the 3 additive primaries to display all possible colors • To interpret remotely sensed imagery you must be able to interpret color

  20. Infrared Portion of the Spectrum

  21. Infrared Radiation Near Infrared (NIR) 720 – 1300 nm Mid Infrared (MIR) 1300 – 3000 nm Far Infrared (FIR, aka thermal) > 3000 nm Thermal (far) IR images of Pacific Ocean

  22. Other parts of the Spectrum UV Radar

  23. Atmospheric Effects • Absorption • Scattering • The atmosphere affects sunlight coming to the ground and reflected light going back to the satellite.

  24. Absorption Ozone Hole Thermal IR – Greenhouse Effect

  25. Clouds! • Most EMR wavelengths can’t penetrate clouds • Big problem in remotely sensed imagery—tropics especially • Temporal compositing to get rid of clouds • Cloud shadows a problem too

  26. Riverton Landsat Image July 15 1999 Cloudy!

  27. Reflectance

  28. Reflectance • Definition: The amount of reflected radiation divided by that amount of incoming radiation in a particular wavelength % Reflectance = 100*reflected/irradiance

  29. Reflectance Spectra • Understanding reflectance spectra is fundamental to using remote sensing • Reflectance spectra are graphs of reflectance vs. wavelength.

  30. Vegetation Reflectance

  31. Summary • All of this is important because it determines in part how objects of interest interact with EMR. The better we understand these interactions, the better we are at using the remote sensing tool!

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