1. Resolution

2. Landsat ETM+ image

4. 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.

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

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

7. 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.

8. 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?

9. Spatial Resolution

10. IFOV
1 pixel

11. Raster pixel size
Higher resolution
Lower resolution

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

13. Satellite data resolution
MODIS: m
Landsat MSS: m
Landsat 5, 7, 8: m (15 m panchromatic)
IRS MS: m (5 m pan)
SPOT: m
ASTER: m
WorldView 3: m (0.3 m pan!)

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

15. MODIS
500 m spatial resolution

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

17. 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)

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

19. 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

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

21. From USGS Spectral Characteristics Viewer

22. 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

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

24. From USGS Spectral Characteristics Viewer

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

26. 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)

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

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

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

30. 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)

31. 26 = 64 levels (6 bit)
22 = 4 levels (2 bit)

32. Radiometric resolution
1 bit ( 0 - 1)
8 bit ( ) (older Landsats, many others)
16 bit ( ,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

33. 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

34. 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.

35. 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?

36. 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.

37. Converting Base 10 to Binary
Base 2 (Binary) 1 2 10 3 11 4
100 5
101 6
110 7
111 8
1000
255
256
257
(etc.)

38. 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.

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

40. Orbital Characteristics
Geosynchronous
Polar
Sun synchronous

41. 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

42. 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

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

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