Wetland hydrology is highly variable across Ohio’s landscape due to differences in water source and connection. This variability in hydrologic regime controls the function of how nutrients are stored, released, and moved throughout wetlands. Knowledge of these functions allows for better management of wetlands and can provide greater insight for future restoration projects. Previous databases, such as the National Wetlands Inventory, have achieved broad qualitative classifications of wetlands, but quantitative methods are minimally used to classify wetlands by their hydrologic regime. A method that uses open source remote sensing, such as Synthetic Aperture Radar, geologic, and hydrologic data to quantify wetland hydrologic regimes will help to fill the knowledge gap of how these regimes impact nutrient retention in wetlands. We created a supervised classification model in Google Earth Engine that identifies and classifies wetlands across Ohio by their hydrologic regime. By incorporating quantitative methods in this model we wish to address if 1) remote sensing is able to quantify large and small scale differences in wetland function and 2) how wetland classification can be streamlined using a model that incorporates remote sensing data. Using remote sensing to understand wetland hydrologic regime may allow wetland managers and restoration officials to increase wetland nutrient retention capabilities and could save them time and money in the classification process.
