1. SEE THE FORESTS WITH DIFFERENT EYES CHUE POH TAN ARMANDO MARINO IAIN WOODHOUSE SHANE CLOUDE JUAN SUAREZ COLIN EDWARDS A contribution from radar polarimetry
18. LIDAR DATA PROCESSING Height derived from LiDAR Biomass LiDAR Canopy Cover derived from LiDAR LIDAR
19. 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
20. 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
21.
22. YAMAGUCHI MODEL Volume Scattering (P v ) Double Bounce Scattering (P d ) Surface Scattering (P s ) HV Helix Scattering (P h ) Google aerial photograph RADAR
24. Biomass Regression Analysis Biomass fieldwork P v / P s BIOMASS REGRESSION ALGORITHM R 2 =0.74 Yamaguchi Vol/Odd AGB: Above Ground Biomass
25. 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
26. 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
27. 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
31. HUNGRY FUN ! SLOPY Rain! GHOST? SPIKY No Signal THANK YOU QUESTIONS?
Editor's Notes
Overall, the biomass are broadly comparable. In forests here, the volume contribution is overestimated when the number density is high, but the total biomass is low. This is also possibly caused by the sloping terrain where the slopes are not removed radiometrically. In this case, the forest returns high volume scattering power, thus giving high ratio of volume and surface scattering values.
In sparse forest, the above ground biomass is underestimated. This is because of the regression that has bias due to the volume scattering contributed by heather/ moorland.
This part of the biomass cannot be estimated because of the shadow due to geometric effects. The amount of missing coverage is dependent on the range of distribution of relief within the study area.