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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
Overview
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 !
STUDY AREA – GLEN AFFRIC
STUDY AREA – GLEN AFFRIC
FIELDWORK
FIELDWORK
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
• X-band (9.6 GHz)
• Quad-pol data
• Ascending mode
• Incidence Angle 43o
• Data acquisition 13 April 2010
TerraSAR-X
•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
• 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
•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
HV=-14.2151+1.8251*(1-exp(0.0488*AGB)), R2=0.36
REGRESSION ANALYSIS – HV
Methodology
METHODOLOGY
STUDY AREA
Aerial Photograph DEM
1500m
1500
mArea
Google Earth
LIDAR DATA PROCESSING
CSM
Canopy
Heig
ht M
odel
CHM
Canopy
Surfa
ce M
odel
LIDAR
LIDAR DATA PROCESSING
Height derived from LiDAR
Biomass LiDAR
Canopy Cover derived from LiDAR
LIDAR
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
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
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
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
Results
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
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
BIOMASS ESTIMATION: SPARSE AREA
Aerial Photograph Canopy Cover derived from LiDAR Biomass derived from LiDAR
Height derived from LiDAR Biomass derived from ALOS PALSARDEM
BIOMASS ESTIMATION: SHADOW EFFECT
Aerial Photograph Canopy Cover derived from LiDAR Biomass derived from LiDAR
Height derived from LiDAR Biomass derived from ALOS PALSARDEM
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
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
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.
HUNGRY
FUN !
SLOPY
Rain!
GHOST?
SPIKY No
Signal
THANK YOU QUESTIONS?