Scaling Arctic landscape and permafrost features improves active layer depth modeling
Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, ve...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research: Ecology |
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| Online Access: | https://doi.org/10.1088/2752-664X/ad9f6c |
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| author | Wouter Hantson Daryl Yang Shawn P Serbin Joshua B Fisher Daniel J Hayes |
| author_facet | Wouter Hantson Daryl Yang Shawn P Serbin Joshua B Fisher Daniel J Hayes |
| author_sort | Wouter Hantson |
| collection | DOAJ |
| description | Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region. |
| format | Article |
| id | doaj-art-2201a8d89c464ff59d64ddc0bcc01351 |
| institution | Kabale University |
| issn | 2752-664X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research: Ecology |
| spelling | doaj-art-2201a8d89c464ff59d64ddc0bcc013512025-01-20T11:07:18ZengIOP PublishingEnvironmental Research: Ecology2752-664X2025-01-014101500110.1088/2752-664X/ad9f6cScaling Arctic landscape and permafrost features improves active layer depth modelingWouter Hantson0https://orcid.org/0000-0002-2882-6897Daryl Yang1https://orcid.org/0000-0003-1705-7823Shawn P Serbin2https://orcid.org/0000-0003-4136-8971Joshua B Fisher3https://orcid.org/0000-0003-4734-9085Daniel J Hayes4https://orcid.org/0000-0002-3011-7934School of Forest Resources, University of Maine , Orono, ME 04469, United States of AmericaEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory , Oak Ridge, TN 37830, United States of AmericaBiospheric Sciences Laboratory, NASA Goddard Space Flight Center , Greenbelt, MD 20771, United States of AmericaSchmid College of Science and Technology, Chapman University, 1 University Drive , Orange, CA 92866, United States of AmericaSchool of Forest Resources, University of Maine , Orono, ME 04469, United States of AmericaTundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region.https://doi.org/10.1088/2752-664X/ad9f6cscalingArcticpermafrostUAS |
| spellingShingle | Wouter Hantson Daryl Yang Shawn P Serbin Joshua B Fisher Daniel J Hayes Scaling Arctic landscape and permafrost features improves active layer depth modeling Environmental Research: Ecology scaling Arctic permafrost UAS |
| title | Scaling Arctic landscape and permafrost features improves active layer depth modeling |
| title_full | Scaling Arctic landscape and permafrost features improves active layer depth modeling |
| title_fullStr | Scaling Arctic landscape and permafrost features improves active layer depth modeling |
| title_full_unstemmed | Scaling Arctic landscape and permafrost features improves active layer depth modeling |
| title_short | Scaling Arctic landscape and permafrost features improves active layer depth modeling |
| title_sort | scaling arctic landscape and permafrost features improves active layer depth modeling |
| topic | scaling Arctic permafrost UAS |
| url | https://doi.org/10.1088/2752-664X/ad9f6c |
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