PGeostat LLC, Jerome, MI 49249, USA.
Abt Associates, Boulder, CO 80302, USA.
J Environ Manage. 2016 Sep 15;180:264-71. doi: 10.1016/j.jenvman.2016.05.041. Epub 2016 Jun 1.
Stranded oil covering soil and plant stems in fragile Louisiana marshes was one of the most visible impacts of the 2010 Deepwater Horizon (DWH) oil spill. As part of the assessment of marsh injury after the DWH spill, plant stem oiling was broken into five categories (0%, 0-10%, 10-50%, 50-90%, 90-100%) and used as the independent variable for estimating death of vegetation, accelerated erosion, and other metrics of injury. The length of shoreline falling into each of these stem oiling categories was therefore a key measure of the total extent of marsh injury, and its accurate estimation is the focus of this paper. First, we used geographically-weighted logistic regression (GWR) to explore and model spatially varying relationships between stem oiling field data and secondary information (oiling exposure category) collected during shoreline surveys. We then combined GWR probability estimates with field data using indicator cokriging to predict the probability of exceeding four stem oiling thresholds (0, 10, 50, and 90%) at 50 m intervals along the Louisiana shoreline. Cross-validation using Receiver Operating Characteristic (ROC) Curves demonstrate the greater prediction accuracy of the multivariate geostatistical approach relative to either aspatial regression or indicator kriging that ignores secondary information.
搁浅在路易斯安那脆弱湿地的土壤和植物茎上的油污是 2010 年深海地平线（DWH）石油泄漏最明显的影响之一。作为 DWH 泄漏后对湿地受损评估的一部分，植物茎油污被分为五个类别（0%、0-10%、10-50%、50-90%、90-100%），并用作估计植被死亡、加速侵蚀和其他损伤指标的自变量。因此，属于这些油污类别的海岸线长度是衡量湿地总受损程度的关键指标，准确估计该长度是本文的重点。首先，我们使用地理加权逻辑回归（GWR）来探索和模拟茎油污实地数据与在海岸线调查中收集的次要信息（油污暴露类别）之间的空间变化关系。然后，我们使用指示协克里金法将 GWR 概率估计值与实地数据相结合，以预测在路易斯安那州海岸线每 50 米间隔处超过四个油污阈值（0、10、50 和 90%）的概率。使用接收者操作特征（ROC）曲线进行的交叉验证表明，与忽略次要信息的非空间回归或指示克里金相比，多变量地质统计方法具有更高的预测准确性。
