Wetlands Mapping with Deep ResU-Net CNN and Open-Access Multisensor and Multitemporal Satellite Data in Alberta’s Parkland and Grassland Region

Abstract

Wetlands are a valuable ecosystem that provides various services to flora and fauna. This study developed and compared deep and shallow learning models for wetland classification across the climatically dynamic landscape of Alberta’s Parkland and Grassland Natural Region. This approach to wetland mapping entailed exploring multi-temporal (combination of spring/summer and fall months over 4 years—2017 to 202) and multisensory (Sentinel 1 and 2 and Advanced Land Observing Satellite, ALOS) data as input in the predictive models. This input image consisted of S1 dual-polarization vertical-horizontal bands, S2 near-infrared and shortwave infrared bands, and ALOS-derived topographic wetness index. The study explored the ResU-Net deep learning (DL) model and two shallow learning models, namely random forest (RF) and support vector machine (SVM). We observed a significant increase in the average F1-score of the ResNet model prediction (0.82) compared to SVM and RF prediction of 0.69 and 0.69, respectively. The SVM and RF models showed a significant occurrence of mixed pixels, particularly marshes and swamps confused for upland classes (such as agricultural land). Overall, it was evident that the ResNet CNN predictions performed better than the SVM and RF models. The outcome of this study demonstrates the potential of the ResNet CNN model and exploiting open-access satellite imagery to generate credible products across large landscapes.