The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species.Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques.In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform.To this end, an object-based supervised machine learning (ML) classification workflow is proposed.The proposed method contains two main classification steps.
In the first step, several non-wetland classes TROUSERS URBAN COTTON (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model.In the second step, wetland classes, WATERMELON including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified.Using the proposed method, the GL were classified with an overall accuracy of 93.
6% and a Kappa coefficient of 0.90.Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91.Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.
62% and Kappa coefficient = 0.95).