ChuePoh_IGARSS2011.ppt

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  • 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.
  • ChuePoh_IGARSS2011.ppt

    1. 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
    2. 2. Overview
    3. 3. OUTLINE <ul><li>Combination of sensors (RADAR + LIDAR) may be a powerful solution </li></ul><ul><li>This work will treat meanly the RADAR part of the problem </li></ul><ul><li>Backscatter-biomass regression is problematic on mountainous terrain and sparse woodlands </li></ul><ul><li>Polarimetry can be our SAVIOUR ! </li></ul>
    4. 4. STUDY AREA – GLEN AFFRIC
    5. 5. STUDY AREA – GLEN AFFRIC
    6. 6. FIELDWORK
    7. 7. FIELDWORK
    8. 8. ALOS PALSAR <ul><li>L-band (1.27 GHz) </li></ul><ul><li>Quad-pol data </li></ul><ul><li>Ascending mode </li></ul><ul><li>Incidence Angle 23 o </li></ul><ul><li>Data acquisition </li></ul><ul><li>17 April 2007 & 08 June 2009 </li></ul>ALOS PALSAR
    9. 9. <ul><li>X-band (9.6 GHz) </li></ul><ul><li>Quad-pol data </li></ul><ul><li>Ascending mode </li></ul><ul><li>Incidence Angle 43 o </li></ul><ul><li>Data acquisition </li></ul><ul><li>13 April 2010 </li></ul>TerraSAR-X
    10. 10. <ul><li>Ability to capture high resolution images quickly and relatively independent of lighting conditions </li></ul><ul><li>Ability to collect 20000 to 75000 records per second </li></ul><ul><li>High vertical accuracy with 15-20cm RMS and horizontal accuracy with 20-30cm RMS </li></ul><ul><li>Data acquisition 9 June 2007 </li></ul>LiDAR: Optech ALTM 3100 LiDAR system
    11. 11. PREVIOUS WORK <ul><li>Many studies on forest volume/ biomass estimation in flat areas </li></ul><ul><li>(Kurvonen et al., 1999; Saatchi et al., 2001; Austin et al., 2003) </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul>
    12. 12. REGRESSION ANALYSIS – HV <ul><li>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) </li></ul><ul><li>Studies have been done on biomass estimation using backscatter-biomass relationship (Kasischke et al., 1995; Ranson et al.,1996; Mitchard et. al) </li></ul><ul><li>Figure Reference: http://www.eci.ox.ac.uk/research/biodiversity/linkcarbon.php </li></ul><ul><li>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 . ) </li></ul>
    13. 13. REGRESSION ANALYSIS – HV HV=-14.2151+1.8251*(1-exp(0.0488*AGB)), R 2 =0.36
    14. 14. Methodology
    15. 15. METHODOLOGY
    16. 16. STUDY AREA Aerial Photograph DEM 1500m 1500m Area Google Earth
    17. 17. LIDAR DATA PROCESSING CSM Canopy Height Model CHM Canopy Surface Model LIDAR
    18. 18. LIDAR DATA PROCESSING Height derived from LiDAR Biomass LiDAR Canopy Cover derived from LiDAR LIDAR
    19. 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. 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. 21. RADAR POLARIMETRY YAMAGUCHI MODEL <ul><li>Model-based Decomposition </li></ul><ul><li>Satisfy non reflection symmetric case <S HH S HV * >≠0 and <S HV S VV * >≠0 </li></ul>- Processed using POLSARpro, courtesy of ESA - RADAR
    22. 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
    23. 23. Results
    24. 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. 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. 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. 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
    28. 28. Regression Analysis : TerraSAR-X TerraSAR-X ALOS PALSAR R 2 =0.74 R 2 =0.56
    29. 29. BIOMASS ESTIMATION TerraSAR-X ALOS PALSAR <ul><li>Data acquisition: 13 April 2010 08 June 2009 </li></ul>
    30. 30. CONCLUSION <ul><li>ALOS PALSAR has the potential for biomass estimation over wide coverage area but it is limited to biomass less that 150t/ha </li></ul><ul><li>TerraSAR-X can be used to detect the existence of vegetation. </li></ul><ul><li>LiDAR is able to estimate biomass at stand level over small coverage footprint </li></ul><ul><li>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. </li></ul>
    31. 31. HUNGRY FUN ! SLOPY Rain! GHOST? SPIKY No Signal THANK YOU QUESTIONS?

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