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Biodiversity and Ecosystem Informatics - BDEI - Spatio-temporal Models of Biogeophysical Fields for Ecological Forecasting: A Cross-Disciplinary Incubation Activity

Biodiversity and Ecosystem Informatics - BDEI - Spatio-temporal Models of Biogeophysical Fields for Ecological Forecasting: A Cross-Disciplinary Incubation Activity
生物多样性和生态系统信息学 - BDEI - 用于生态预测的生物地球物理场时空模型:跨学科孵化活动
批准号:
0131937
负责人:
Geoffrey Henebry
金额:
$10.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2001
资助国家:
美国
项目状态:
已结题
起止时间:
2001-09-01 至 2004-02-29

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EIA-0131937Henebry, GeoffreyUniversity of Nebraska - LincolnBDEI: Spatio-temporal models of Biogeophysical Fields for Ecological Forecasting:A Cross-Disciplinary Incubation Activity SummaryWe are now in an era of intensive earth observation: orbital platforms generate myriad remote sensingdatastreams across a range of spatial, temporal, spectral, and radiometric resolutions. The number andvariety of "eyes in the skies" are scheduled to increase significantly over the next few years. Thisveritable data deluge necessitates new ways of thinking about transforming remote sensing data intoinformation about ecological patterns and processes. These datastreams hold the promise forenvironmental decision support. Yet, there is a critical need for theories and tools that will enable efficientand reliable characterization of spatio-temporal patterns contained in image time series. We think thatsuch tools must be based on ecological expectations of land surface dynamics, analogous toclimatological expectations. Ecological expectations would summarize across specific regions the typicaltemporal development of spatial pattern in biogeophysical fields. We have a robust principal method forextracting ecological expectations from remote sensing datastreams: projecting image time series intopattern metric spaces. To make ecological forecasting an operational possibility, we need the capabilityto establish and to update complex spatio-temporal baselines that will enable prediction of the usual andidentification, quantification, and assessment of the unusual. A recent NASA workshop on Earth Sciencedata mining identified anomaly detection as a key characteristic of scientific data mining; yet, there arerelatively 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-temporalentities and fields in databases must support sophisticated spatio-temporal queries: a capability that doesnot currently exist.
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Cross-Site: Spatio-temporal Dynamics of Canopy and Soil Moisture: Linking Synthetic Aperture Radar Image Phenomenology with Ecosystem Processes
  • 批准号:
    0196445
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.73万
  • 财政年份:
    2000
  • 负责人:
    Geoffrey Henebry
  • 依托单位:
Cross-Site: Spatio-temporal Dynamics of Canopy and Soil Moisture: Linking Synthetic Aperture Radar Image Phenomenology with Ecosystem Processes
  • 批准号:
    9696229
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.73万
  • 财政年份:
    1996
  • 负责人:
    Geoffrey Henebry
  • 依托单位:
海外基金