SEE THE FORESTS WITH DIFFERENT EYES CHUE POH TAN ARMANDO MARINO IAIN WOODHOUSE SHANE CLOUDE JUAN SUAREZ COLIN EDWARDS A contribution from radar polarimetry
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
L-band (1.27 GHz)
Incidence Angle 23 o
17 April 2007 & 08 June 2009
X-band (9.6 GHz)
Incidence Angle 43 o
13 April 2010
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.
REGRESSION ANALYSIS – HV
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)
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)), R 2 =0.36
STUDY AREA Aerial Photograph DEM 1500m 1500m Area Google Earth
LIDAR DATA PROCESSING CSM Canopy Height Model CHM Canopy Surface Model LIDAR
LIDAR DATA PROCESSING Height derived from LiDAR Biomass LiDAR Canopy Cover derived from LiDAR LIDAR
Polarisation : Scatterers have different polarisation behaviour M-POL /35 Radar Polarimetric Observations Cylinder: Anisotropic Ground: No depolarisation Sphere: isotropic + … Cylinder: Anisotropic Ground: No depolarisation Sphere: isotropic + … RADAR
RADAR POLARIMETRY AZIMUTH SLOPE REMOVAL S RR =(S HH -S VV + i S HV )/2 S LL =(S VV -S HH + i S HV )/2 θ =[Arg(< S RR S LL * >) + π ]/4] For θ > π /4, θ = θ - π /2 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
Satisfy non reflection symmetric case <S HH S HV * >≠0 and <S HV S VV * >≠0
- Processed using POLSARpro, courtesy of ESA - RADAR
YAMAGUCHI MODEL Volume Scattering (P v ) Double Bounce Scattering (P d ) Surface Scattering (P s ) HV Helix Scattering (P h ) Google aerial photograph RADAR
Biomass Regression Analysis Biomass fieldwork P v / P s BIOMASS REGRESSION ALGORITHM R 2 =0.74 Yamaguchi Vol/Odd 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 PALSAR DEM
BIOMASS ESTIMATION: SHADOW EFFECT Aerial Photograph Canopy Cover derived from LiDAR Biomass derived from LiDAR Height derived from LiDAR Biomass derived from ALOS PALSAR DEM
Regression Analysis : TerraSAR-X TerraSAR-X ALOS PALSAR R 2 =0.74 R 2 =0.56
BIOMASS ESTIMATION TerraSAR-X ALOS PALSAR
Data acquisition: 13 April 2010 08 June 2009
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?