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BBK 05 lecture 10

VIEWS: 0 PAGES: 23

									Remote Sensing and Image
     Processing: 10




                    Dr. Hassan J. Eghbali
                Revision: Lecture 1
• Introductions and definitions
   – EO/RS is obtaining information at a distance from target
      • Spatial, spectral, temporal, angular, polarization etc.
   – Measure reflected / emitted / backscattered EMR and
     INFER biophysical properties from these
   – Range of platforms and applications, sensors, types of
     remote sensing (active / passive)
• Why EO?
   – Global coverage (potentially), synoptic, repeatable….
   – Can do in inaccessible regions


                                                              Dr. Hassan J. Eghbali
                      Lecture 1

• Intro to EM spectrum
     •   Continuous range of 
     •   …UV, Visible, near IR, thermal, microwave, radio…
     •   shorter  (higher f) == higher energy
     •   longer  (lower f) == lower energy




                                                Dr. Hassan J. Eghbali
Spectral information: e.g. vegetation




                               Dr. Hassan J. Eghbali
                            Lecture 2
• Image processing
   – NOT same as remote sensing
   – Display and enhancement; information extraction
• Display
   – Colour composites of different bands
       • E.g. standard false colour composite (NIR, R, G on red, green, blue to
         highlight vegetation)
   – Colour composites of different dates
   – Density slicing, thresholding
• Enhancement
   – Histogram manipulation
       • Make better use of dynamic range via histogram stretching, histogram
         equalisation etc.




                                                                  Dr. Hassan J. Eghbali
 Lecture 3: Blackbody concept & EMR
• 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)
• Total emitted radiation from a blackbody, M, described by
  Stefan-Boltzmann Law M = T4
   – TSun  6000K M,sun  73.5 MWm-2
   – TEarth  300K M, Earth  460 Wm-2
• Wien’s Law (Displacement Law)
   – Energy per unit wavelength E() is function of T and 
   – As T↓ peak  of emitted radiation gets longer
• For 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



                                                              Dr. Hassan J. Eghbali
Blackbody radiation curves




                             Dr. Hassan J. Eghbali
                          Planck’s Law
•Explains/predicts shape of blackbody curve
•Use to predict how much energy lies between given 
   •Crucial for remote sensing as it tells us how energy is distributed across
   EM spectrum




                                                                Dr. Hassan J. Eghbali
 Lecture 4: image arithmetic and
     Vegetation Indices (VIs)
• Basis:




                           Dr. Hassan J. Eghbali
                     Why VIs?
• Empirical relationships with range of vegetation /
  climatological parameters
    fAPAR – fraction of absorbed photosynthetically active
     radiation (the bit of solar EM spectrum plants use)
    NPP – net primary productivity (net gain of biomass by
     growing plants)
 simple to understand/implement
 fast – per scene operation (ratio, difference etc.), not
  per pixel (unlike spatial filtering)


                                                   Dr. Hassan J. Eghbali
                      Some VIs
                                      nir
 • RVI (ratio)               RVI 
                                      red


 • DVI (difference)          DVI   nir   red




 • NDVI                       NDVI 
                                         nir   red 
                                        nir   red 


NDVI = Normalised Difference Vegetation Index i.e. combine
RVI and DVI
                                                           Dr. Hassan J. Eghbali
    limitations of NDVI
 NDVI is empirical i.e. no physical meaning
 atmospheric effects:
     esp. aerosols (turbid - decrease)
     Correct via direct methods - atmospheric
      correction or indirect methods e.g. new idices
      e.g. atmos.-resistant VI (ARVI/GEMI)
 sun-target-sensor effects (BRDF):
     Max. value composite (MVC) - ok on cloud, not
      so effective on BRDF
 saturation problems !!!
     saturates at LAI of > 3


