Mike Dietze project receives funding from NASA
Department of Earth and Environment Assistant Professor Mike Dietze‘s project, “Assimilation of imaging spectroscopy data to improve the representation of vegetation dynamics in ecosystem models,” was recently awarded funding by NASA.
The project will span three years and will be the product of the collaboration of Dietze and two other Principle Investigators, Shawn Serbin and Phil Townsend. Serbin (a former Dietze Lab post-doc), Townsend, and projector collaborator Ankur Desai are based at the University of Wisconsin. Tristan Quaife at the University of Reading in the United Kingdom will also collaborate on the project.
For more information about currently funded research by Asst. Prof. Dietze and other members of the Department of Earth and Environment see the grants section of our website.
A summary of the project is as follows:
The ability to seamlessly integrate information on forest function across a continuum of scales, from field to satellite observations, greatly enhances our ability to understand how terrestrial vegetation-atmosphere interactions change over time and in response to anthropogenic and natural disturbances. This project focuses on the use of field and high-spectral resolution remote sensing observations (i.e. imaging spectroscopy, IS), within an efficient model-data assimilation framework, to improve the characterization of vegetation dynamics in terrestrial ecosystem models. This effort comes at a crucial time because the experimental, remote sensing, and modeling communities have entered into an increasingly data-rich era; however the tools needed to make use of the numerous but disparate data for model improvements are currently lacking. For example, remote sensing can provide detailed spatial and temporal information on a number of important biophysical and biochemical properties of ecosystems. State-of-the-art dynamic vegetation ecosystem models, such as Ecosystem Demography (ED2.2) model (Medvigy et al., 2009), a physiologically-based forest community model, can potentially use this information to improve model representation of vegetation dynamics. ED2 is especially relevant to these efforts because it contains a sophisticated structure for scaling ecological processes across a range of spatial scales: from tree- level physiology to stand demography to landscape heterogeneity to regional carbon, water, and energy fluxes, which allows for the direct use of remotely sensed data at the appropriate spatial scale. The project leverages extensive field and imaging spectroscopy (IS) data that have been collected by Co-PI’s Shawn Serbin and Phil Townsend within the upper Midwest, US, directly within an ecosystem modeling framework. We are working to utilize a radiative transfer modeling (RTM) module being developed by Serbin and Dietze for use with the ED2 model and Predictive Ecosystem Analyzer (PEcAn, LeBauer et al., 2013) workflow system (www.pecanproject.org) to enable efficient assimilation of spectral reflectance observations from imaging spectroscopy data (and eventually any optical remote sensing observations, such as Landsat and MODIS/VIIRS). Through this open-source workflow system we will facilitate direct assimilation of spectral observations rather than derived products. This will improve the models parameterization of canopy optical properties and the surface energy balance. Through state-variable data assimilation we will fuse AVIRIS, flux towers, forest inventories, and model projections to reconcile estimates of vegetation composition and carbon pools and fluxes. The resulting data product will be analyzed to better understand the drivers of spatial and temporal variability in the carbon cycle and the sources of uncertainty in these estimates. This project would be an important step toward the operational capacity to assimilate reflectance observations, uniformly, within sophisticated ecosystem models with the goal to accurately constraining model projections of carbon pools and fluxes of terrestrial ecosystems.