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Biodiversity and Ecosystem Informatics - BDEI - Spatio-temporal Models of Biogeophysical Fields for Ecological Forecasting: A Cross-Disciplinary Incubation Activity      (Back to Search Results)


Grant Number: 131937

  • Description: Standard Grant
  • Associated Project:
  • Award Date: 2001-09-10
  • Award Period: 2001-09-01 to 2004-02-29
  • Amount: $ 100000.00

Primary Investigator:
Geoffrey Henebry

Researchers
Geoffrey Henebry
Jan Chomicki
Tony Fountain

Technology:
Biodiversity
Modeling & Simulation

Government Domain:
Natural Resource Management

Primary Institution:
U of Nebraska-Lincoln

Project Home Page:
http://www.calmit.unl.edu/BDEI/

Latest Project Highlight:
None

Abstract:
EIA-0131937 Henebry, Geoffrey University of Nebraska - Lincoln BDEI: Spatio-temporal models of Biogeophysical Fields for Ecological Forecasting: A Cross-Disciplinary Incubation Activity Summary We are now in an era of intensive earth observation: orbital platforms generate myriad remote sensing datastreams across a range of spatial, temporal, spectral, and radiometric resolutions. The number and variety of "eyes in the skies" are scheduled to increase significantly over the next few years. This veritable data deluge necessitates new ways of thinking about transforming remote sensing data into information about ecological patterns and processes. These datastreams hold the promise for environmental decision support. Yet, there is a critical need for theories and tools that will enable efficient and reliable characterization of spatio-temporal patterns contained in image time series. We think that such tools must be based on ecological expectations of land surface dynamics, analogous to climatological expectations. Ecological expectations would summarize across specific regions the typical temporal development of spatial pattern in biogeophysical fields. We have a robust principal method for extracting ecological expectations from remote sensing datastreams: projecting image time series into pattern metric spaces. To make ecological forecasting an operational possibility, we need the capability to establish and to update complex spatio-temporal baselines that will enable prediction of the usual and identification, quantification, and assessment of the unusual. A recent NASA workshop on Earth Science data mining identified anomaly detection as a key characteristic of scientific data mining; yet, there are relatively few examples of spatio-temporal data mining of biogeophysical data. Our approach is spatio-temporal datamining that is informed by relevant domain expertise. Representation of the spatio-temporal entities and fields in databases must support sophisticated spatio-temporal queries: a capability that does not currently exist.