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%, 0e10%, 10e50%, 50e90%, 90e100%) 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.