Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
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.
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.
Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.