Forrest Hoffman, Oak Ridge National Laboratory
Forrest Hoffman
Computational Earth System Scientist
Oak Ridge National Laboratory

Forrest M. Hoffman is a Distinguished Computational Earth System Scientist and the Group Leader for the Computational Earth Sciences Group at Oak Ridge National Laboratory (ORNL). As a resident researcher in ORNL’s Climate Change Science Institute (CCSI) and a member of ORNL’s Computational Sciences & Engineering Division (CSED), Forrest develops and applies Earth system models (ESMs) to investigate the global carbon cycle and feedbacks between biogeochemical cycles and the climate system. He applies data mining methods using high performance computing to problems in landscape ecology, ecosystem modeling, remote sensing, and large-scale climate data analytics. He is particularly interested in applying machine learning methods to explore the influence of terrestrial and marine ecosystems on hydrology and climate. Forrest is also a Joint Faculty Member in the University of Tennessee’s Department of Civil & Environmental Engineering in nearby Knoxville, Tennessee.

Abstract: Prospects for Satellite Remote Sensing to Identify Evolving Anthromes and Quantify Carbon Cycle Dynamics

F. M. HOFFMAN , J. KUMAR, Z. L. LANGFORD, V. S. KONDURI, R. LIMBER, W. W. HARGROVE

Computational Earth Sciences Group, Oak Ridge National Laboratory, Building 4500N Room F106, Oak Ridge, TN 37831-6301, USA


Land ecosystems represent a large and growing natural sink of carbon attributable primarily to CO2-fertilization in response to increasing anthropogenic carbon being emitted into the atmosphere. This sink is mediated by the accompanying effects of increasing temperature, land use and land cover change, and changes in the frequency, intensity, and extent of extreme events. Satellite remote sensing offers the ability to estimate ecosystem carbon state and dynamics at a range of spatial and temporal scales. Vegetation phenoregions, geographic areas that exhibit similar seasonal phenology, represent a useful metric for distinguishing the evolving carbon dynamics of plant communities. Derived from remote sensing indices of greenness, annual phenological curves provide a representation of gross primary production that incorporate responses to climate variation, effects of urban heat islands and irrigation, human land management, disturbance, shifting ecological communities, and changing atmospheric CO2 concentration. The spatial areas of phenoregions vary interannually, and phenoregions shift across landscapes as climate change occurs. Thus, phenoregions can be considered one form of anthromes that directly incorporates carbon dynamics. When combined with ancillary meteorological and related data, the concept of phenoregions can be extended to encompass additional properties considered important for altering the carbon sink potential of terrestrial ecosystems.