Mapping artificial drains in peatlands—A national‐scale assessment of Irish raised bogs using sub‐meter aerial imagery and deep learning methods

International audience ; Peatlands, constituting over half of terrestrial wetland ecosystems across the globe, hold critical ecological significance and are large stores of carbon (C). Irish oceanic raised bogs are a rare peatland ecosystem offering numerous ecosystem services, including C storage, biodiversity support and water regulation. However, they have been degraded over the centuries due to artificial drainage, followed by peat extraction, afforestation and agriculture. This has an overall negative impact on the functioning of peatlands, shifting them from a moderate C sink to a large C source. Recognizing the importance of these ecosystems, efforts are underway for conservation (rewetting and rehabilitation), while accurately accounting for C stock and greenhouse gas (GHG) emissions. However, the implementation of these efforts requires accurate identification and mapping of artificial drainage ditches. This study utilized very high-resolution (25 cm) aerial imagery, and a deep learning (U-Net) approach to map the visible artificial drainage (unobstructed by vegetation or infill) in raised bogs at a national scale. The results show that artificial drainage is widespread, with $ 20 000 km of drains mapped. The overall accuracy of the model was 80% on an independent testing dataset. The data were also used to derive the Frac ditch which was 0.03 (fraction of artificial drainage on industrial peat extraction sites). This is lower than IPCC Tier 1 Frac ditch and can aid in IPCC Tier 2 reporting for Ireland. This is the first study to map drains with diverse sizes and patterns on Irish-raised bogs using optical aerial imagery and deep learning methods. The map will serve as an important baseline dataset for evaluating the artificial drainage ditch conditions. It will prove useful for sustainable management, conservation and refined estimations of GHG emissions. The model's capacity for generalization implies its potential in mapping artificial drains in peatlands at a regional and global scale, thereby ...