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The Lower Mississippi River Delta (MRD) is dominated by Phragmites australis which provides a stabilizing force, protecting marsh communities from erosion and storm-related impacts. The MRD has experienced recent die-offs of Phragmites stands, which have coincided with a number of abiotic (hurricane and water-level) and biotic (Phragmites scale) stressors. During this event, previously healthy stands have died or experienced stunted growth, resulting in conversion to replacement species or to open water. This study utilized remote sensing methods to (1) evaluate changes in Phragmites health and distribution in the MRD through time; (2) assess changes in plant cover and floristic quality before, during, and after the dieback event; (3) evaluate changes in landscape patterns (i.e., percentage of landscape, patch density, total edge, and aggregation index); and (4) evaluate changes in channel length and width as a function of the dieback event. Normalized Difference Vegetation Index data showed an active delta landscape with below typical plant biomass/health in periods of hurricane and dieback impacts, with recovery to health values more typical for the MRD after the dieback event. Vegetation species level assessments showed the MRD landscape experienced decreasing coverage and floristic quality during the disturbance periods, and low and nominally increasing vegetation cover and quality through the dieback and recovery periods. Landscape metrics showed similar trends, where wetland areas that experienced event-related stress during the hurricane and dieback periods, showed some level of rebound during the recovery period. And finally, bank-line change analyses showed a significant increase in the rate of shortening and widening of the Mississippi River Passes since the die-back event began. This study serves as a methodological basis for deriving vegetation trend assessments and integrating those results with landscape metrics to prioritize areas of interest; all of which are essential for effective management and mitigation of aquatic nuisance vegetation.

Landscape models are essential tools that link landscape patterns to ecological processes. Barrier island vegetation communities are strongly correlated with geomorphology, which makes elevation-based metrics suitable for developing a predictive habitat classification model in these systems. In this study, multinomial logistic regression is used to predict herbaceous, sparse, and woody habitat distributions on the North End of Assateague Island from slope, distance to shore, and elevation change, all of which are derived from digital elevation models (DEMs). Sparse habitats, which were generally found closest to shore in areas that are exposed to harsh conditions, had the highest predictive accuracy. Herbaceous and woody habitats occupied more protected inland settings and had lower predictive accuracies. A majority of woody cells were misclassified as herbaceous likely because of the similarity in the predictive parameter distributions. This relatively simple model is transparent and does not rely on subjective interpretations. This makes it an effective tool that can directly aid practitioners making coastal management decisions surrounding storm response planning and conservation management. The model results were used in a nutrient sequestration application to quantify carbon and nitrogen stored in barrier island vegetation. This represents an example of how the model results can be used to assign economic value of ecosystem services in a coastal system to justify different management and conservation initiatives.