Transcript BBK 05 lecture 10 - UCL Department of Geography

Remote Sensing and Image
Processing: 10
Dr. Mathias (Mat) Disney
UCL Geography
Office: 301, 3rd Floor, Chandler House
Tel: 7670 4290 (x24290)
Email: [email protected]
www.geog.ucl.ac.uk/~mdisney
1
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
2
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
3
Spectral information: e.g. vegetation
4
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.
5
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
6
Blackbody radiation curves
7
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
http://hyperphysics.phy-astr.gsu.edu/hbase/bbrc.html#c1
8
Lecture 4: image arithmetic and
Vegetation Indices (VIs)
• Basis:
9
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)
10
Some VIs
 nir
 red
• RVI (ratio)
RVI 
• DVI (difference)
DVI   nir   red
• NDVI
NDVI 
  nir   red 
 nir   red 
NDVI = Normalised Difference Vegetation Index i.e. combine
RVI and DVI
11
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
12
saturated
13
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
14
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
15
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
16
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)
17
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??
From http://modis.gsfc.nasa.gov/about/specs.html
18
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
19
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.)
20
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
21
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)
22
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
23