Assessing Relationships Between Land Cover and Summer Local Climates in the Abisko Region, Northern Sweden
Source abstract
Abstract
Climate warming impacts arctic and subarctic lands, subjecting it to a generalized rise in soil temperature and causing changes in the surface cover. Land cover is a key control parameter for soil hydrothermal states, and its study by satellite imagery is necessary for monitoring boreal surface changes over time at large scales. Understanding the links between land cover and environmental conditions is also crucial to anticipate the impacts of atmospheric changes on continental surfaces. Sentinel-1 and Sentinel-2 data combined with a field campaign in July 2024 were used to produce a 10 m spatial resolution land cover map in the Abisko region, northern Sweden, covering 2180 km2 and including three watersheds with an overall accuracy exceeding 94%. In parallel, temperature and precipitation fields were statistically downscaled at 100 m spatial resolution using topography, ordinary kriging based on weather stations and reanalysis. The relationships between surface areas and average summer temperature–precipitation clusters reveal that the vegetation distribution closely reflects the recent atmospheric conditions with the treeline following the 10.2 °C July–August isotherm in the considered area. This study provides a spatial basis for investigating the complex atmosphere–surface interactions and for assessing the sensitivity of boreal landscapes to ongoing climate warming.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Climate change and permafrost · Science and Climate Studies · Tree-ring climate responses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Géosciences Environnement Toulouse (GET), Centre National de la Recherche Scientifique (CNRS), UMR5563, 31400 Toulouse, France
Abisko Scientific Research Station, Swedish Polar Research Secretariat, SE-981 07 Abisko, Sweden
University of Toulouse, CNES, CNRS, INRAE, IRD, CESBIO, 31400 Toulouse, France
Funding
French National Research Agency ANR · PEACE of PEPR FairCarboN
References (100+)
Show source-provided references
1. The Recent Emergence of Arctic Amplification10.1029/2021GL094086 ↗
2. Arctic Amplification Dominated by Temperature Feedbacks in Contemporary Climate Models10.1038/ngeo2071 ↗
3. Arctic Amplification: A Rapid Response to Radiative Forcing10.1029/2020GL089933 ↗
4. Climate Trends in the Arctic as Observed from Space10.1002/wcc.277 ↗
5. The Arctic Has Warmed Nearly Four Times Faster than the Globe since 197910.1038/s43247-022-00498-3 ↗
6. Spring Snow Cover Extent Reductions in the 2008-2012 Period Exceeding Climate Model Projections: Spring Snow Cover Extent Reductions10.1029/2012GL053387 ↗
7. Northern Hemisphere Spring Snow Cover Variability and Change over 1922–2010 Including an Assessment of Uncertainty10.5194/tc-5-219-2011 ↗
8. Recent Northern Hemisphere Snow Cover Extent Trends and Implications for the Snow-Albedo Feedback
9. Temperature-Induced Recruitment Pulses of Arctic Dwarf Shrub Communities10.1111/1365-2745.12361 ↗
10. Shallow Soils Are Warmer under Trees and Tall Shrubs across Arctic and Boreal Ecosystems10.1088/1748-9326/abc994 ↗
11. Warming-Induced Tipping Points of Arctic and Alpine Shrub Recruitment10.1073/pnas.2118120119 ↗
12. Shrub Growth and Expansion in the Arctic Tundra: An Assessment of Controlling Factors Using an Evidence-Based Approach10.1088/1748-9326/aa7989 ↗
13. Tundra Vegetation Change and Impacts on Permafrost10.1038/s43017-021-00233-0 ↗
14. Arctic Greening and Browning: Challenges and a Cascade of Complexities10.1111/gcb.16118 ↗
15. Microbiome Assembly in Thawing Permafrost and Its Feedbacks to Climate10.1111/gcb.16231 ↗
