1. Compton Tucker, GSFC Sassan Satchi, JPL Jeff Masek, GSFC Rama Nemani, ARC Diane Wickland, HQ Terrestrial Biomass Pilot Product: Estimating Biomass and Carbon Storage by Combining Satellite and Ground Observations

2. Terrestrial Biomass Pilot Project GOAL Estimate aboveground terrestrial vegetation biomass and carbon storage at high spatial resolution for the U.S by combining advanced satellite products with ground observations and evaluate how well these estimates meet the nation’s need for monitoring carbon storage / sequestration.

3. Terrestrial Vegetation Biomass Vegetation biomass density (Mg dry weight per hectare) is used to estimate the amount of carbon stored in vegetation and emitted to the atmosphere when the vegetation is disturbed or harvested Biomass is key to : understanding the global carbon cycle, defining policies in the context of the UNFCCC REDD initiative as a climate mitigation strategy, the mission and measurement objectives of NASA (e.g., DESDynI).

4. Vegetation Aboveground Biomass How to Measure / Estimate? Destructive sampling (in situ) – Harvesting sample trees, drying and weighing them. Non-destructive sampling (in situ) – Sampling measurements such as height and tree trunk diameter that are used in allometric relationships to extrapolate to biomass. Inference from Remote Sensing – Use of passive optical and/or active radar or lidar observations (land cover, leaf area index, height) that either can be related to biomass and carbon storage or can be used to extend point measurements/estimates to large regions Modeling – Process models based on multiple environmental variables, calibrated to account for different vegetation types.

5. Terrestrial Biomass Pilot Project: Methodology LAI/NPP Time since disturbance Tree height Reflectance Data (MODIS LANDSAT) Lidar and Radar Data (GLAS/LVIS SRTM PALSAR) Forest Inventory (FIA) Empirical Methods Biomass

6. Summary of Products and Methods Product RegionBiomassBiomass Change United States (benchmark)  100-250 m resolution  2005-2010 epoch Data: MODIS, PALSAR, GLAS, SRTM, FIA Methods: -Convert GLAS waveforms to vegetation height and biomass; - Use GLAS biomass to train fusion/ecological methods using RS data - Use FIA data for validation & error analysis Data: LEDAPS 5-year disturbance, MODIS, PALSAR, FIA, MTBS fire history, US biomass Methods: - Assess stock change uncertainty using various estimations of forest clearing from MODIS - Use LEDAPS "age" + FIA age/biomass data to calculate carbon accumulation -Use all input data to train on FIA productivity

7. Product RegionBiomassBiomass Change United States (best - 5 subregions)  100-250 m resolution [higher resolution?]  2005-2010 epoch Data: MODIS, PALSAR, SRTM, LVIS, FIA Methods: - use FIA as training target; DESDynI-like input data in fusion methods (PALSAR SRTM) - Assess spatial uncertainty using LVIS data Data: NAFD annual disturbance, best biomass, MTBS fire history, MODIS, PALSAR, LVIS Methods: - Similar to above for clearing/stock losses + PALSAR disturbance maps -Use ED model to generate height/biomass/age curves; compare to observed LVIS data + PALSAR and NAFD disturbance history Summary of Products and Methods, Contd.

8. Terrestrial Biomass Pilot Project Summary Estimate aboveground biomass and carbon storage by combining data from multiple satellite and airborne sensors with ground data. Assess the accuracy of derived estimates by using Forest Inventory and Analysis (FIA) and other high-quality forest carbon/biomass inventory data. Produce a continental U.S. map of above-ground biomass and carbon storage at high spatial resolution, fully mapping errors and uncertainties.