API Governance and Monetization - The evolution of API governance
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
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?
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
----- Meeting Notes (7/14/11 02:27) -----Last but not least...
Principle: - using several flights, builds an additional synthetic aperture - provides resolution in cross-range/elevation
