Transcript Vegetation monitoring for climate studies

Remote Sensing and Image
Processing: 3
Dr. Mathias (Mat) Disney
UCL Geography
Office: 301, 3rd Floor, Chandler House
Tel: 7670 4290
Email: [email protected]
www.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”
External forcing
Hydrosphere
Cryosphere
Atmosphere
Geosphere
Biosphere
From Ruddiman, W.
F., 2001. Earth's
Climate: past and
future.
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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 process
Analogue
image
processing
•Multi:
•spectral, spatial,
temporal, angular,
scale, disciplinary
•Visualisation
•Ancillary info.:
field and lab
measurements,
literature etc.
After Jensen, p. 22
Image
interpretation
Presentation
of information
•Tone, colour,
stereo parallax
Primary
elements
•Size, shape,
texture,
pattern, fractal
dimension
Spatial
arrangements
•Height/shadow
Secondary
elements
•Site,
association
Context
•Multi:
•spectral, spatial,
temporal, angular,
scale, disciplinary
•Statistical/rulebased patterns
•Hyperspectral
•Modelling and
simulation
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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
information that can be 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/)
– shorter  (higher f) == higher energy
– longer  (lower f) == lower energy
from http://rst.gsfc.nasa.gov/Intro/Part2_4.html
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Units
•EM wavelength  is m, but various prefixes
•cm (10-2m)
•mm (10-3m)
•micron or micrometer, m (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/ 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, )
•When radiation passes from one medium to another, speed of light (c) and  change,
hence f stays the same
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Electromagnetic spectrum: visible
• Visible part - very small
part
– from visible blue (shorter
)
– to visible red (longer )
– ~0.4 to ~0.7m
Violet: 0.4 - 0.446 m
Blue: 0.446 - 0.500 m
Green: 0.500 - 0.578 m
Yellow: 0.578 - 0.592 m
Orange: 0.592 - 0.620 m
Red: 0.620 - 0.7 m
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Electromagnetic spectrum: IR
• Longer wavelengths (submm)
• 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  so low energy,
hence need to use
own energy source
(active wave)
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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, , 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, M, in Wm-2,
described by Stefan-Boltzmann Law
M   T
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•Where T is temperature of the object in K; and  = is
Stefan-Boltzmann constant = 5.6697x10-8 Wm-2K-4
•So energy  T4 and as T so does M
•Tsun  6000K M,sun  73.5 MWm-2
•TEarth  300K M , 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 m
•negligible after 4-6m
•Peak of Earth’s radiant energy around 10 m
•negligible before ~ 4m
•Total energy in each case is area under curve
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Peak  of emitted radiation: Wien’s Law
•Wien deduced from thermodynamic principles that
energy per unit wavelength E() is function of T and 
f (T )
E (  
5
•At what m is maximum radiant energy emitted?
•Comparing blackbodies at different T, note mT is
constant, k = 2897mK i.e. m = k/T
•m, sun = 0.48m
•m, Earth = 9.66m
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Wien’s Law
•AKA Wien’s
Displacement Law
•Increase
(displacement) in m
as T reduces
Increasing 
•Straight line in loglog space
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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
E   
2c 2 h
5
1
e
hc
kT
1
•dE()/d gives constant of Wien’s Law
•E() over all  results in Stefan-Boltzmann relation
•Blackbody energy function of , 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 
•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  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 max 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!
•IKONOS: 11km swath at nadir, 1m
panchromatic, 4m multispectral
•QuickBird: 16.5km swath at nadir, 61cm!
panchromatic, 2.44m multispectral
•http://www.spaceimaging.com/
•http://www.digitalglobe.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.
commercial
http://www.euspaceimaging.com/sime.asp?page=
Gallery
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