principles of remote sensing 10: radar 3 applications of imaging radar dr. mathias (mat) disney ucl...
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Principles of Remote Sensing 10: RADAR 3Applications 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
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AGENDA
• Single channel data• Radar penetration
• Multi-temporal data• Vegetation, and modelling
• Agriculture & water cloud model• Forest structure and coherent models
• Multi-parameter
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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
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• 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)
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EasternSahara desert
SIR-APenetration 1 – 4 m
Landsat
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Safsaf oasis, Egypt
SIR-C L-band 16 April 1994Landsat
Penetration up to 2 m
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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
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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 ?
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Wetlands in Vietnam - ERS
Oct 97 Jan 99 18 Mar 99 27 May 99
Sept 99 Dec 99 Jan 00 Feb 00
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Wetlands...
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SIR-C (mission 1 left, mission 2 centre, difference in blue on right)
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Floods...
Maastricht
A two date composite of ERS SAR images
30/1/95 (red/green)
21/9/95 (blue)
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Snow cover...Glen Tilt - Blair Atholl
ERS-2 composite
red = 25/11/96
cyan=19/5/97
Scott Polar Research Institute
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Agriculture
Gt. Driffield
Composite of 3 ERS SAR images from different dates
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OSR - Oil seed rapeWW - Winter wheat
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ERS SAR
East Anglia
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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
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Water cloud model
cos
2
cos
20 exp.exp1cos
BLBL
sDmCA
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
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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
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Response to moisture
So
urc
e:
Gra
ha
m 2
00
1
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Detection?
SAR image
In situ irrigation
Source: Graham 2001
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Simulated backscatter
r2 = 0.81
-11
-10
-9
-8
-7
-6-11 -10 -9 -8 -7 -6
Actual backscatter (dB)
CH
IPS
simulated backscatter (dB
)
cos
2
cos
2
0 exp.exp1cosBLBL
sDmCA
r2 = 0.81
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Canopy moisture
r2 = 0.96
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Measured fractional canopy moisture
Sim
ulat
ed fr
actio
nal c
anop
y m
oist
ure
r2 = 0.96
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Applications
• Irrigation fraud detection• Irrigation scheduling• Crop status mapping, e.g.
disease, water stress
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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,...
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Flevoland April 1994
(SIR-C/X-SAR)
(L/C/X composite)
L-total power (red)
C-total power (green)
X-VV (blue)
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Thetford, UK
AIRSAR (1991)
C-HH
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Thetford, UK
AIRSAR (1991)
multi-freq composite
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Thetford, UK
SHAC (SAR and Hyperspectral Airborne Campaign)
http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66Disney 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
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Thetford, UK
SHAC (SAR and Hyperspectral Airborne Campaign)
http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66Disney 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
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Optical signal with age for different tree density (HyMAP optical data)
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Coherent (polarised) modelled RADAR signal (CASM)
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OPTICAL
RADAR
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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
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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
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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
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Original SAR data
Frost filter
Gamma MAP filter
Simulated annealing
Ret
ford
, UK
ER
S-2
SA
R d
ata
Apr
il –
Sep
tem
ber
1998
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Original SAR data
Frost filter
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Gamma MAP filter
Simulated annealing
Recommendation : use these two
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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