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Remote Sensing and Image Processing: 3

Dr. Mathias (Mat) DisneyUCL Geography

Office: 301, 3rd Floor, Chandler HouseTel: 7670 4290

Email: mdisney@geog.ucl.ac.ukwww.geog.ucl.ac.uk/~mdisney

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Back to the process....

• What sort of parameters are of interest?

• Variables describing Earth system....

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EO and the Earth

“System”

From Ruddiman, W. F., 2001. Earth's Climate: past and future.

External forcing

Hydrosphere

Atmosphere

Geosphere

Cryosphere

Biosphere

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Example biophysical variables

After Jensen, p. 9

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Example biophysical variables

Good discussion of spectral information extraction:

http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf

After Jensen, p. 9

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Remote Sensing Examples

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Information extraction processImage

interpretation

•Tone, colour, stereo parallax

•Size, shape, texture, pattern, fractal dimension

•Height/shadow

•Site, association

Primary elements

Spatial arrangements

Secondary elements

Context

Analogue image

processing

•Multi:•spectral, spatial, temporal, angular, scale, disciplinary

•Visualisation

•Ancillary info.: field and lab measurements, literature etc.

Presentation of information

•Multi:•spectral, spatial, temporal, angular, scale, disciplinary

•Statistical/rule-based patterns

•Hyperspectral

•Modelling and simulation

After Jensen, p. 22

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Example: Vegetation canopy modelling•Develop detailed 3D models

•Simulate canopy scattering behaviour

•Compare with observations

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Electromagnetic (EM) Spectrum

• Core principles of electromagnetic radiation (EMR)– solar radiation– blackbody concept and radiation laws

• EMR and remote sensing– wave and particle models of radiation– regions of EM spectrum– interaction with atmosphere– interaction with surface

• Measurement of radiation

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EM spectrum: so what?

• This is what we measure in remote sensing• Terms, units, definitions• Provide basis for understanding type of

infomration that can be (usefully) retrieved • Why we choose given regions of the EM

spectrum in which to make measurements

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Remote sensing process: recap

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Remote sensing process: recap• Note various paths

– Source to sensor direct?– Source to surface to sensor– Sensor can also be source

• RADAR, LiDAR, SONAR • i.e. “active” remote sensing

• Reflected and emitted components– What do these mean?

• Several components of final signal captured at sensor

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Energy transport• Conduction

– transfer of molecular kinetic (motion) energy due to contact– heat energy moves from T1 to T2 where T1 > T2

• Convection– movement of hot material from one place to another– e.g. Hot air rises

• Radiation– results whenever an electrical charge is accelerated– propagates via EM waves, through vacuum & over long distances

hence of interest for remote sensing

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EM Spectrum

•EM Spectrum•Continuous range of EM radiation

•From very short wavelengths (<300x10-9m)

•high energy

•To very long wavelengths (cm, m, km)

•low energy

•Energy is related to wavelength (and hence frequency)

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• Energy radiated from sun (or active sensor)• Energy ∝ 1/wavelength (1/λ)

– shorter λ (higher f) == higher energy– longer λ (lower f) == lower energyfrom http://rst.gsfc.nasa.gov/Intro/Part2_4.html

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Units•EM wavelength λ is m, but various prefixes

•cm (10-2m)

•mm (10-3m)

•micron or micrometer, µm (10-6m)

•Angstrom, Å (10-8m, used by astronomers mainly)

•nanometer, nm (10-9)

•f is waves/second or Hertz (Hz)

•NB can also use wavenumber, k = 1/λ i.e. m-1

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EM Spectrum

•We will see how energy is related to frequency, f (and hence inversely proportional to wavelength, λ)

•When radiation passes from one medium to another, speed of light (c) and λ change, hence f stays the same

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Electromagnetic spectrum: visible

• Visible part - very small part– from visible blue (shorter

λ)– to visible red (longer λ)– ~0.4 to ~0.7µm

Violet: 0.4 - 0.446 µmBlue: 0.446 - 0.500 µmGreen: 0.500 - 0.578 µmYellow: 0.578 - 0.592 µmOrange: 0.592 - 0.620 µmRed: 0.620 - 0.7 µm

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Electromagnetic spectrum: IR• Longer wavelengths (sub-

mm)• Lower energy than visible• Arbitrary cutoff• IR regions covers

– reflective (shortwave IR, SWIR)

– and emissive (longwave or thermal IR, TIR)

– region just longer than visible known as near-IR, NIR.

