Extrapolation of LIDAR for Forest Structure Estimation Using SAR, IFSAR, and Optical Data
Leland
E
Pierce, Univ of Michigan, lep@umich.edu
(Presenting)
Michael
L
Benson, Univ of Michigan, mlbenson@umich.edu
Kamal
Sarabandi, Univ of Michigan, saraband@umich.edu
Kathleen
M
Bergen, Univ of MIchigan, kbergen@umich.edu
One of the most fundamental new technical challenges of a DESDynI spaceborne mission is the fusion of the several sensor modalities - LiDAR, SAR, InSAR, and Optical - in order to accurately estimate desired Vegetation 3D and biomass parameters at their point of intersection and to extrapolate them over continuous areas.
The objective of this paper is to use both our simulation models and measured dataset to develop and validate fusion and extrapolation methods while simulating DESDynI-type missions.
We use existing datasets to develop and validate our fusion and extrapolation approach, which involves using our four sensor simulators, including our fractal-based tree geometry generator, in tandem with our in-house parameter estimation software which performs both fusion and retrieval functions. We use existing field and radar-lidar-VNIR data for the north and south Boreas sites, as well as simulated data.
Preliminary results using simulated data for uniform conifer forests shows that a straightforward regression can provide for a 20% to 30% improvement in biomass retrievals over areas with only radar and VNIR.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
Sarabandi, Kamal
VEGEX3D: LiDAR-SAR/InSAR Extrapolation and Simulation Models for Retrieving Vegetation 3D Structure and Biomass