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An overview of my current research

An overview of my current research. Dr. Matthew B. Charlton. Click to begin. Introduction. I am currently in Budapest where I am: 1. Finalising publications 2. Continuing and developing existing research 3. Developing new projects (drafting proposals) 4. Developing my teaching materials

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An overview of my current research

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  1. An overview of my current research Dr. Matthew B. Charlton Click to begin

  2. Introduction I am currently in Budapest where I am: 1. Finalising publications 2. Continuing and developing existing research 3. Developing new projects (drafting proposals) 4. Developing my teaching materials 5. Investigating potential field sites

  3. Overview of Presentation 1. General overview of research 2. Research issues 3. Combining GPR and SAR 4. GPR - moisture estimation 5. SAR - the subsurface context 6. Summary & synthesis

  4. - Fezzan Project: Geoarchaeology of the Sahara - Landscape and Environmental Change in Ithaca - Landscape and desertification in S.E.Spain 2. Three main themes, which have evolved from research projects that I have been involved in and am working on. - Ground Penetrating Radar, Soil Moisture and Surface Roughness. - MODULUS Decision Support System Model - Role of failed blocks in river bank erosion / failure - Flood Risk Management Research Consortium - OST Foresight Flood and Coastal Defence Project Research Overview My research: 1. Aims to analyse and understand past, present and future environmental change and processes and to inform management practices. 3. Is particularly concerned with issues of water resources and land degradation under changing environmental conditions. 4. Uses remote sensing and geophysical techniques and a combination of experimental laboratory, field studies and modelling approaches.

  5. Major research issues Direction: Interested in the derivation and spatial characterization of hydrological modelling parameters and soil / surface properties at a variety of scales using a range of remote sensing and geophysical techniques for application to monitoring and modelling environmental change scenarios. However: There are a series of issues associated with the use of recent technological advances to improve our understanding and modelling of surface and subsurface processes at the local and catchment scales: 1. Topographic data 2. Process scale, spatial variability, connectivity 3. Remote sensing for model input 4. Relationships & modelling between remote sensing parameter and surface characteristic 5. Measurement scale 6. Subsurface

  6. Combining SAR and GPR for hydrology I believe a combined GPR and SAR methodology has the potential for addressing many of the issues discussed above: - SAR / GPR can estimate moisture content and other surface properties - Both are based on the same physical principle - Both can be used to study spatial variability - Both measure at different scales - GPR can be used to assess the subsurface - GPR can be used to assess sub-SAR pixel scale variability To this end I will briefly present some results of my work into soil moisture estimation with GPR and the subsurface context in SAR studies, which forms the basis of future development in this area.

  7. GPR soil moisture estimation 1. GPR Principles Measurements based on transmission and reflection of electromagnetic waves. Signal velocity and reflection strength depend on variability in dielectric constant. Dielectric constant depends on volumetric moisture content (VMC). VMC can be determined from signal velocity or some attribute of the returned signal

  8. GPR soil moisture estimation 2. Deriving moisture content - velocity methods (paper submitted to Journal of Hydrology) The traditional approach, using signal velocity based on common midpoint profiling, is limited by low spatial resolution and long survey time (e.g. Galagedara et al. 2001). The fixed-offset method can be used to provide higher spatial resolution and faster survey times (e.g. Galagedara et al. 2005). The accuracy of the method is not yet well established (Huisman et al. 2003) but compared to TDR errors of only ± 0.036 m3/m3 have been achieved (Huisman et al. 2001). I have just re-evaluated earlier laboratory studies to assess the potential of this approach:

  9. GPR soil moisture estimation 3. Fixed-offset velocity methods - laboratory experiments Using a specially developed small test facility (0.58 m deep), a series of experiments were conducted to determine GPR response in six different materials ranging from gravel (M1) to clay (M6) for successive increments of water (5 l each time). 900 MHz GPR in normal survey mode (fixed-offset of 0.17 m) after each water addition. ThetaProbe to validate moisture estimates.