16. Intergovernmental Panel on Climate Change (IPCC) (2022). The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press. [1st ed.].10.1017/9781009157964 ↗
17. An Observation-Based Constraint on Permafrost Loss as a Function of Global Warming10.1038/nclimate3262 ↗
18. Hydrologic Impacts of Thawing Permafrost—A Review10.2136/vzj2016.01.0010 ↗
19. Fast Response of Cold Ice-Rich Permafrost in Northeast Siberia to a Warming Climate10.1038/s41467-020-15725-8 ↗
20. Pan-Arctic Ice-Wedge Degradation in Warming Permafrost and Its Influence on Tundra Hydrology10.1038/ngeo2674 ↗
21. Vegetation Type Is an Important Predictor of the Arctic Summer Land Surface Energy Budget10.1038/s41467-022-34049-3 ↗
22. Numerical Assessment of Morphological and Hydraulic Properties of Moss, Lichen and Peat from a Permafrost Peatland10.5194/hess-27-431-2023 ↗
23. Snow–Shrub Interactions in Arctic Tundra: A Hypothesis with Climatic Implications10.1175/1520-0442(2001)014<0336:SSIIAT>2.0.CO;2 ↗
24. Cross-scale Regulation of Seasonal Microclimate by Vegetation and Snow in the Arctic Tundra10.1111/gcb.16426 ↗
25. Permafrost Cooled in Winter by Thermal Bridging through Snow-Covered Shrub Branches10.1038/s41561-022-00979-2 ↗
26. Bartsch, A., Höfler, A., Kroisleitner, C., and Trofaier, A. (2016). Land Cover Mapping in Northern High Latitude Permafrost Regions with Satellite Data: Achievements and Remaining Challenges. Remote Sens., 8.10.3390/rs8120979 ↗
27. Current State and Challenges in Producing Large-Scale Land Cover Maps: Review Based on Recent Land Cover Products10.1080/10106049.2023.2242693 ↗
28. Space-Based Observations for Understanding Changes in the Arctic-Boreal Zone10.1029/2019RG000652 ↗
29. Estimating Ice Discharge at Greenland’s Three Largest Outlet Glaciers Using Local Bedrock Uplift10.1029/2021GL094252 ↗
30. Accelerating Ice Loss from Peripheral Glaciers in North Greenland10.1029/2022GL098915 ↗
31. Hagen Bræ: A Surging Glacier in North Greenland—35 Years of Observations10.1029/2019GL085802 ↗
32. Sea Ice Remote Sensing—Recent Developments in Methods and Climate Data Sets10.1007/s10712-023-09781-0 ↗
33. Sentinel-1 Time Series for Mapping Snow Cover Depletion and Timing of Snowmelt in Arctic Periglacial Environments: Case Study from Zackenberg and Kobbefjord, Greenland10.5194/tc-16-625-2022 ↗
34. Hughes-Allen, L., Bouchard, F., Séjourné, A., Fougeron, G., and Léger, E. (2023). Automated Identification of Thermokarst Lakes Using Machine Learning in the Ice-Rich Permafrost Landscape of Central Yakutia (Eastern Siberia). Remote Sens., 15.10.3390/rs15051226 ↗
35. Permafrost Monitoring from Space10.1007/s10712-023-09770-3 ↗
36. Aslami, F., Hopkinson, C., Chasmer, L., Mahoney, C., and Peters, D.L. (2025). Using Bi-Temporal Lidar to Evaluate Canopy Structure and Ecotone Influence on Landsat Vegetation Index Trends Within a Boreal Wetland Complex. Appl. Sci., 15.10.3390/app15094653 ↗
37. Recent Trends and Remaining Challenges for Optical Remote Sensing of Arctic Tundra Vegetation: A Review and Outlook10.1016/j.rse.2020.111872 ↗
38. Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada10.3390/rs6098565 ↗
39. The Circumpolar Arctic Vegetation Map10.1111/j.1654-1103.2005.tb02365.x ↗
40. Cloud Cover and Cloud Types in the Eurasian Arctic in 1936–201210.1002/joc.6187 ↗
41. Long-Term Trends in Total Cloud Cover in the Arctic Based on Surface Observations in 1985–202010.12775/bgeo-2022-0003 ↗
42. Recent Advances in Arctic Cloud and Climate Research10.1007/s40641-016-0051-9 ↗
43. Inglada, J., Nicolas, J.-M., and Tupin, F. (2014). Imagerie de Télédétection, Hermès Science Publications-Lavoisier. IC2.
