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

• Published in: Remote sensing in ecology and conservation Vol. 10; no. 4; pp. 551 - 562
• Main Authors: Habib, Wahaj, Cresson, Rémi, McGuinness, Kevin, Connolly, John
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons, Inc 01.08.2024; Wiley
• Subjects: Aerial photography; Agricultural ecosystems; Aquatic ecosystems; artificial drainage; Automation; Biodiversity; Bogs; Canals; Carbon; Conservation; Datasets; Deep learning; Ditches; Drainage; Drainage ditches; Drains; Ecosystem services; Ecosystems; Emissions; Environmental regulations; Environmental Sciences; Farm buildings; Fracditch; Greenhouse gases; Hydrology; Imagery; Intergovernmental Panel on Climate Change; Land use; Mapping; Marine ecosystems; Peat; Peatlands; Remote sensing; semantic segmentaion; Semantics; Sustainability management; Vegetation; wetlands; United Kingdom > UK; Ireland; artificial drainage; semantic segmentaion; wetlands; remote sensing; Frac ditch
• ISSN: 2056-3485; 2056-3485
• DOI: 10.1002/rse2.387

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 Fracditch which was 0.03 (fraction of artificial drainage on industrial peat extraction sites). This is lower than IPCC Tier 1 Fracditch 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 enhancing the comprehension of the global effects of artificial drainage ditches on peatlands. Using very high‐resolution (25 cm) aerial imagery and a deep learning approach based on U‐Net, we mapped 20 000 km of artificial drains in peatlands at a fine scale with high accuracy. The resulting maps illustrated the high density and intensity of degradation patterns in Irish‐raised bogs at a national scale. The study demonstrates the potential of the model and its widespread applicability for peatlands at regional and global scales.