Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4709-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4709-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
20 Jun 2022
Model description paper |
| 20 Jun 2022
| 20 Jun 2022
A map of global peatland extent created using machine learning (Peat-ML)
Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
Climate Research Division, Environment and Climate Change Canada, Toronto, ON, Canada
Koreen Millard
Geography and Environmental Studies, Carleton University, Ottawa, ON, Canada
Matthew Fortier
Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
R. Scott Winton
Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
Department of Surface Waters, Eawag, Swiss Federal Institution of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland
Department of Earth System Science, Stanford University, Stanford, CA 94305, USA
Javier M. Martín-López
Agroecosystems and Sustainable Landscapes Program, Alliance Bioversity-CIAT, Cali, Colombia
Hinsby Cadillo-Quiroz
School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
Darren Kidd
Natural Values Science Services, Department of Natural Resources and Environment, Hobart, Tasmania, Australia
Louis V. Verchot
Agroecosystems and Sustainable Landscapes Program, Alliance Bioversity-CIAT, Cali, Colombia
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- SatViT: Pretraining Transformers for Earth Observation A. Fuller et al. 10.1109/LGRS.2022.3201489
- Control of local topography and surface patterning on the formation and stability of a slope permafrost peatland at 4800-m elevation on the central Qinghai-Tibetan Plateau Y. Li et al. 10.1016/j.ecolind.2023.111475
- Analysis of peat soil testing errors based on its characteristics and appropriate recommendation of peat soil testing A. Khoerani et al. 10.1051/e3sconf/202342904018
- Montane peatland response to drought: Evidence from multispectral and thermal UAS monitoring J. Langhammer et al. 10.1016/j.ecolind.2024.112587
- The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios J. Doelman et al. 10.1088/2752-5295/acd5f4
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- Mapping and monitoring peatland conditions from global to field scale B. Minasny et al. 10.1007/s10533-023-01084-1
- Warming-induced vapor pressure deficit suppression of vegetation growth diminished in northern peatlands N. Chen et al. 10.1038/s41467-023-42932-w
- Research Progress in the Field of Peatlands in 1990–2022: A Systematic Analysis Based on Bibliometrics J. Shi et al. 10.3390/land13040549
- Towards a roadmap for space-based observations of the land sector for the UNFCCC global stocktake O. Ochiai et al. 10.1016/j.isci.2023.106489
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- Genes and genome‐resolved metagenomics reveal the microbial functional make up of Amazon peatlands under geochemical gradients M. Pavia et al. 10.1111/1462-2920.16469
- A meta-analysis of peatland microbial diversity and function responses to climate change M. Le Geay et al. 10.1016/j.soilbio.2023.109287
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- A new data-driven map predicts substantial undocumented peatland areas in Amazonia A. Hastie et al. 10.1088/1748-9326/ad677b
- Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands I. Burdun et al. 10.1016/j.rse.2023.113736
- Unveiling the Past: Deep-Learning-Based Estimation of Historical Peatland Distribution S. Cha et al. 10.3390/land13030328
- Initial assessment of the peatlands of the upper-Ucayali Valley, Central Peruvian Amazon: Basic analysis of geographic products & predictors B. Crnobrna et al. 10.1016/j.gecco.2024.e03056
- Topographic and climatic controls of peatland distribution on the Tibetan Plateau J. Sun et al. 10.1038/s41598-023-39699-x
- Peatlands and their carbon dynamics in northern high latitudes from 1990 to 2300: a process-based biogeochemistry model analysis B. Zhao & Q. Zhuang 10.5194/bg-20-251-2023
- Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium C. Lawley et al. 10.3389/esss.2024.10109
- Global increase in biomass carbon stock dominated by growth of northern young forests over past decade H. Yang et al. 10.1038/s41561-023-01274-4
- Adapting machine learning for environmental spatial data - A review M. Jemeļjanova et al. 10.1016/j.ecoinf.2024.102634
- A map of global peatland extent created using machine learning (Peat-ML) J. Melton et al. 10.5194/gmd-15-4709-2022
- A deep learning approach for high‐resolution mapping of Scottish peatland degradation F. Macfarlane et al. 10.1111/ejss.13538
- Modeling Carbon Accumulation and Permafrost Dynamics of Northern Peatlands Since the Holocene B. Zhao et al. 10.1029/2022JG007009
25 citations as recorded by crossref.
