Project Funding: 2014 - 2017

NRA: 2013 NASA: Terrestrial Ecology   

Funded by NASA

Abstract:
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 proposal focuses on the use of field (spectroscopy) 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. Our primary objective is to comprehensively examine the potential for direct assimilation of optical remote sensing observations into sophisticated ecosystem models to better constrain projections of energy balance, vegetation composition, and carbon pools and fluxes. This proposed work would be a novel step toward improving our ability to better diagnose ecosystem vulnerability to environmental change and predict responses to climatic and other perturbations. 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, such as leaf optical properties, leaf chemistry, morphology, vegetation composition and structure. State-of-the-art dynamic vegetation ecosystem models, such as the Ecosystem Demography (ED v2.2) model (Medvigy et al., 2009), a physiologically-based forest community model, can potentially use this information to improve model representation of vegetation dynamics. ED 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 proposed research will leverage within an ecosystem modeling framework extensive existing field and imaging spectroscopy (IS) data that have been collected by Serbin and Townsend in the upper Midwest US, as well as from California as part of the ongoing NASA HyspIRI Airborne Campaign, and other from other sites in the eastern US with extensive data records. We propose 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., 2012) workflow system (www.pecanproject.org) to enable efficient assimilation of spectral reflectance observations from IS data (and eventually any optical remote sensing observations, such as Landsat and MODIS/VIIRS). This open-source workflow system directly ingests 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 (or other IS data), flux towers, forest inventories, and model projections to reconcile estimates of vegetation composition and carbon pools and fluxes. The resulting data product will provide the basis to develop a better understanding of the drivers of spatial and temporal variability in the carbon cycle and the sources of uncertainty in these estimates. This project is 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.

Publications:

Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D., Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K., Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu, X., Zhang, T., Moorcroft, P. R. 2017. Vegetation demographics in Earth System Models: A review of progress and priorities. Global Change Biology. 24(1), 35-54. DOI: 10.1111/gcb.13910

Shiklomanov, A. N., Cowdery, E. M., Bahn, M., Byun, C., Jansen, S., Kramer, K., Minden, V., Niinemets, U., Onoda, Y., Soudzilovskaia, N. A., Dietze, M. C. 2020. Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis. Ecological Applications. 30(3). DOI: 10.1002/eap.2064

Shiklomanov, A. N., Dietze, M. C., Fer, I., Viskari, T., Serbin, S. P. 2021. Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance. Geoscientific Model Development. 14(5), 2603-2633. DOI: 10.5194/gmd-14-2603-2021

2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)

• Next Generation UAV Based Spectral Systems for Environmental Monitoring   --   (Petya Campbell, Philip Townsend, Dan Mandl, Clayton Kingdon, Vuong Ly, Robert Shlberg, Jyoteshwar Nagol, Vincent Ambrosia, Stuart Frye, Lwrence Ong, Lawrence Corp, Pat Cappelaere, Felix Navarro)   [abstract]   [poster]
• Characterization of variability and uncertainty in leaf biophysical traits using spectral data   --   (Alexey N Shiklomanov, Michael Dietze, Shawn Paul Serbin)   [abstract]   [poster]

More details may be found in the following project profile(s):

• NASA Terrestrial Ecology (TE) Project Profile