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

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
5. Investigating potential field sites
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
Research Overview
My research:
1. Aims to analyse and understand past,
present and future environmental
change and processes and to inform
management practices.
- 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.
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.
- 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
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
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.
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
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:
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.
GPR soil moisture estimation
4. Fixed-offset velocity methods - deriving VMC
(paper in preparation)
Process acquired data
Measure signal travel time
Convert to signal velocity:
v  d /(TWTT / 2 )
Convert to dielectric
constant:
2
TWTT:
 r  ( 0 .3 / v )
Convert to VMC:
Topp et al. (1980) relationship
most commonly used:
 v   5 . 3  10
2
 2 . 92  10
2
 r  5 . 5  10
4
 r  4 . 3  10
2
6
r
3
GPR soil moisture estimation:
5. Fixed-offset velocity methods - results
High error due to:
3
O bse rv ed V M C (m /m )
0.5
3
0.4
M1
0.3
M2
M3
0.2
Problems with base
reflection event
identification:
M4
M5
0.1
0
0
0.1
0.2
0.3
3
0.4
3
P red icted V M C (m /m )
M aterial
M1
M2
M3
M4
M5
0.5
- too much signal
attenuation at high
VMC
A bsolute D e viation fro m e xpected
3
3
(m /m )
M ean
M axim um
0.0791
0.1893
0.0332
0.0773
0.0798
0.1368
0.0340
0.0718
0.0825
0.1272
- for the clay
material there was
too much signal
attenuation at all
VMC values
ThetaProbe error
GPR soil moisture estimation:
6. Fixed-offset attribute analysis - Mean Instantaneous Amplitude
(paper in preparation)
The preceding methods are of limited
use in complex subsurface situations.
The data were inverted and models
developed allowing VMC to be
estimated from MIA for each material:
I used the mean instantaneous
amplitude (Charlton 2001, 2002)
0.5
M1
M2
M3
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
3
3
V M C (m /m )
0.4
M4
0.3
M5
M6
0.2
L ine a r (M 1 )
L ine a r (M 2 )
0.1
L o g . (M 3 )
E xp o n. (M 4 )
0
L ine a r (M 5 )
0
10000
20000
30000
40000
E xp o n. (M 6 )
M ean In stan tan eo u s A m p litu d e (u V )
M aterial
M1
M2
M3
M4
M5
M6
O verall
F o rm
L inear
L inear
L o garith m ic
E xp o nential
L inear
E xp o nential
E xp o nential
B est-F it R elatio nship
P aram eter 1
P aram eter 2
0 .5 8 73
-2 .1 3 E -0 5
0 .5 1 71
-1 .7 6 E -0 5
-0 .3 0 7 3
3 .1 9 09
4 .4 8 02
-0 .0 0 0 2
0 .6 3 54
-1 .9 6 E -0 5
0 .6 1 77
-0 .0 0 0 1
0 .6 5 35
-0 .0 0 0 1
2
R
0 .8 5 24
0 .9 5 56
0 .9 5 76
0 .9 4 01
0 .8 9 01
0 .9 5 92
0 .5 1 24
GPR soil moisture estimation:
7. Testing the models
8. Limitations and summary
The models were tested on
independent data from different
experimental runs:
M1
M2
M3
M4
M5
M6
Observed VMC (m3/m3)
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
Predicted VMC (m3/m3)
- Maximum error only 0.062m3/m3
- Error introduced by variation in
recorded MIA values
0.5
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
Subsurface scattering of SAR signals:
1. Introduction (White et al. 2006)
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)
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).
Landsat ETM
JERS-1
Radarsat
Subsurface scattering of SAR signals:
2. Integral Equation Model input parameters (Charlton and White, 2006)
Frequency
1.275 GHz
5.3 GHz
Incidence Angle
10-50°
JERS: 35.21°
RADARSAT: 36.9°
Dielectric Constant
= 2.5 - 4.1
Average value: 2.88
No significant
variation
Roughness Parameters
 = 0.53
L = 47.68
UBR1
 = 0.66
L = 35.87
UL1
 = 0.24
L = 20.53
Exponential and gaussian functions
NSGP1
• Sites smooth at L- & C-band
• High variability at shorter profile lengths
• Drift and periodicity in correlograms
Predictions were then validated against SAR data
Subsurface scattering of SAR signals:
3. Estimated and observed backscatter (Charlton and White, 2006)
Backscatter (dB):
JERS
Backscatter (dB):
RADARSAT
Predicted: -34.12
Observed: -0.52
Predicted: -27.44
Observed: -22.39
Predicted: -30.95
Observed: 2.60
Predicted: -23.87
Observed: -21.64
Predicted: -37.70
Observed: 4.33
Predicted: -31.67
Observed: -23.86
Subsurface scattering of SAR signals:
4. Comparison of estimated and observed backscatter - errors
Problems with the estimations:
Reasons:
Backscatter coefficient is
underestimated
Drift and periodicity in correlograms
results in high correlation length L
Patterns are wrong for JERS
- I am exploring variogram analysis to
see if this results in improvements
Predicted and RADARSAT:
UBR1 > NSGP1 > UL1
JERS
UL1 > UBR1 > NSGP1
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.
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
S ite
A
A
B
B
GPR
frequency
(M H z)
450
900
450
900
A i (D N )
2256
1425
2546
2016
Summary and synthesis:
There is potential...
Work is needed to:
GPR can measure moisture
content
- finalise a combined GPR and
SAR methodology
It can also be used to understand
SAR response
- develop parameters from GPR
that can have direct application to
SAR
With the increased use of low
frequency SAR (e.g. PALSAR) the
subsurface needs to be considered
- assess penetration depth of SAR
- combine with geostatistics
GPR can be used to understand
sub-pixel variability for SAR studies
There is potential for combining
techniques for multiscale
hydrological parameter derivation.
- apply the approach on a more
spatially comprehensive basis
- a proposal is being prepared...
Thank you...
Please visit www.mbcharlton.com for further information