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

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

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 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

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

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 RVI (ratio) DVI (difference) NDVI 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 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

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