Remote Sensing and Image Processing: 3 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: 7670 4290 Email: mdisney@geog.ucl.ac.uk www.geog.ucl.ac.uk/~mdisney

What sort of parameters are of interest? Back to the process.... What sort of parameters are of interest? Variables describing Earth system....

EO and the Earth “System” Atmosphere EO and the Earth “System” External forcing Cryosphere Geosphere Biosphere Hydrosphere From Ruddiman, W. F., 2001. Earth's Climate: past and future.

Example biophysical variables After Jensen, p. 9

Example biophysical variables Good discussion of spectral information extraction: http://dynamo.ecn.purdue.edu/~landgreb/Principles.pdf After Jensen, p. 9

Remote Sensing Examples

Information extraction process Analogue image processing Multi: spectral, spatial, temporal, angular, scale, disciplinary Visualisation Ancillary info.: field and lab measurements, literature etc. Image interpretation Tone, colour, stereo parallax Size, shape, texture, pattern, fractal dimension Height/shadow Site, association Primary elements Spatial arrangements Secondary elements Context Presentation of information Multi: spectral, spatial, temporal, angular, scale, disciplinary Statistical/rule-based patterns Hyperspectral Modelling and simulation After Jensen, p. 22

Example: Vegetation canopy modelling Develop detailed 3D models Simulate canopy scattering behaviour Compare with observations

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

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

Remote sensing process: recap

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

Energy transport Conduction Convection Radiation 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

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)

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

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

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

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

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.

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)

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)

“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?

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

Stefan-Boltzmann Law

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

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

Wien’s Law AKA Wien’s Displacement Law Increase (displacement) in m as T reduces Straight line in log-log space Increasing 

Planck’s Law of blackbody radiation Planck was able to explain energy spectrum of blackbody Based on quantum theory rather than classical mechanics 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

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

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

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

MODIS: building global picture From http://visibleearth.nasa.gov/Sensors/Terra/

IKONOS & QuickBird: very local view! IKONOS: 11km swath at nadir, 1m panchromatic, 4m multispectral http://www.spaceimaging.com/ QuickBird: 16.5km swath at nadir, 61cm! panchromatic, 2.44m multispectral http://www.digitalglobe.com

Ikonos: high res. commercial http://www.spaceimaging.com/gallery/spacepics/khaolak_side_by_side.jpg

Ikonos: high res. commercial http://www.euspaceimaging.com/sime.asp?page=Gallery