The Deepwater Horizon oil spill, which occurred from April to July of 2010, was the largest spill in U.S. history. Oil washed onto hundreds of kilometers of intertidal marsh shoreline resulting in widespread plant mortality and short-term reductions in ecosystem function. Past incidences of oiling have shown that marsh recovery trajectories can vary greatly over space and time. Accordingly, the long-term negative effects of an oil spill of this magnitude on marsh ecosystems remains largely unknown. This dissertation investigates the effects of oil contamination from the Deepwater Horizon oil spill on community dominant plant species distributions and land loss rates and, simultaneously, demonstrates the value of employing advanced remote sensing and GIS techniques to address landscape-scale ecological disturbances. To examine the response of marsh plant communities to heavy oiling, dominant species in heavily oiled salt marshes, an image classification system was developed to map dominant species. This classification approach utilizes canonical discriminant analysis (CDA), along with a library of field-referenced image endmembers collected from a time series of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images (2010-2012). Land loss rates were calculated using the normalized difference vegetation index (NDVI) applied to a time series (2006-2016) of high resolution (0.30-0.64 m) orthorectified image datasets. Finally, a simple, fetch-limited wind-wave model was integrated into the analysis of shoreline oiling and land loss to examine the interacting effects of wave characteristics and oiling on bay-wide land loss rates. This dissertation’s findings suggest that the most important impact of oiling along marsh boundaries is the acceleration of shoreline retreat and land loss. Further, the results imply that marsh responses to oil contamination are highly variable, and wave action is a significant factor in determining marsh recovery trajectories. Without high wave energy, marsh plant communities show signs of recovery within 3 years of oil contamination. Conversely, oiled shorelines that are exposed to high wave energy can accelerate land loss exponentially. Finally, the results demonstrate the value of advanced remote sensing techniques in examining landscape-scale ecosystem changes that are impractical to assess using traditional, field-based quantitative methods.