44. Basic Principles of SAR Polarimetry
45. Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sens., 12.10.3390/rs12071135 ↗
46. The Impacts of Training Data Spatial Resolution on Deep Learning in Remote Sensing10.1016/j.srs.2024.100185 ↗
47. Random Forests for Land Cover Classification10.1016/j.patrec.2005.08.011 ↗
48. Random Forest in Remote Sensing: A Review of Applications and Future Directions10.1016/j.isprsjprs.2016.01.011 ↗
49. Eyes of the World on a Warmer, Less Frozen, and Greener Arctic10.1111/gcb.16767 ↗
50. Plot-Scale Evidence of Tundra Vegetation Change and Links to Recent Summer Warming10.1038/nclimate1465 ↗
51. Climate Sensitivity of Shrub Growth across the Tundra Biome10.1038/nclimate2697 ↗
52. Shrub Expansion in Tundra Ecosystems: Dynamics, Impacts and Research Priorities10.1088/1748-9326/6/4/045509 ↗
53. The Tundra-Taiga Interface and Its Dynamics: Concepts and Applications
54. Borealization of Tundra Ecosystems with Climate and Land-Use Change10.1088/1748-9326/ae2e18 ↗
55. Circumarctic Land Cover Diversity Considering Wetness Gradients10.5194/hess-28-2421-2024 ↗
56. Auda, Y., Lundin, E.J., Gustafsson, J., Pokrovsky, O.S., Cazaurang, S., and Orgogozo, L. (2023). A New Land Cover Map of Two Watersheds under Long-Term Environmental Monitoring in the Swedish Arctic Using Sentinel-2 Data. Water, 15.10.20944/preprints202308.1812.v1 ↗
57. Is the Subarctic Landscape Still a Carbon Sink? Evidence from a Detailed Catchment Balance: A Subarctic Catchment Carbon Balance10.1002/2015GL066970 ↗
58. Vegetation, Climatic Changes and Net Carbon Sequestration in a North-Scandinavian Subarctic Mire over 30 Years10.1111/j.1365-2486.2005.01042.x ↗
59. Combining Airborne Laser Scanning Data and Optical Satellite Data for Classification of Alpine Vegetation
60. Multi-Decadal Changes in Snow Characteristics in Sub-Arctic Sweden10.1007/s13280-011-0164-2 ↗
61. Assessment of Biological and Environmental Phenology at a Landscape Level from 30 Years of Fixed-Date Repeat Photography in Northern Sweden10.1007/s13280-011-0167-z ↗
62. Two Decades of Experimental Manipulations of Heaths and Forest Understory in the Subarctic10.1007/s13280-012-0303-4 ↗
63. Catchment-Scale Dissolved Carbon Concentrations and Export Estimates across Six Subarctic Streams in Northern Sweden10.5194/bg-11-525-2014 ↗
64. Lessons Learned from Monitoring the Stable Water Isotopic Variability in Precipitation and Streamflow across a Snow-Dominated Subarctic Catchment10.1080/15230430.2018.1454778 ↗
65. Northern Hemisphere Permafrost Map Based on TTOP Modelling for 2000–2016 at 1 Km2 Scale10.1016/j.earscirev.2019.04.023 ↗
66. Morin, P., Porter, C., Cloutier, M., Howat, I., Noh, M.-J., Willis, M., Bates, B., Willamson, C., and Peterman, K. (2016, January 17–22). ArcticDEM.; A Publically Available, High Resolution Elevation Model of the Arctic. Proceedings of the EGU General Assembly 2016, Vienna, Austria.
67. Present and Future Köppen-Geiger Climate Classification Maps at 1-Km Resolution10.1038/sdata.2018.214 ↗
68. Climatic Definitions of the World’s Terrestrial Biomes10.3897/VCS.86102 ↗
69. History and Environment of the Nordic Mountain Birch10.1007/3-540-26595-3_1 ↗
70. Copernicus Climate Change Service (2025, September 01). Nordic Gridded Temperature and Precipitation Data from 1971 to Present Derived from In-Situ Observations 2021. Available online: https://cds.climate.copernicus.eu/datasets/insitu-gridded-observations-nordic?tab=overview.
