chuepoh_igarss2011.ppt
TRANSCRIPT
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SEE THE FORESTS WITH DIFFERENT EYES
CHUE POH TAN
ARMANDO MARINO
IAIN WOODHOUSE
SHANE CLOUDE
JUAN SUAREZ
COLIN EDWARDS
A contribution from radar polarimetry
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Overview
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OUTLINE Combination of sensors (RADAR + LIDAR) may
be a powerful solution
This work will treat meanly the RADAR part of the problem
Backscatter-biomass regression is problematic on mountainous terrain and sparse woodlands
Polarimetry can be our SAVIOUR !
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STUDY AREA – GLEN AFFRIC
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STUDY AREA – GLEN AFFRIC
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FIELDWORK
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FIELDWORK
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ALOS PALSAR
• L-band (1.27 GHz)
• Quad-pol data
• Ascending mode
• Incidence Angle 23o
• Data acquisition 17 April 2007 & 08 June 2009
ALOS PALSAR
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• X-band (9.6 GHz)
• Quad-pol data
• Ascending mode
• Incidence Angle 43o
• Data acquisition 13 April 2010
TerraSAR-X
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•Ability to capture high resolution images quickly and relatively independent of lighting conditions
•Ability to collect 20000 to 75000 records per second
•High vertical accuracy with 15-20cm RMS and horizontal accuracy with 20-30cm RMS
•Data acquisition 9 June 2007
LiDAR: Optech ALTM 3100 LiDAR system
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• Many studies on forest volume/ biomass estimation in flat areas(Kurvonen et al., 1999; Saatchi et al., 2001; Austin et al., 2003)
•Kurvonen L. et al., “Retrieval of Biomass in Boreal Forests from Multitempotal ERS-1 and JERS-1 SAR Images,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, pp. 198-206, 1999.•Figure Reference: Mette, T. Papathanassiou et al., “Forest biomass estimation using polarimetric SAR interferometry,” IEEE International Geoscience and Remote Sensing Symposium, Vol.2, pp. 817- 819, 2002.
PREVIOUS WORK
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•The level of saturation has been found to vary as a function of polarization and forest types at L-band. (M. L. Imhoff ,1995;Luckman ,1998;L. Kurvonen,1999)•Studies have been done on biomass estimation using backscatter-biomass relationship (Kasischke et al., 1995; Ranson et al.,1996; Mitchard et. al)
Figure Reference: http://www.eci.ox.ac.uk/research/biodiversity/linkcarbon.php Mitchard, E.T.A., S.S. Saatchi, I.H. Woodhouse, T.R. Feldpausch, S.L. Lewis, B. Sonké, C. Rowland, & P. Meir. In press. Measuring biomass changes due to woody encroachment and deforestation/degradation in a forest-savanna boundary region of central Africa using multi-temporal L-band radar backscatter. In press, Remote Sensing of Environment.)
REGRESSION ANALYSIS – HV
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HV=-14.2151+1.8251*(1-exp(0.0488*AGB)), R2=0.36
REGRESSION ANALYSIS – HV
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Methodology
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METHODOLOGY
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STUDY AREA
Aerial Photograph DEM
1500m
1500
mArea
Google Earth
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LIDAR DATA PROCESSING
CSM
Canopy
Heig
ht M
odel
CHM
Canopy
Surfa
ce M
odel
LIDAR
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LIDAR DATA PROCESSING
Height derived from LiDAR
Biomass LiDAR
Canopy Cover derived from LiDAR
LIDAR
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Polarisation: Scatterers have different polarisation behaviour
M-POL 19/35
Radar Polarimetric Observations
Cylin
der:
An
isotr
op
ic Ground:
No depolarisationGround:No depolarisation
Sphere:isotropic
+ …
Cylin
der:
An
isotr
op
ic
Ground:No depolarisation
Ground:No depolarisation
Sphere:isotropic
+ …
RADAR
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RADAR POLARIMETRYAZIMUTH SLOPE REMOVAL
SRR =(SHH-SVV+ i SHV)/2SLL =(SVV-SHH+ i SHV)/2
θ=[Arg(< SRRSLL *>) + π ]/4]
For θ> π /4, θ= θ - π /2
22
*1
4
Re4tan
4
1
HVVVHH
HVVVHH
SSS
SSS
]][][[][ Tnew UTUT
2cos2sin0
2sin2cos0
001
U
Ref: Lee, J. S., Shuler, D. & Ainsworth, T. (2000). Polarimetric SAR Data Compensation for Terrain Azimuth Slope Variation, IEEE Transactions on Geoscience and Remote Sensing, Vol. 38(5), pp. 2153-2163.
Circular Polarisation Method
RADAR
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RADAR POLARIMETRY YAMAGUCHI MODEL
• Model-based Decomposition• Satisfy non reflection symmetric case <SHHSHV
*>≠0 and <SHVSVV*>≠0
helixcvolumevdoubledsurfaces TfTfTfTfT
10
10
000
2100
010
002
4000
01
0
000
0
01*
2
2
*
j
jff
ffT cvds
- Processed using POLSARpro, courtesy of ESA -
RADAR
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YAMAGUCHI MODEL
)1(2 ss fP
vv fP )1(2 dd fP
Volume Scattering (Pv) Double Bounce Scattering (Pd)Surface Scattering (Ps)
HVch fP
Helix Scattering (Ph)Google aerial photograph
RADAR
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Results
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Biomass
Regression Analysis
Biomass fieldwork
Pv/ Ps
BIOMASS REGRESSION ALGORITHM
R2 =0.74
AGB
S
V eP
P 0104.015363.03604.0
Yam
aguc
hi V
ol/O
dd
AGB: Above Ground Biomass
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BIOMASS ESTIMATION: DENSE FOREST
Aerial Photograph Canopy Cover derived from LiDAR Biomass derived from LiDAR
DEM Height derived from LiDAR Biomass derived from ALOS PALSAR
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BIOMASS ESTIMATION: SPARSE AREA
Aerial Photograph Canopy Cover derived from LiDAR Biomass derived from LiDAR
Height derived from LiDAR Biomass derived from ALOS PALSARDEM
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BIOMASS ESTIMATION: SHADOW EFFECT
Aerial Photograph Canopy Cover derived from LiDAR Biomass derived from LiDAR
Height derived from LiDAR Biomass derived from ALOS PALSARDEM
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TerraSAR-X ALOS PALSAR
R2 =0.74
Regression Analysis : TerraSAR-X
AGB
S
V eP
P 0104.015363.03604.0 AGB
S
V eP
P 0158.012605.04498.0
R2 =0.56
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TerraSAR-X ALOS PALSAR
10 20 30 40 50
5
10
15
20
25
30
35
40
45
50 0
50
100
150
•Data acquisition: 13 April 2010 08 June 2009
BIOMASS ESTIMATION
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CONCLUSION• ALOS PALSAR has the potential for biomass
estimation over wide coverage area but it is limited to biomass less that 150t/ha
• TerraSAR-X can be used to detect the existence of vegetation.
• LiDAR is able to estimate biomass at stand level over small coverage footprint
• It can be extended to be incorporated into the existing measurement models to estimate the biomass and to compare it with other remote sensing data.
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HUNGRY
FUN !
SLOPY
Rain!
GHOST?
SPIKY No
Signal
THANK YOU QUESTIONS?