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SPATIAL  AND  TEMPORAL  FOREST  VARIABILITYIN RADAR DATA Maxim Neumann		JPL, CALTECH SassanS. Saatchi		JPL, CALTECH Laurent Ferro-Famil		University of Rennes 1 Andreas Reigber			German Aerospace Center (DLR) 07/28/2011 Vancouver, IGARSS 2011
OutlineVariability – Change – Dynamics Polarimetry Spatial Variability Spatial correlation length Resolution & forest type dependence Vertical Structure Variability SAR Tomography & Lidar Profiles Temporal Change & Decorrelation Backscatter, Polarimetry, InSAR coherence Temporal & resolution dependence Geometric effects Experimental Results Data JPL’s UAVSAR (Howland, Harvard, La Selva) DLR’s E-SAR (Krycklan Catchment, Remningstorp) ONERA’s SETHI (French Guyana) Small-footprint Lidar (La Selva, Krycklan Catchment) Data by courtesy of JPL, DLR, ONERA, FOI, SLU, CESBIO, ESA. Physics: dielectric and    geometric properties HH  VV  HV Interferometry Elevation and coherence   of scattering center Volumetric and  temporal properties Tomography 3D scene  reconstruction
Spatio-temporal Forest Dynamics Strong dependence on: Layers affected differently: ,[object Object]
Subsurface
Understory
Trunks
Branch layers in dependence of size
Needles/leaves
Incidence angle & Topography
Environmental ConditionsImpacts on radar: ,[object Object]
Polarimetry
InterferometryTime scale important: Temporal processes: ,[object Object]
Days (airborne/space-borne)
Months/years (space-borne/airborne)
Random (wind)
Deterministic (mortality, human and natural disturbances)

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talk_igarss11_st.pptx

  • 1. SPATIAL AND TEMPORAL FOREST VARIABILITYIN RADAR DATA Maxim Neumann JPL, CALTECH SassanS. Saatchi JPL, CALTECH Laurent Ferro-Famil University of Rennes 1 Andreas Reigber German Aerospace Center (DLR) 07/28/2011 Vancouver, IGARSS 2011
  • 2. OutlineVariability – Change – Dynamics Polarimetry Spatial Variability Spatial correlation length Resolution & forest type dependence Vertical Structure Variability SAR Tomography & Lidar Profiles Temporal Change & Decorrelation Backscatter, Polarimetry, InSAR coherence Temporal & resolution dependence Geometric effects Experimental Results Data JPL’s UAVSAR (Howland, Harvard, La Selva) DLR’s E-SAR (Krycklan Catchment, Remningstorp) ONERA’s SETHI (French Guyana) Small-footprint Lidar (La Selva, Krycklan Catchment) Data by courtesy of JPL, DLR, ONERA, FOI, SLU, CESBIO, ESA. Physics: dielectric and geometric properties HH VV HV Interferometry Elevation and coherence of scattering center Volumetric and temporal properties Tomography 3D scene reconstruction
  • 3.
  • 7. Branch layers in dependence of size
  • 9. Incidence angle & Topography
  • 10.
  • 12.
  • 16. Deterministic (mortality, human and natural disturbances)
  • 18.
  • 20.
  • 21.
  • 22. French Guyana tropical forest
  • 23. 2009
  • 26. P-band – 6 tracks! Single-Channel HH+VV HH-VV HV PolInSAR G/V Separation Ground Volume Capon pseudo-backscatter vertical profile: a: steering vector R: interferometric covariance matrix Color-coded: HH+VV, HH-VV, HV
  • 27. Vertical Structure Variability La SelvaTropical Forest – Small-Footprint Lidar Profiles French Guyana Tropical Forest – P-band SAR Tomographic Profiles
  • 28. Temporal Change & DecorrelationExperimental Results TropiSAR Campaign 2009 French Guyana Tropical Forest ONERA – SETHI instrument Temporal baselines: 8x L-band: 0.5–4 hours / 2-8 days 11x P-band: 0.5–4 hours / 2-22 days Biosar 1 Campaign 2007 Swedish Boreal Forest DLR – E-SAR instrument Temporal baselines: 9x L-band: 0.3–4 hours / 1-2 months 9x P-band: 0.3–4 hours / 1-2 months Howland/Harvard 2009 New England Temp/Boreal JPL – UAVSAR instrument Temporal baselines: 3x L-band: 0.5 hours / 6 days L-band L-band P-band L-band P-band
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.

Notas do Editor

  1. I will talk for a change not about biomass and forest height estimation…Before: as much as possible information from Pol + In inside a cell – now, what else is available?
  2. As everybody knows, space-time has 4 dimensions – at least as we can perceive for now.This presentation is mainly experimental data and results driven. Used data… different instruments and forest types.Results: exciting, expected, & unexpected.Curse of dimensionality – some quantitative numbers
  3. ----- Meeting Notes (7/14/11 02:27) -----Last but not least...
  4. Principle: - using several flights, builds an additional synthetic aperture - provides resolution in cross-range/elevation