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Electromagnetic spectrum: microwave• Longer wavelength again

– RADAR– mm to cm

– various bands used by RADAR instruments

– long λ so low energy, hence need to use own energy source (active µwave)

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Electromagnetic spectrum

• Interaction with the atmosphere– transmission NOT even across the spectrum– need to choose bands carefully to coincide with regions where

transmission high (atmospheric windows – see later)

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“Blackbody” concept•All objects above absolute zero (0 K or -273° C) radiate EM energy (due to vibration of atoms)

•We can use concept of a perfect blackbody•Absorbs and re-radiates all radiation incident upon it at maximum possible rate per unit area (Wm-2), at each wavelength, λ, for a given temperature T (in K)

•No real object is blackbody but it is v. useful assumption

•Energy from a blackbody?

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Stefan-Boltzmann Law•Total emitted radiation from a blackbody, Mλ, in Wm-2, described by Stefan-Boltzmann Law

4TM σλ =

•Where T is temperature of the object in K; and σ = is Stefan-Boltmann constant = 5.6697x10-8 Wm-2K-4

•So energy ∝ T4 and as T⇑ so does M

•Tsun ≈ 6000K Mλ,sun ≈ 73.5 MWm-2

•TEarth ≈ 300K M λ, Earth ≈ 460 Wm-2

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Stefan-Boltzmann Law

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Stefan-Boltzmann Law

•Note that peak of sun’s energy around 0.5 µm

•negligible after 4-6µm

•Peak of Earth’s radiant energy around 10 µm

•negligible before ~ 4µm

•Total energy in each case is area under curve

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Peak λ of emitted radiation: Wien’s Law•Wien deduced from thermodynamic principles that energy per unit wavelength E(λ) is function of T and λ

•At what λm is maximum radiant energy emitted?

•Comparing blackbodies at different T, note λmT is constant, k = 2897µmK i.e. λm = k/T

•λm, sun = 0.48µm

•λm, Earth = 9.66µm

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)((λλλ TfE =)

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Wien’s Law

•AKA Wien’s Displacement Law

•Increase (displacement) in λmas T reduces

•Straight line in log-log space

Increasing λ

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Planck’s Law of blackbody radiation•Planck was able to explain energy spectrum of blackbody

•Based on quantum theory rather than classical mechanics

( )1

125

2

−=

kThc

e

hcEλλ

πλ

•dE(λ)/dλ gives constant of Wien’s Law

•∫E(λ) over all λ results in Stefan-Boltzmann relation

•Blackbody energy function of λ, and T

http://www.tmeg.com/esp/e_orbit/orbit.htm

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Planck’s Law•Explains/predicts shape of blackbody curve

•Use to predict how much energy lies between given λ•Crucial for remote sensing

http://hyperphysics.phy-astr.gsu.edu/hbase/bbrc.html#c1

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Consequences of Planck’s Law•Allows us to explain radiant energy distribution of any object (e.g. sun)

•Predict at what λ peak energy is emitted and so choose our spectral bands accordingly

•Chlorophyll a,b absorption spectra

•Photosynthetic pigments•Driver of (nearly) all life on Earth!

•Source of all fossil fuel

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Recap• Physical properties we might measure

– E.g. reflectance, temperature, height etc.• EM radiation is what we measure in RS• Blackbody concept used to explain energy

distribution of sun / Earth– Stefan-Boltzmann law explains total energy– Wien’s law explains shift of λmax with decreasing T– Planck’s Law explains shape of BB energy distribution– BUT remember, no object is really a blackbody – only an

approximation

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MODIS: building global picture

From http://visibleearth.nasa.gov/Sensors/Terra/

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IKONOS & QuickBird: very local view!

•QuickBird: 16.5km swath at nadir, 61cm! panchromatic, 2.44m multispectral

•http://www.digitalglobe.com

•IKONOS: 11km swath at nadir, 1m panchromatic, 4m multispectral

•http://www.spaceimaging.com/

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Ikonos: high res. commercial

http://www.spaceimaging.com/gallery/spacepics/khaolak_side_by_side.jpg

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Ikonos: high res.

commercialhttp://www.euspaceimaging.com/sime.asp?page=Gallery