- Socio-Ecological Approach to a Forest-Swamp-Savannah Mosaic Landscape Using Remote Sensing and Local Knowledge: a Case Study in the Bas-Ogooué Ramsar Site, Gabon C. Demichelis et al. 10.1007/s00267-023-01827-8
- SatViT: Pretraining Transformers for Earth Observation A. Fuller et al. 10.1109/LGRS.2022.3201489
- Control of local topography and surface patterning on the formation and stability of a slope permafrost peatland at 4800-m elevation on the central Qinghai-Tibetan Plateau Y. Li et al. 10.1016/j.ecolind.2023.111475
- Analysis of peat soil testing errors based on its characteristics and appropriate recommendation of peat soil testing A. Khoerani et al. 10.1051/e3sconf/202342904018
- Montane peatland response to drought: Evidence from multispectral and thermal UAS monitoring J. Langhammer et al. 10.1016/j.ecolind.2024.112587
- The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios J. Doelman et al. 10.1088/2752-5295/acd5f4
- Using the Canadian Model for Peatlands (CaMP) to examine greenhouse gas emissions and carbon sink strength in Canada's boreal and temperate peatlands K. Bona et al. 10.1016/j.ecolmodel.2024.110633
- An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa K. Nesha et al. 10.1088/1748-9326/ad6679
- Mapping and monitoring peatland conditions from global to field scale B. Minasny et al. 10.1007/s10533-023-01084-1
- Warming-induced vapor pressure deficit suppression of vegetation growth diminished in northern peatlands N. Chen et al. 10.1038/s41467-023-42932-w
- Research Progress in the Field of Peatlands in 1990–2022: A Systematic Analysis Based on Bibliometrics J. Shi et al. 10.3390/land13040549
- Towards a roadmap for space-based observations of the land sector for the UNFCCC global stocktake O. Ochiai et al. 10.1016/j.isci.2023.106489
- On the detailed mapping of peat (raised bogs) using airborne radiometric data D. Beamish & J. White 10.1016/j.jenvrad.2024.107462
- Genes and genome‐resolved metagenomics reveal the microbial functional make up of Amazon peatlands under geochemical gradients M. Pavia et al. 10.1111/1462-2920.16469
- A meta-analysis of peatland microbial diversity and function responses to climate change M. Le Geay et al. 10.1016/j.soilbio.2023.109287
- Mapping high-altitude peatlands to inform a landscape conservation strategy in the Andes of northern Peru G. Curatola Fernández et al. 10.1017/S0376892923000267
- A new data-driven map predicts substantial undocumented peatland areas in Amazonia A. Hastie et al. 10.1088/1748-9326/ad677b
- Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands I. Burdun et al. 10.1016/j.rse.2023.113736
- Unveiling the Past: Deep-Learning-Based Estimation of Historical Peatland Distribution S. Cha et al. 10.3390/land13030328
- Initial assessment of the peatlands of the upper-Ucayali Valley, Central Peruvian Amazon: Basic analysis of geographic products & predictors B. Crnobrna et al. 10.1016/j.gecco.2024.e03056
- Topographic and climatic controls of peatland distribution on the Tibetan Plateau J. Sun et al. 10.1038/s41598-023-39699-x
- Peatlands and their carbon dynamics in northern high latitudes from 1990 to 2300: a process-based biogeochemistry model analysis B. Zhao & Q. Zhuang 10.5194/bg-20-251-2023
- Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium C. Lawley et al. 10.3389/esss.2024.10109
- Global increase in biomass carbon stock dominated by growth of northern young forests over past decade H. Yang et al. 10.1038/s41561-023-01274-4
- Adapting machine learning for environmental spatial data - A review M. Jemeļjanova et al. 10.1016/j.ecoinf.2024.102634
3 citations as recorded by crossref.
- A map of global peatland extent created using machine learning (Peat-ML) J. Melton et al. 10.5194/gmd-15-4709-2022
- A deep learning approach for high‐resolution mapping of Scottish peatland degradation F. Macfarlane et al. 10.1111/ejss.13538
- Modeling Carbon Accumulation and Permafrost Dynamics of Northern Peatlands Since the Holocene B. Zhao et al. 10.1029/2022JG007009
Latest update: 15 Oct 2024
Short summary
Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.
Peat-ML is a high-resolution global peatland extent map generated using machine learning...
