Articles | Volume 8, issue 2
https://doi.org/10.5194/soil-8-733-2022
© 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/soil-8-733-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
07 Dec 2022
Original research article |
| 07 Dec 2022
| 07 Dec 2022
Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions
Department of Forest Ecology and Management, Swedish University of
Agricultural Science, Umeå 901 87, Sweden
Eliza Maher Hasselquist
Department of Forest Ecology and Management, Swedish University of
Agricultural Science, Umeå 901 87, Sweden
Johan Stendahl
Department of Soil and Environment, Swedish University of Agricultural
Science, Uppsala 756 51, Sweden
Mats B. Nilsson
Department of Forest Ecology and Management, Swedish University of
Agricultural Science, Umeå 901 87, Sweden
Siddhartho S. Paul
Department of Forest Ecology and Management, Swedish University of
Agricultural Science, Umeå 901 87, Sweden
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Cited
14 citations as recorded by crossref.
- Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices L. Rimondini et al. 10.5194/essd-15-3473-2023
- Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape J. Larson et al. 10.1038/s41598-024-61098-z
- Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects A. Abdelmajeed & R. Juszczak 10.3390/rs16030591
- Mapping soil parent materials in a previously glaciated landscape: Potential for a machine learning approach for detailed nationwide mapping Y. Lin et al. 10.1016/j.geodrs.2024.e00905
- A fully automated model for land use classification from historical maps using machine learning A. Ågren & Y. Lin 10.1016/j.rsase.2024.101349
- Estimating Soil Strength Using GIS-Based Maps - A case study in Sweden S. MOHTASHAMİ et al. 10.33904/ejfe.1321075
- Drone-borne ground-penetrating radar reveals spatiotemporal moisture dynamics in peatland root zones M. Henrion et al. 10.1016/j.srs.2025.100311
- High-resolution harvester data for estimating rolling resistance and forest trafficability A. Salmivaara et al. 10.1007/s10342-024-01717-6
- Carbon accumulation in recently deposited peat is reduced by increased nutrient supply B. Ehnvall et al. 10.1038/s41467-025-59387-w
- Soil moisture controls the partitioning of carbon stocks across a managed boreal forest landscape J. Larson et al. 10.1038/s41598-023-42091-4
- Ditches show systematic impacts on soil and vegetation properties across the Swedish forest landscape A. Ågren et al. 10.1016/j.foreco.2024.121707
- Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning J. Pohjankukka et al. 10.1016/j.geoderma.2025.117216
- A 20 m spatial resolution peatland extent map of Alaska M. Lara et al. 10.1038/s41597-025-04502-1
- Topography and Time Shape Mire Morphometry and Large‐Scale Mire Distribution Patterns in the Northern Boreal Landscape B. Ehnvall et al. 10.1029/2023JF007324
13 citations as recorded by crossref.
- Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices L. Rimondini et al. 10.5194/essd-15-3473-2023
- Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape J. Larson et al. 10.1038/s41598-024-61098-z
- Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects A. Abdelmajeed & R. Juszczak 10.3390/rs16030591
- Mapping soil parent materials in a previously glaciated landscape: Potential for a machine learning approach for detailed nationwide mapping Y. Lin et al. 10.1016/j.geodrs.2024.e00905
- A fully automated model for land use classification from historical maps using machine learning A. Ågren & Y. Lin 10.1016/j.rsase.2024.101349
- Estimating Soil Strength Using GIS-Based Maps - A case study in Sweden S. MOHTASHAMİ et al. 10.33904/ejfe.1321075
- Drone-borne ground-penetrating radar reveals spatiotemporal moisture dynamics in peatland root zones M. Henrion et al. 10.1016/j.srs.2025.100311
- High-resolution harvester data for estimating rolling resistance and forest trafficability A. Salmivaara et al. 10.1007/s10342-024-01717-6
- Carbon accumulation in recently deposited peat is reduced by increased nutrient supply B. Ehnvall et al. 10.1038/s41467-025-59387-w
- Soil moisture controls the partitioning of carbon stocks across a managed boreal forest landscape J. Larson et al. 10.1038/s41598-023-42091-4
- Ditches show systematic impacts on soil and vegetation properties across the Swedish forest landscape A. Ågren et al. 10.1016/j.foreco.2024.121707
- Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning J. Pohjankukka et al. 10.1016/j.geoderma.2025.117216
- A 20 m spatial resolution peatland extent map of Alaska M. Lara et al. 10.1038/s41597-025-04502-1
Latest update: 28 Oct 2025
Short summary
Historically, many peatlands in the boreal region have been drained for timber production. Given the prospects of a drier future due to climate change, wetland restorations are now increasing. Better maps hold the key to insights into restoration targets and land-use management policies, and maps are often the number one decision-support tool. We use an AI-developed soil moisture map based on laser scanning data to illustrate how the mapping of peatlands can be improved across an entire nation.
Historically, many peatlands in the boreal region have been drained for timber production. Given...
