1. Remote Sensing and Image Processing: 10
Dr. Hassan J. Eghbali

2. Revision: Lecture 1 Introductions and definitions Why EO?
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

3. 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

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

5. Lecture 2 Image processing Display Enhancement
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

6. Lecture 3: Blackbody concept & EMR
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

7. Blackbody radiation curves
Dr. Hassan J. Eghbali

8. 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

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

10. 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

11. Some VIs RVI (ratio) DVI (difference) NDVI
NDVI = Normalised Difference Vegetation Index i.e. combine RVI and DVI
Dr. Hassan J. Eghbali

12. 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

13. saturated
Dr. Hassan J. Eghbali

14. 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

15. 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

16. 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

17. Lecture 7: Resolution Spatial resolution Spectral 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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