Transcript Radar Remote Sensing - UCL Department of Geography

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: [email protected]
www.geog.ucl.ac.uk/~mdisney
AGENDA
• Single channel data
• Radar penetration
• Multi-temporal data
• Vegetation, and modelling
• Agriculture & water cloud model
• Forest structure and coherent models
• Multi-parameter
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
• 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)
Eastern
Sahara desert
Landsat
SIR-A
Penetration 1 – 4 m
Safsaf oasis, Egypt
Penetration
up to 2 m
Landsat
SIR-C L-band 16 April 1994
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 multitemporal 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
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 ?
Wetlands in Vietnam - ERS
Oct 97
Sept 99
Jan 99
Dec 99
18 Mar 99
Jan 00
27 May 99
Feb 00
Wetlands...
SIR-C (mission 1 left, mission 2 centre, difference in blue on right)
Floods...
Maastricht
A two date composite of ERS
SAR images
30/1/95 (red/green)
21/9/95 (blue)
Snow cover...
Glen Tilt - Blair Atholl
ERS-2 composite
red = 25/11/96
cyan=19/5/97
Scott Polar Research Institute
Agriculture
Gt. Driffield
Composite of
3 ERS SAR
images from
different dates
OSR - Oil seed rape
WW - Winter wheat
ERS SAR
East Anglia
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
Water cloud model


 2 BL 
 2 BL 






cos


 0  A cos 1  exp cos    C  Dm . exp
s


A – vegetation canopy backscatter
at full cover
B – canopy attenuation coefficient
C – dry soil backscatter
σ0 = scattering coefficient
ms = soil moisture
θ = incidence angle
L = leaf area index
D – sensitivity to soil moisture
Vegetation
Values of A, B, C, D
Parameter
Value
Units / description
A
-10.351
dB
B
1.945
Fractional canopy moisture
C
-23.640
dB
D
0.262
Fractional soil moisture
Source: Graham 2001
Response to moisture
Detection?
SAR image
In situ irrigation
Source: Graham 2001
Simulated backscatter
Actual backscatter (dB)
-11
-10
-9
-8
-7
-6
-6
r2 = 0.81
-8
-9


 2 BL 
 2 BL 





0
 cos  
 cos  
  A cos 1  exp
  C  Dm . exp
s


-10
-11
CHIPS simulated backscatter (dB)
-7
r2 = 0.81
Canopy moisture
1
Simulated fractional canopy moisture
r2 = 0.96
0.8
r2 = 0.96
0.6
0.4
0.2
0
0
0.2
0.4
0.6
Measured fractional canopy moisture
0.8
1
Applications
• Irrigation fraud detection
• Irrigation scheduling
• Crop status mapping, e.g.
disease, water stress
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,...
Flevoland April 1994
(SIR-C/X-SAR)
(L/C/X composite)
L-total power (red)
C-total power (green)
X-VV (blue)
Thetford, UK
AIRSAR (1991)
C-HH
Thetford, UK
AIRSAR (1991)
multi-freq
composite
Coherent RADAR modelling
Thetford, UK
SHAC (SAR and
Hyperspectral
Airborne
Campaign)
http://www.neodc.rl
.ac.uk/?option=dis
playpage&Itemid=
66&op=page&Sub
Menu=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)
Coherent RADAR modelling
Thetford, UK
SHAC (SAR and
Hyperspectral
Airborne
Campaign)
http://www.neodc.rl
.ac.uk/?option=dis
playpage&Itemid=
66&op=page&Sub
Menu=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)
Optical signal with age for different tree density (HyMAP optical data)
Coherent (polarised) modelled RADAR signal (CASM)
OPTICAL
RADAR
An ambitious list of Applications...
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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
CONCLUSIONS
ALOS
• 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?
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
Gamma
MAP
filter
Frost
filter
Simulated
annealing
Retford, UK ERS-2 SAR data April – September 1998
Original
SAR data
Original
SAR data
Frost
filter
Gamma
MAP
filter
Simulated
annealing
Recommendation : use these two
Discussion question
• What sort of radars are preferred for the following
applications to be successfully realised and what
is the physical basis?
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–
–
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Forest mapping
Flood extent
Soil moisture in vegetated areas
Snow mapping