Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions
Abstract Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long‐term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian peatlands, which have experienced...
Main Authors: | , , , , , , , , |
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Format: | Article |
Language: | English |
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Wiley
2021-03-01
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Series: | AGU Advances |
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Online Access: | https://doi.org/10.1029/2020AV000321 |
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author | Nathan C. Dadap Alison M. Hoyt Alexander R. Cobb Doruk Oner Mateusz Kozinski Pascal V. Fua Krishna Rao Charles F. Harvey Alexandra G. Konings |
author_facet | Nathan C. Dadap Alison M. Hoyt Alexander R. Cobb Doruk Oner Mateusz Kozinski Pascal V. Fua Krishna Rao Charles F. Harvey Alexandra G. Konings |
author_sort | Nathan C. Dadap |
collection | DOAJ |
description | Abstract Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long‐term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian peatlands, which have experienced decades of land use change, the exact extent and spatial distribution of drainage canals are unknown. This has prevented regional‐scale investigation of the relationships between drainage, land use, and carbon emissions. Here, we create the first regional map of drainage canals using high resolution satellite imagery and a convolutional neural network. We find that drainage is widespread—occurring in at least 65% of peatlands and across all land use types. Although previous estimates of peatland carbon emissions have relied on land use as a proxy for drainage, our maps show substantial variation in drainage density within land use types. Subsidence rates are 3.2 times larger in intensively drained areas than in non‐drained areas, highlighting the central role of drainage in mediating peat subsidence. Accounting for drainage canals was found to improve a subsidence prediction model by 30%, suggesting that canals contain information about subsidence not captured by land use alone. Thus, our data set can be used to improve subsidence and associated carbon emissions predictions in peatlands, and to target areas for hydrologic restoration. |
first_indexed | 2024-04-12T15:49:53Z |
format | Article |
id | doaj.art-931c5c365b2f4fa38104bcfd8735d87c |
institution | Directory Open Access Journal |
issn | 2576-604X |
language | English |
last_indexed | 2024-04-12T15:49:53Z |
publishDate | 2021-03-01 |
publisher | Wiley |
record_format | Article |
series | AGU Advances |
spelling | doaj.art-931c5c365b2f4fa38104bcfd8735d87c2022-12-22T03:26:33ZengWileyAGU Advances2576-604X2021-03-0121n/an/a10.1029/2020AV000321Drainage Canals in Southeast Asian Peatlands Increase Carbon EmissionsNathan C. Dadap0Alison M. Hoyt1Alexander R. Cobb2Doruk Oner3Mateusz Kozinski4Pascal V. Fua5Krishna Rao6Charles F. Harvey7Alexandra G. Konings8Department of Earth System Science Stanford University Stanford CA USAMax Planck Institute for Biogeochemistry Jena GermanyCenter for Environmental Sensing and Modeling Singapore‐MIT Alliance for Research and Technology Singapore SingaporeÉcole Polytechnique Fédérale de Lausanne Lausanne SwitzerlandÉcole Polytechnique Fédérale de Lausanne Lausanne SwitzerlandÉcole Polytechnique Fédérale de Lausanne Lausanne SwitzerlandDepartment of Earth System Science Stanford University Stanford CA USADepartment of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USADepartment of Earth System Science Stanford University Stanford CA USAAbstract Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long‐term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian peatlands, which have experienced decades of land use change, the exact extent and spatial distribution of drainage canals are unknown. This has prevented regional‐scale investigation of the relationships between drainage, land use, and carbon emissions. Here, we create the first regional map of drainage canals using high resolution satellite imagery and a convolutional neural network. We find that drainage is widespread—occurring in at least 65% of peatlands and across all land use types. Although previous estimates of peatland carbon emissions have relied on land use as a proxy for drainage, our maps show substantial variation in drainage density within land use types. Subsidence rates are 3.2 times larger in intensively drained areas than in non‐drained areas, highlighting the central role of drainage in mediating peat subsidence. Accounting for drainage canals was found to improve a subsidence prediction model by 30%, suggesting that canals contain information about subsidence not captured by land use alone. Thus, our data set can be used to improve subsidence and associated carbon emissions predictions in peatlands, and to target areas for hydrologic restoration.https://doi.org/10.1029/2020AV000321carbon emissionsdeep learningdrainageland usesubsidencetropical peatlands |
spellingShingle | Nathan C. Dadap Alison M. Hoyt Alexander R. Cobb Doruk Oner Mateusz Kozinski Pascal V. Fua Krishna Rao Charles F. Harvey Alexandra G. Konings Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions AGU Advances carbon emissions deep learning drainage land use subsidence tropical peatlands |
title | Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions |
title_full | Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions |
title_fullStr | Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions |
title_full_unstemmed | Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions |
title_short | Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions |
title_sort | drainage canals in southeast asian peatlands increase carbon emissions |
topic | carbon emissions deep learning drainage land use subsidence tropical peatlands |
url | https://doi.org/10.1029/2020AV000321 |
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