                                           Dr. Hassan J. Eghbali
saturated




Dr. Hassan J. Eghbali
  Lecture 5: atmosphere and surface interactions
• Top-of-atmosphere (TOA) signal is NOT target signal
   – function of target reflectance
   – plus atmospheric component (scattering, absorption)
   – need to choose appropriate regions of EM spectrum to view
     target (atmospheric windows)
• Surface reflectance is anisotropic
   – i.e. looks different in different directions
   – described by BRDF
   – angular signal contains information on size, shape and
     distribution of objects on surface



                                                    Dr. Hassan J. Eghbali
                     Atmospheric windows




• If you want to look at surface
   – Look in atmospheric windows where transmissions high
   – BUT if you want to look at atmosphere ....pick gaps
• Very important when selecting instrument channels
   – Note atmosphere nearly transparent in wave i.e. can see through clouds!
   – BIG advantage of wave remote sensing



                                                                Dr. Hassan J. Eghbali
     Lecture 6: Spatial filtering
• Spatial filters divided into two broad categories
   – Feature detection e.g. edges
       • High pass filter
   – Image enhancement e.g. smoothing “speckly” data e.g. RADAR
       • Low pass filters




                                                       Dr. Hassan J. Eghbali
                Lecture 7: Resolution
• Spatial resolution
   – Ability to separate objects spatially (function of optics and orbit)
• Spectral resolution
   – location, width and sensitivity of chosen  bands (function of detector
     and filters)
• Temporal resolution
   – time between observations (function of orbit and swath width)
• Radiometric resolution
   – precision of observations (NOT accuracy!) (determined by detector
     sensitivity and quantisation)




                                                               Dr. Hassan J. Eghbali
                Low v high resolution?
• Tradeoff of coverage v detail (and data volume)
• Spatial resolution?
   – Low spatial resolution means can cover wider area
   – High res. gives more detail BUT may be too much data (and less
     energy per pixel)
• Spectral resolution?
   – Broad bands = less spectral detail BUT greater energy per band
   – Dictated by sensor application
       • visible, SWIR, IR, thermal??




                                                            Dr. Hassan J. Eghbali
        Lecture 8: temporal sampling
• Sensor orbit
   – geostationary orbit - over same spot
      • BUT distance means entire hemisphere is viewed e.g. METEOSAT
   – polar orbit can use Earth rotation to view entire surface
• Sensor swath
   – Wide swath allows more rapid revisit
      • typical of moderate res. instruments for regional/global
        applications
   – Narrow swath == longer revisit times
      • typical of higher resolution for regional to local applications




                                                              Dr. Hassan J. Eghbali
                       Tradeoffs
• Tradeoffs always made over resolutions….
  – We almost always have to achieve compromise between
    greater detail (spatial, spectral, temporal, angular etc) and
    range of coverage
  – Can’t cover globe at 1cm resolution – too much
    information!
  – Resolution determined by application (and limitations of
    sensor design, orbit, cost etc.)




                                                     Dr. Hassan J. Eghbali
    Lecture 9: vegetation and terrestrial
                carbon cycle
• Terrestrial carbon cycle is global
• Primary impact on surface is vegetation / soil system
• So need monitoring at large scales, regularly, and
  some way of monitoring vegetation……
   – Hence remote sensing in conjunction with in situ
     measurement and modelling




                                                   Dr. Hassan J. Eghbali
       Vegetation and carbon
 We can use complex models of carbon cycle
   Driven by climate, land use, vegetation type and
    dynamics, soil etc.
   Dynamic Global Vegetation Models (DGVMS)
 Use EO data to provide….
   Land cover
   Estimates of “phenology” veg. dynamics (e.g. LAI)
   Gross and net primary productivity (GPP/NPP)



                                            Dr. Hassan J. Eghbali
      EO and carbon cycle: current
 Use global capability of MODIS, MISR,
  AVHRR, SPOT-VGT...etc.
     Estimate vegetation cover (LAI)
     Dynamics (phenology, land use change etc.)
     Productivity (NPP)
     Disturbance (fire, deforestation etc.)
        Compare with models and measurements
        AND/OR use to constrain/drive models




                                                Dr. Hassan J. Eghbali

								
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