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

内布拉斯加州大学林肯分校dei：用于生态预测的生物地球物理场的时空模型：一个跨学科的孵化活动摘要我们现在处于一个密集的地球观测时代：轨道平台产生无数的遥感数据流，这些数据流跨越了空间、时间、光谱和辐射分辨率。“天空之眼”的数量和种类预计将在未来几年显著增加。这种真正的数据洪流需要新的思维方式，将遥感数据转化为有关生态模式和过程的信息。这些数据流承诺提供环境决策支持。然而，迫切需要理论和工具来有效和可靠地表征图像时间序列中包含的时空模式。我们认为这些工具必须基于陆地表面动态的生态预期，类似于气候学预期。生态预期将总结生物地球物理领域空间格局的典型时空发展。我们有一种从遥感数据流中提取生态期望的鲁棒主要方法：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一种可行的可能性，我们需要建立和更新复杂的时空基线的能力，从而能够预测通常的情况，并对异常情况进行识别、量化和评估。最近NASA关于地球科学数据挖掘的研讨会将异常检测确定为科学数据挖掘的一个关键特征；然而，生物地球物理数据的时空数据挖掘实例相对较少。我们的方法是基于相关领域专业知识的时空数据挖掘。数据库中时空实体和字段的表示必须支持复杂的时空查询：目前还不存在这种功能。

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.