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Resolution

Resolution. Landsat ETM+ image. Learning Objectives. What are the four types of resolution that we must consider with remotely sensed data? Be able to define each type of resolution. Be able to calculate the number of pixels in a given area.

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Resolution

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  1. Resolution

  2. Landsat ETM+ image

  3. Learning Objectives What are the four types of resolution that we must consider with remotely sensed data? Be able to define each type of resolution. Be able to calculate the number of pixels in a given area. Understand the trade-offs between different types of resolution. Understand the relationship between SNR and resolution.

  4. Learning Objectives (cont.) • Understand binary data and the relationship between radiometric resolution and storage space. • Understand the difference between different types of orbits.

  5. What are the four types of resolution? • Spatial • Spectral • Radiometric • Temporal

  6. Spatial Resolution • Usually reported as the length of one side of a single pixel • In analog imagery, the dimension (e.g. width) of the smallest object on the ground that can be distinguished in the imagery • Determined by sensor characteristics (for digital imagery), film characteristics (for air photos), field of view, and altitude.

  7. Group Problem If you have a study area that covers 1 km2, how many Landsat 30 m pixels does it take to cover it (nearest whole number)? How many 15 m panchromatic pixels would it take to cover the same area?

  8. Spatial Resolution

  9. IFOV 1 pixel

  10. Raster pixel size Higher resolution Lower resolution

  11. Available Spatial Resolution for Land RS • Satellites: ~ 0.3 m to1 km • Air photos ~ centimeters to meters

  12. Satellite data resolution • MODIS: 250 - 1000 m • Landsat MSS: 80 m • Landsat 5, 7, 8: 30 m (15 m panchromatic) • IRS MS: 22.5 m (5 m pan) • SPOT: 20 m • ASTER: 15m • WorldView 3: 1.24 m (0.3 m pan!)

  13. Quickbird (Digital Globe, Inc.) ~ 2.4 m spatial resolution in multispectral bands.

  14. MODIS 500 m spatial resolution

  15. Spatial Resolution Trade-offs • Data volume • Signal to Noise Ratio • “Salt and Pepper” • Cost

  16. Spectral Resolution Can be described two ways, but they usually go hand in hand. • How many spectral “bands” an instrument records • How “wide” each band is (the range of wavelengths covered by a single band)

  17. Spectral resolution • Related to the measured range of EMR • Wide range - coarser resolution • Narrow range - finer resolution

  18. Case 1 • Measure the EMR across a wide range • E.g., a single panchromatic band covering the entire visible portion of the spectrum • Assigns a single DN representing all visible light energy hitting the sensor • Analogous to black and white (panchromatic) film

  19. green blue red UV Near-infrared 0.4 0.5 0.6 0.7 Case 1

  20. From USGS Spectral Characteristics Viewer

  21. Case 2 Measure EMR across narrower ranges E.g., Separate bands for blue, green and red parts of the spectrum Assign a DN for each of these wavelength ranges to create 3 bands

  22. green blue red UV Near-infrared 0.4 0.5 0.6 0.7 Case 2

  23. From USGS Spectral Characteristics Viewer

  24. Red Green Blue Coarser (lower) Spectral Resolution RGB Finer (higher) Spectral Resolution

  25. High Spectral Resolution Spectral reflectance curve for green leaf using 224 bands (high spectral resolution) Reflectance Wavelength (nm) Low Spectral Resolution Spectral reflectance curve for green leaf using 6 bands (lower spectral resolution) Reflectance Wavelength (nm)

  26. Could you distinguish Dolomite from Calcite using Landsat 8 spectral data?

  27. Spectral Resolution Trade-Offs • Data Volume and processing 1 DN for each pixel in EACH BAND • Signal to Noise Ratio • Cost

  28. Group Problem For your 1 km2 study area, if you use 7 Landsat 8 bands, how many DNs will your computer have to store?

  29. Radiometric Resolution • How finely does the satellite divide up the radiance it receives in each band? How much light does it take to change the DN from one number to the next? • Usually expressed as number of bits used to store the maximum possible DN value • 8 bits = 28 = 256 levels (usually 0 to 255) • 16 bits = 216 = 65,536 levels (0 to 65,535)

  30. 26 = 64 levels (6 bit) 22 = 4 levels (2 bit)

  31. Radiometric resolution • 1 bit ( 0 - 1) • 8 bit ( 0 - 255 ) (older Landsats, many others) • 16 bit ( 0 - 65,535 ) (Landsat 8) • 32 bit ( 0 - 4,294,967,295 ) (uncommon) For an 8-bit satellite: DN = 0: No EMR or below some minimum amount of light (threshold) DN = 255: Max EMR or above some maximum amount of light

  32. Radiometric resolution • 8 bit data (e.g., Landsat 5) (256 values) • Everything will be scaled from 0 – 255 • Subtle details may not be represented • 16 bit data (e.g., Landsat 8) (65,536 values) • Wide range of choices • Required storage space will be twice that of 8 bit

  33. Radiometric Radiation Trade Offs • Data volume Every 8 bits takes 1 byte to store on a computer. One 8-bit DN takes 1 byte One 16-bit DN takes 2 bytes Etc.

  34. Group Problem If your are using 7-band, 16-bit Landsat 8 data for your 1 km2 area, how many bytes are needed to store your DNs on your computer?

  35. Calculating Image Size Computer hard drives store data in “boxes” called bytes (e.g., 1 Mb = 1 million bytes) 1 byte can hold 8 binary (base 2) digits (0s or 1s or some combination of 0s and 1s) Each “bit” is a single binary digit An 8-bit number is made of of 8 binary digits and fits into 1 byte. A 9-bit number won’t fit in 1 byte and requires 2 bytes.

  36. Converting Base 10 to Binary

  37. Temporal resolution • Time between two subsequent data acquisitions for an area All of the Landsat satellites have a 16-day return time MODIS has a 1-2 day return time.

  38. Return Time (Temporal Resolution) • Depends on: • Orbital characteristics • Swath width • Ability to point the sensor

  39. Orbital Characteristics • Geosynchronous • Polar • Sun synchronous

  40. Geosynchronous Orbits • Satellite orbits the earth at a rate that allows it to match the earth’s rotation—so the satellite is always over the same place • Narrow range of altitudes—about 35,786 km above the equator. • Useful for communications, weather etc. • Example: GOES satellite (weather) • Geosynchronous orbiting earth satellite

  41. Polar/Sun Synchronous Orbits • Pass roughly over the north and south poles • Fly over the same place on earth at the same time of day • Examples: Landsat, AVHRR • Good for land remote sensing • Return time related to spatial resolution, latitude, swath width, and orbital altitude

  42. Return Time Trade Offs • Spatial resolution • Viewing geometry effects (off nadir) • Clouds and other atmospheric problems • Lack of archival repeat coverage for pointable satellites

  43. In summary, choosing a satellite is often an exercise in weighing the relative trade-offs of resolution against data needs (and budgets!).

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