Accuracy of DESDynI Biomass Estimates using Lidar and Data Fusion Methods
Bruce
D
Cook, NASA, bruce.cook@nasa.gov
(Presenting)
Guoqing
Sun, Univ. of MD, guoqing.sun-1@nasa.gov
Kenneth
J
Ranson, NASA, kenneth.j.ranson@nasa.gov
Paul
M
Montesano, Sigma Space, paul.m.montesano@nasa.gov
Scott
B
Luthcke, NASA, scott.b.luthcke@nasa.gov
J
Bryan
Blair, NASA, james.b.blair@nasa.gov
DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) is a NASA satellite mission that will provide global estimates of aboveground biomass using LiDAR (Light Detection and Ranging) and L-band radar. LiDAR waveforms and radar backscatter coefficients at different wave polarizations are sensitive to forest height, structure, and composition, and can be used to make quantitative estimates of standing biomass and carbon stocks. Accuracy requirements for the DESDynI biomass product are 20 Mg ha-1 or 20% (errors not to exceed 50 Mg ha-1), at a spatial resolution of 250 m globally at end of mission, and 100 m for areas of low biomass annually (< 100 Mg ha-1). A NASA field campaign was conducted in New England, USA, during 2009 to quantify sources of errors associated with biomass estimates. Coincident data from DESDynI airborne simulators (Laser Vegetation Imaging Sensor, LVIS; Uninhabited Aerial Vehicle Synthetic Aperture Radar, UAVSAR) and ground-based forest inventory measurements provided data needed to quantify model uncertainty and measurement errors. To compute sampling errors, DESDynI orbits and cloud cover was simulated and used to subsample wall-to-wall LVIS data. Model uncertainty and measurement errors for LiDAR-derived biomass were less than radar, but the gridded estimates of LiDAR biomass also included a sampling error that was greater than measurement errors. Radar estimates are important for filling gaps in LiDAR sampling, and a “fused” data product will have greater accuracy, primarily in areas of low biomass.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
Ranson, Jon
Amount, Spatial Distribution, and Statistical Uncertainty of Aboveground Carbon Stocks in the Circumpolar Boreal Forest
Sun, Guoqing
Data Fusion Algorithms for Forest Biomass Mapping From Lidar and SAR Data