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

Remote Sensing and Image Processing: 10

Dr. Hassan J. Eghbali

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• 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

Revision: Lecture 1


3

• Intro to EM spectrum• Continuous range of • …UV, Visible, near IR, thermal, microwave, radio…

• shorter (higher f) == higher energy

• longer (lower f) == lower energy

Lecture 1


Spectral information: e.g. vegetation


5

• 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.

Lecture 2


• 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 = 2897mK i.e. m = k/T m, sun = 0.48m m, Earth = 9.66m

Lecture 3: Blackbody concept & EMR


Blackbody radiation curves


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


Lecture 4: image arithmetic and Vegetation Indices (VIs)

• Basis:


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)


Some VIs

• RVI (ratio)

• DVI (difference)

• NDVI

RVI nir

red

DVI nir red

NDVI nir red

nir red

NDVI = Normalised Difference Vegetation Index i.e. combine RVI and DVI


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


saturated


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


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


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


• 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)

Lecture 7: Resolution


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??


• 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

Lecture 8: temporal sampling


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.)


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


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)


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


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