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

IF 3.9 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2024-04-23 DOI:10.1002/rse2.387

Wahaj Habib, Rémi Cresson, Kevin McGuinness, John Connolly

{"title":"Mapping artificial drains in peatlands—A national‐scale assessment of Irish raised bogs using sub‐meter aerial imagery and deep learning methods","authors":"Wahaj Habib, Rémi Cresson, Kevin McGuinness, John Connolly","doi":"10.1002/rse2.387","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"217 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing in Ecology and Conservation","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1002/rse2.387","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

绘制泥炭地人工排水沟图--利用亚米级航空图像和深度学习方法对爱尔兰隆起沼泽进行国家级评估

泥炭地占全球陆地湿地生态系统的一半以上，具有重要的生态意义，是大量的碳（C）储存地。爱尔兰海洋性隆起沼泽是一种罕见的泥炭地生态系统，可提供多种生态系统服务，包括碳储存、生物多样性支持和水调节。然而，几个世纪以来，由于人工排水、泥炭开采、植树造林和农业，它们已经退化。这对泥炭地的功能产生了全面的负面影响，使其从适度的碳汇转变为大量的碳源。由于认识到这些生态系统的重要性，人们正在努力进行保护（复湿和恢复），同时准确计算碳储量和温室气体（GHG）排放量。然而，这些工作的实施需要对人工排水沟进行准确的识别和绘图。本研究利用高分辨率（25 厘米）航空图像和深度学习（U-Net）方法，绘制了全国范围内隆起沼泽中可见的人工排水沟（未被植被或填充物阻挡）。结果表明，人工排水系统非常普遍，绘制的排水系统总长约 2 万公里。在一个独立的测试数据集上，该模型的总体准确率为 80%。这些数据还被用于推导弗拉克迪奇指数（Fracditch），该指数为 0.03（工业泥炭开采地人工排水的比例）。这比 IPCC 第 1 级的 Fracditch 要低，有助于爱尔兰的 IPCC 第 2 级报告。这是首次使用航空光学图像和深度学习方法绘制爱尔兰沼泽地上不同规模和模式的排水沟的研究。该地图将成为评估人工排水沟状况的重要基准数据集。它将被证明有助于可持续管理、保护和温室气体排放的精细估算。该模型的泛化能力意味着它具有在区域和全球范围内绘制泥炭地人工排水沟地图的潜力，从而增强对人工排水沟对泥炭地全球影响的理解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences

CiteScore

9.80

自引率

5.50%

发文量

审稿时长

18 weeks

期刊介绍： emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.