Random Forest Classification Method for Predicting Intertidal Wetland Migration Under Sea Level Rise

Abstract

Intertidal wetlands such as mangrove and saltmarsh are increasingly susceptible to areal losses related to sea level rise. This exposure is potentially offset by processes that might enable wetlands to accrete in situ or migrate landward under sea level rise, and planning policies that might open new opportunities for migration. We present and demonstrate a method to predict intertidal wetland distribution in the present-day landscape using random forest classification models, and use these models to predict the intertidal wetland distribution in future landscapes under specified sea level scenarios. The method is demonstrably robust in predicting present-day intertidal wetland distribution, with moderate correlation or better between predicted and mapped wetland distributions occurring in nearly all estuaries and strong correlation or better occurring in more than half of the estuaries. Given the accuracy in predicting present-day wetland distribution the method is assumed to be informative in predicting potential future wetland distribution when combined with best available models of future sea level. The classification method uses a variety of hydro-geomorphological surrogates that are derived from digital elevation models, Quaternary geology or soils mapping and land use mapping, which is then constrained by a representation of the future sea level inside estuaries. It is anticipated that the outputs from applying the method would inform assessments of intertidal wetland vulnerability to sea level rise and guide planning for potential wetland migration pathways.