  10. GPR soil moisture estimation Convert to signal velocity: Convert to dielectric constant: TWTT: Convert to VMC: Topp et al. (1980) relationship most commonly used: 4. Fixed-offset velocity methods - deriving VMC (paper in preparation) Process acquired data Measure signal travel time

  11. GPR soil moisture estimation: 5. Fixed-offset velocity methods - results High error due to: Problems with base reflection event identification: - too much signal attenuation at high VMC - for the clay material there was too much signal attenuation at all VMC values ThetaProbe error

  12. GPR soil moisture estimation: The data were inverted and models developed allowing VMC to be estimated from MIA for each material: 6. Fixed-offset attribute analysis - Mean Instantaneous Amplitude (paper in preparation) The preceding methods are of limited use in complex subsurface situations. I used the mean instantaneous amplitude (Charlton 2001, 2002) MIA measures a combination of signal attenuation and reflection events and it: - is easy to determine - consistently produced the strongest relationships for each material - works in the absence of reflector identification - offers potential for deriving an equivalent to SAR backscattter

  13. GPR soil moisture estimation: 7. Testing the models The models were tested on independent data from different experimental runs: - Maximum error only 0.062m3/m3 - Error introduced by variation in recorded MIA values 8. Limitations and summary GPR does measure soil moisture at high resolution but application to field conditions is currently limited by: - surface coupling / roughness - MIA very sensitive to aspects of the subsurface not related to moisture content - MIA very sensitive to the the actual distribution of moisture There is a need for further testing: - to confirm potential - for more rigorous comparison of the fixed-offset velocity and attribute methods - development of physically-based approach to deriving VMC that is appropriate for combination with SAR

  14. Subsurface scattering of SAR signals: Research into combined SAR and GPR was driven by the realisation that remote sensing was not always successful in detecting duricrusts in the Libyan Sahara (important environmental and archaeological record) Landsat ETM JERS-1 Radarsat 1. Introduction (White et al. 2006) This could be due to differences in - SAR system - surface roughness - dielectric constant - subsurface scattering Field data were acquired to parameterise the Integral Equation Model (Fung, 1994).

  15. Subsurface scattering of SAR signals: 2. Integral Equation Model input parameters (Charlton and White, 2006) Frequency 1.275 GHz 5.3 GHz • Roughness Parameters • NSGP1  = 0.53 • L = 47.68 • UBR1  = 0.66 • L = 35.87 • UL1  = 0.24 • L = 20.53 • Exponential and gaussian functions • Sites smooth at L- & C-band • High variability at shorter profile lengths • Drift and periodicity in correlograms Incidence Angle 10-50° JERS: 35.21° RADARSAT: 36.9° Dielectric Constant = 2.5 - 4.1 Average value: 2.88 No significant variation Predictions were then validated against SAR data

  16. Subsurface scattering of SAR signals: 3. Estimated and observed backscatter (Charlton and White, 2006) Backscatter (dB): JERS Predicted: -34.12 Observed: -0.52 Predicted: -30.95 Observed: 2.60 Predicted: -37.70 Observed: 4.33 Backscatter (dB): RADARSAT Predicted: -27.44 Observed: -22.39 Predicted: -23.87 Observed: -21.64 Predicted: -31.67 Observed: -23.86

  17. Subsurface scattering of SAR signals: 4. Comparison of estimated and observed backscatter - errors Problems with the estimations: Backscatter coefficient is underestimated Patterns are wrong for JERS Predicted and RADARSAT: UBR1 > NSGP1 > UL1 JERS UL1 > UBR1 > NSGP1 Reasons: Drift and periodicity in correlograms results in high correlation length L - I am exploring variogram analysis to see if this results in improvements Variability in roughness parameters - there still need to be improvements in measuring and representing surface variability at sub-pixel scales Subsurface scattering not understood - GPR was used to understand the role this could play.

  18. Subsurface scattering of SAR signals: 5. Quantifying GPR response - using Instantaneous Amplitude (Charlton and White, submitted) The combined effects of scattering and signal attenuation were assessed using the instantaneous amplitude This is similar to the principle used earlier in GPR moisture assessment There is greater returned energy at Site 2 (UL1) due to more complex subsurface. Subsurface scattering could explain some of the IEM prediction error

  19. Summary and synthesis: There is potential... Work is needed to: - finalise a combined GPR and SAR methodology - develop parameters from GPR that can have direct application to SAR - assess penetration depth of SAR - combine with geostatistics - apply the approach on a more spatially comprehensive basis - a proposal is being prepared... GPR can measure moisture content It can also be used to understand SAR response With the increased use of low frequency SAR (e.g. PALSAR) the subsurface needs to be considered GPR can be used to understand sub-pixel variability for SAR studies There is potential for combining techniques for multiscale hydrological parameter derivation. Thank you...

  20. Please visitwww.mbcharlton.comfor further information

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