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Principles of Remote Sensing 10: RADAR 3 Applications of imaging RADAR

Principles of Remote Sensing 10: RADAR 3 Applications of imaging RADAR. Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 Email: mdisney@ucl.geog.ac.uk www.geog.ucl.ac.uk/~mdisney. AGENDA. Single channel data Radar penetration Multi-temporal data

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Principles of Remote Sensing 10: RADAR 3 Applications of imaging RADAR

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  1. Principles of Remote Sensing 10: RADAR 3Applications of imaging RADAR Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 Email: mdisney@ucl.geog.ac.uk www.geog.ucl.ac.uk/~mdisney

  2. AGENDA • Single channel data • Radar penetration • Multi-temporal data • Vegetation, and modelling • Agriculture & water cloud model • Forest structure and coherent models • Multi-parameter

  3. Observations of forests... • C-band (cm-tens of cm) • low penetration depth, leaves / needles / twigs • L-band • leaves / branches • P-band • can propagate through canopy to branches, trunk and ground • C-band quickly saturates (even at relatively low biomass, it only sees canopy); P-band maintains sensitivity to higher biomass as it “sees” trunks, branches, etc • Low biomass behaviour dictated by ground properties

  4. Surfaces - scattering depends on moisture and roughness • Note - we could get penetration into soils at longer wavelengths or with dry soils (sand) • Surfaces are typically • bright if wet and rough • dark if dry and smooth • What happens if a dry rough surface becomes wet ? • Note similar arguments apply to snow or ice surfaces. • Note also, always need to remember that when vegetation is present, it can act as the dominant scatterer OR as an attenuator (of the ground scattering)

  5. EasternSahara desert Landsat SIR-A Penetration 1 – 4 m

  6. Safsaf oasis, Egypt Penetration up to 2 m Landsat SIR-C L-band 16 April 1994

  7. Single channel data • Many applications are based on the operationally-available spaceborne SARs, all of which are single channel (ERS, Radarsat, JERS) • As these are spaceborne datasets, we often encounter multi-temporal applications (which is fortunate as these are only single-channel instruments !) • When thinking about applications, think carefully about “where” the information is:- • scattering physics • spatial information (texture, …) • temporal changes

  8. Multi-temporal data • Temporal changes in the physical properties of regions in the image offer another degree of freedom for distinguishing them but only if these changes can actually be seen by the radar • for example - ERS-1 and ERS-2:- • wetlands, floods, snow cover, crops • implications for mission design ?

  9. Wetlands in Vietnam - ERS Oct 97 Jan 99 18 Mar 99 27 May 99 Sept 99 Dec 99 Jan 00 Feb 00

  10. Wetlands...

  11. SIR-C (mission 1 left, mission 2 centre, difference in blue on right)

  12. Floods... Maastricht A two date composite of ERS SAR images 30/1/95 (red/green) 21/9/95 (blue)

  13. Snow cover... Glen Tilt - Blair Atholl ERS-2 composite red = 25/11/96 cyan=19/5/97 Scott Polar Research Institute

  14. Agriculture Gt. Driffield Composite of 3 ERS SAR images from different dates

  15. OSR - Oil seed rape WW - Winter wheat

  16. ERS SAR East Anglia

  17. Radar modelling • Surface roughness • Volume roughness • Dielectric constant ~ moisture • Models of the vegetation volume, e.g. water cloud model of Attema and Ulaby, RT2 model of Saich Multitemporal SHAC radar image Barton Bendish

  18. Water cloud model A – vegetation canopy backscatter at full cover B – canopy attenuation coefficient C – dry soil backscatter D – sensitivity to soil moisture σ0 = scattering coefficient ms = soil moisture θ = incidence angle L = leaf area index Vegetation

  19. Values of A, B, C, D

  20. Response to moisture Source: Graham 2001

  21. Detection? SAR image In situ irrigation Source: Graham 2001

  22. Simulated backscatter r2 = 0.81

  23. Canopy moisture r2 = 0.96

  24. Applications • Irrigation fraud detection • Irrigation scheduling • Crop status mapping, e.g. disease, water stress

  25. Multi-parameter radar • More sophisticated instruments have multi-frequency, multi-polarisation radars, with steerable beams (different incidence angle) • Also, different modes • combinations of resolutions and swath widths • SIR-C / X-SAR • ENVISAT ASAR, ALOS PALSAR,...

  26. Flevoland April 1994 (SIR-C/X-SAR) (L/C/X composite) L-total power (red) C-total power (green) X-VV (blue)

  27. Thetford, UK AIRSAR (1991) C-HH

  28. Thetford, UK AIRSAR (1991) multi-freq composite

  29. Coherent RADAR modelling Thetford, UK SHAC (SAR and Hyperspectral Airborne Campaign) http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66 Disney et al. (2006) – combine detailed structural models with optical AND RADAR models to simulate signal in both domains Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et al. (2001)

  30. Coherent RADAR modelling Thetford, UK SHAC (SAR and Hyperspectral Airborne Campaign) http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66 Disney et al. (2006) – combine detailed structural models with optical AND RADAR models to simulate signal in both domains Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et al. (2001)

  31. Optical signal with age for different tree density (HyMAP optical data)

  32. Coherent (polarised) modelled RADAR signal (CASM)

  33. OPTICAL RADAR

  34. An ambitious list of Applications... • Flood mapping, Snow mapping, Oil Slicks • Sea ice type, Crop classification, • Forest biomass / timber estimation, tree height • Soil moisture mapping, soil roughness mapping / monitoring • Pipeline integrity • Wave strength for oil platforms • Crop yield, crop stress • Flood prediction • Landslide prediction

  35. CONCLUSIONS • Radar is very reliable because of cloud penetration and day/night availability • Major advances in interferometric SAR • Should radar be used separately or as an adjunct to optical Earth observation data? ALOS

  36. Speckle filtering Mean Median Lee Lee-Sigma Local Region Frost Gamma Maximum a Posteriori (MAP) Simulated annealing: modelling what the radar backscatter would have been like without the speckle

  37. Original SAR data Frost filter Gamma MAP filter Simulated annealing Retford, UK ERS-2 SAR data April – September 1998

  38. Original SAR data Frost filter

  39. Gamma MAP filter Simulated annealing Recommendation : use these two

  40. Discussion question • What sort of radars are preferred for the following applications to be successfully realised and what is the physical basis? • Forest mapping • Flood extent • Soil moisture in vegetated areas • Snow mapping

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