71. Twentieth-Century Variations in Temperature and Precipitation in the Nordic Arctic10.1017/S0032247400017721 ↗
72. Koleck, T., and Hermitte, L. (2023, July 20). S1Tiling. Available online: https://gitlab.orfeo-toolbox.org/s1-tiling/s1tiling.
73. Orfeo ToolBox: Open Source Processing of Remote Sensing Images10.1186/s40965-017-0031-6 ↗
74. Meraoumia, I., Dalsasso, E., Denis, L., and Tupin, F. (2022, January 17). Fast Strategies for Multi-Temporal Speckle Reduction of Sentinel-1 GRD Images. Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.10.1109/IGARSS46834.2022.9883448 ↗
75. Filtering of Multichannel SAR Images10.1109/36.964973 ↗
76. Earnshaw, R.A. (1988). Ray Tracing Algorithms—Theory and Practice. Theoretical Foundations of Computer Graphics and CAD, Springer.10.1007/978-3-642-83539-1 ↗
77. Radar Shadow Detection in Synthetic Aperture Radar Images Using Digital Elevation Model and Projections10.1117/1.JRS.8.083628 ↗
78. Bruzzone, L., Bovolo, F., and Benediktsson, J.A. (2017). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, SPIE.
79. Martins, V., Barbosa, C., De Carvalho, L., Jorge, D., Lobo, F., and Novo, E. (2017). Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes. Remote Sens., 9.10.3390/rs9040322 ↗
80. Müller-Wilm, U., Louis, J., Richter, R., Gascon, F., and Niezette, M. (2013, January 9–13). Sentinel-2 Level 2A Prototype Processor: Architecture, Algorithms and First Results. Proceedings of the ESA Living Planet Symposium 2013, Edinburgh, UK.
81. CloudS2Mask: A Novel Deep Learning Approach for Improved Cloud and Cloud Shadow Masking in Sentinel-2 Imagery10.1016/j.rse.2024.114122 ↗
82. Spectral Signature of Alpine Snow Cover from the Landsat Thematic Mapper10.1016/0034-4257(89)90101-6 ↗
83. Theia Snow Collection: High-Resolution Operational Snow Cover Maps from Sentinel-2 and Landsat-8 Data10.5194/essd-11-493-2019 ↗
84. Fekri-Ershad, S., and Armi, L. (2019). Texture Image Analysis and Texture Classification Methods—A Review. arXiv.
85. Feature Selection Based on Mutual Information Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy10.1109/TPAMI.2005.159 ↗
86. Scikit-Learn: Machine Learning in Python
87. Random Forests10.1023/A:1010933404324 ↗
88. The Predictive Performances of Random Forest Models with Limited Sample Size and Different Species Traits10.1016/j.fishres.2020.105534 ↗
89. Investigation of the Random Forest Framework for Classification of Hyperspectral Data10.1109/TGRS.2004.842481 ↗
90. Liashchynskyi, P., and Liashchynskyi, P. (2019). Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS. arXiv.
91. Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization
92. The Enhancement of Computer Classifications by Logical Smoothing
93. About Regression-Kriging: From Equations to Case Studies10.1016/j.cageo.2007.05.001 ↗
94. Mapping Temperature Using Kriging with External Drift: Theory and an Example from Scotland10.1002/joc.3370140107 ↗
95. Kriging: A Method of Interpolation for Geographical Information Systems10.1080/02693799008941549 ↗
96. Navarro-Serrano, F., López-Moreno, J.I., Azorin-Molina, C., Alonso-González, E., Aznarez-Balta, M., Buisán, S.T., and Revuelto, J. (2020). Elevation Effects on Air Temperature in a Topographically Complex Mountain Valley in the Spanish Pyrenees. Atmosphere, 11.10.3390/atmos11060656 ↗
97. Modelling Surface-air-temperature Variation over Complex Terrain around Abisko, Swedish Lapland: Uncertainties of Measurements and Models at Different Scales10.1111/j.1468-0459.2011.00005.x ↗
98. A Review of the Theories to Explain Arctic and Alpine Treelines Around the World10.1080/10549810802626456 ↗
99. Are Treelines Advancing? A Global Meta-analysis of Treeline Response to Climate Warming10.1111/j.1461-0248.2009.01355.x ↗
100. Scikit-Image: Image Processing in Python10.7717/peerj.453 ↗