AI-Driven Wetland Mapping Across Diverse Natural Regions of Alberta, Canada, Using Combined Airborne and Satellite Remote Sensing Data

This study evaluates the performance of artificial intelligence (AI) technologies for wetland classification in the province of Alberta, Canada, using integrated remote sensing inputs, including airborne light detection and ranging (LiDAR), orthophotography, and multi-sensor satellite imagery (Sentinel-1, Sentinel-2, PlanetScope). Our primary objective was to assess whether AI-driven modelling approaches, specifically machine learning (ML) and deep learning (DL), can meet Alberta’s provincial wetland mapping standards. We hypothesized that integrating high-resolution LiDAR with multi-seasonal optical and radar data composites into advanced AI algorithms would achieve the required classification accuracy, detail, and minimum mapping unit targets. We tested several methodologies in four ecologically distinct pilot areas representing Alberta’s Boreal, Grassland, and Parkland Natural Regions. AI models included ensemble ML using Extreme Gradient Boosting (XGBoost) and Random Forest, and a DL U-Net convolutional neural network (CNN). AI models were trained on expert-labelled photoplots and validated using in situ field surveys. Our findings demonstrate that both ML and DL models met and, in several cases, exceeded the provincial mapping standards with validation overall accuracies surpassing >70% (form), >80% (class), and >90% (wetland–upland). U-Net CNN models generally produced the highest overall accuracies and most precise wetland extent delineation, but XGBoost offered finer detail and granularity for detailed mapping of rare wetland forms. Integrating LiDAR data and derivatives further enhanced model performance, improving accuracy by as much as 13%. Based on these outcomes, we provide a set of recommendations for scaling up these approaches, focusing on model selection, LiDAR imagery integration, and the continued value of field surveys to support the operational scaling of AI-driven classification approaches for wetland inventory updates across Alberta’s diverse landscapes. However, key challenges remain in scaling up this approach due to the cost of acquiring high-resolution LiDAR and satellite imagery.