Spatio-Temporal Statistics and its Application to Remote Sensing

时空统计及其在遥感中的应用

• 批准号: DP150104576
• 负责人: Prof Noel Cressie
• 金额: $ 27.76万
• 依托单位: University of Wollongong
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-07-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP150104576
• 关键词: Spatio; Temporal; Statistics; its; Application

By their very nature, environmental processes involve strong spatial and temporal variability. Inferring cause-effect relationships requires the incorporation of spatial and temporal dependence in the statistical models. The aims of this project are to develop mass-balanced hierarchical spatio-temporal statistical models, new loss functions that are relevant to multivariate processes, and optimal estimators obtained from the hierarchical model's predictive distribution. These methodologies are intended to be applied to the estimation of near-surface fluxes of atmospheric carbon dioxide, using massive remote sensing datasets from satellites and other data sources.

就其本质而言，环境过程涉及强烈的空间和时间变异性。推断因果关系需要在统计模型中纳入空间和时间依赖性。这个项目的目的是开发质量平衡的分层时空统计模型，新的损失函数，是相关的多变量过程，和最佳估计从分层模型的预测分布。这些方法拟用于利用卫星和其他数据来源提供的大量遥感数据集估算大气层二氧化碳的近地表通量。