1 Remote Sensing and Image Processing: 10 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: (x24290)

2 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

4 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

6 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 = T 4 –T Sun  6000K M,sun  73.5 MWm -2 –T Earth  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 m T is constant, k = 2897  mK i.e. m = k/T – m, sun = 0.48  m – m, Earth = 9.66  m Lecture 3: Blackbody concept & EMR

7 Blackbody radiation curves

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

9 Lecture 4: image arithmetic and Vegetation Indices (VIs) Basis:

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)

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

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

13 saturated

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

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

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

17 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

18 Low v high resolution? From 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??

19 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

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.)

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

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)

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