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: $ 10万
• 依托单位: University of Nebraska-Lincoln
• 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
• 起止时间: 2001-09-01 至 2004-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0131937&HistoricalAwards=false
• 关键词: Biodiversity; Ecosystem; Informatics; BDEI; Spatio

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.

EIA-0131937 Henebry，Geoffrey内布拉斯加大学-林肯分校BDEI：用于生态预测的生物地球物理场的时空模型：跨学科孵化活动摘要我们现在处于密集地球观测的时代：轨道平台产生无数的遥感数据流，跨越空间，时间，光谱和辐射分辨率的范围。在未来几年里，“空中之眼”的数量和种类将显著增加。这种名副其实的数据泛滥需要新的思维方式来将遥感数据转化为有关生态模式和过程的信息。这些数据流为决策支持带来了希望。然而，有一个关键的理论和工具，这将使efficientandreliable表征图像时间序列中包含的时空模式的需求。我们认为，这些工具必须基于对陆地表面动力学的生态预期，类似于气候学预期。生态期望将概括特定区域内地球物理场空间格局的典型时间发展。本文提出了一种从遥感数据流中提取生态期望值的方法：将图像时间序列投影到模式度量空间中。为了使生态预测成为可能，我们需要建立和更新复杂的时空基线的能力，这将使我们能够预测通常的情况，识别，量化和评估不寻常的情况。最近NASA地球科学数据挖掘研讨会确定异常检测作为科学数据挖掘的一个关键特征;然而，地球物理数据的时空数据挖掘的例子相对较少。我们的方法是时空数据挖掘，是由相关领域的专业知识。数据库中的时空实体和字段的表示必须支持复杂的时空查询：目前还不存在这种能力。