An integrated modeling approach for monitoring and predicting common reed (Phragmites australis) colonization in a managed South Carolina estuary

Recently, research efforts have focused on the development and testing of geographic information processing (GIP) techniques to more effectively identify and monitor invasive plant species in coastal ecosystems. While these efforts are progressing, particularly with refinements in hyperspectral image processing, there remains a lack of practical, science-based tools for decision-making with respect to the monitoring and mitigation of invasive plant species. Therefore, a geographic information systems (GIS) model is proposed to quantify the probability of a future exotic plant invasion in a user-defined area of interest. The primary objective of this investigation is to present conceptual and technical descriptions of a proposed modeling approach for prediction of common reed (Phragmites australis) colonization in a managed coastal wetland environment. Successful validation and adoption of the model will promote continued discourse regarding the impact of invasive species in coastal ecosystems, while providing the management community with a valuable decision support tool. Three main chapters comprise this manuscript; Chapter 1 focuses on the evaluation of remote sensing data and techniques for directly identifying Phragmites australis in a coastal ecosystem; Chapter 2 describes proposed, GIS-based models for predicting Phragmites colonization and future growth projections; and Chapter 3 discusses the results of the compiled relative suitability index (RSI) compilation and growth models, and considers the key management implications of the investigation. In the absence of a universal eradication solution for the invasive species Phragmites australis, an integrated management approach that utilizes a suite of dynamic, geo-spatial techniques in concert with more traditional practices is ultimately recommended.; Keywords. Invasive species, estuary, NERRS, Phragmites, GIS-based models, remote sensing, coastal resource management, geographic information processing.