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Model Inversion of Multiple-Sensor Data for Forest Biophysical Parameters Retrieval
Project Funding: 2006 - 2009
NRA: 2005 NASA: Remote Sensing Science for Carbon and Climate
Funded by NASA
Abstract:The multi-spectral data provided by imaging spectrometers, backscattering coefficient data from imaging radar, and the waveform from large-footprint laser altimeter (or lidar) are now available from space for vegetation studies. Data from these sensors contain information relevant to different aspects of the biochemical and biophysical properties of the vegetation canopy. A simultaneous exploitation of the information dimensions observed by these sensors based on radiative transfer modeling will therefore provide a new approach to optimize the retrieval of forest foliage biochemical composition and the canopy structure. The proposed research relies on a radiative transfer model of imaging spectrometer data (GeoSail), a 3D radar backscatter model and a lidar waveform model based on the same 3D canopy structure. Both the GeoSail and lidar waveform models have already been employed and validated to retrieve forest properties from Imaging Spectrometer and lidar data separately. The radar backscatter model, combined with forest growth model (ZELIG) and neural network, has been used in the forest biomass retrieval and prediction. The realistic forest stands from the forest growth model (ZELIG ) simulations will be used as inputs for the generation of a Look Up Table (LUT) consisting of the simulated signatures of the Imaging Spectrometer, radar and lidar as a function of a common forest stand parameterization. The model-based algorithms avoid the limitation on the number and accuracy of the field samples, and the misregistration of field data with the remote sensing observations, which happen in developing sample-based empirical algorithms. The forest canopy parameters to be considered include tree height, stem density, crown length, fractional crown cover, LAI, above-ground biomass, forest type (IGBP). The remote sensing data to be simulated include Landsat-7 ETM+, L-band polarimetric radar (ALOS PALSAR), and LVIS. The multi-angle data (AirMISR) and hyperspectral data (Hyperion on EO1) will also be considered. The issue of scaling up from high-resolution (~30m) to low-resolution (1km) using MODIS, HYDROSAT, GLAS data will also be investigated in the proposed studies. Specifically, we will: 1) simulate forest stands (30m x 30m) over a range of ages at our test sites using forest growth model ZELIG; 2) establish interface and common inputs to optical, radar and lidar waveform models; 3) generate LUT with various stand parameters and simulated optical, radar, and lidar waveform data; 4) investigate invertability, and accuracy of the inversion for various forest parameters using one sensor along, and various combination of these three sensors; 5) evaluate the inversion technology using high-resolution optical, radar and lidar data at test sites; 6) exploit the scale effect on the inversion by aggregating LUT from 30m to 1km resolution. This study will give insight on the capabilities of these sensors for the measurement of various forest parameters, and the benefit of combined use of multiple sensors, and help to provide a basic physical or biophysical foundation to prepare for new observations relevant to Carbon Cycle and Ecosystems Roadmap programmatic elements: vegetation 3-D structure, biomass, and disturbance.
2011 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
2010 NASA Terrestrial Ecology Science Team Meeting Poster(s)
2008 NASA Carbon Cycle & Ecosystems Joint Science Workshop Posters
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