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This data set contains a classification of the North Slope, Alaska for drained lake basins (DLBs) based on Landsat-8 imagery of the years 2014-2019 and Arctic Digital Elevation Model (ArcticDEM) data. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. This data set is based on a novel and scalable remote sensing-based approach to identify DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. The data set was validated against several prior sub-regional scale datasets and manually classified points. The study area covers greater than 71,000 square kilometers (km2), including a greater than 39,000 km2 area not previously covered in existing DLB data sets. Within the data set, three classes are present: DLB/ambiguous/noDLB. Areas classified as ambiguous could not be classified as DLB or noDLB with sufficient certainty. Users may decide on a case by case basis if they wish to use the conservative estimate of DLB area, therefore omitting areas classified as ambiguous, or to use all three classes.
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Remote Sensing-Based Statistical Approach for Defining Drained Lake Basins in a Continuous Permafrost Region, North Slope of Alaska
Lake formation and drainage are pervasive phenomena in permafrost regions. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. In this study, we present a novel and scalable remote sensing-based approach to identifying DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. We validated this first North Slope-wide DLB data product against several previously published sub-regional scale datasets and manually classified points. The study area covered >71,000 km2, including a >39,000 km2 area not previously covered in existing DLB datasets. Our approach used Landsat-8 multispectral imagery and ArcticDEM data to derive a pixel-by-pixel statistical assessment of likelihood of DLB occurrence in sub-regions with different permafrost and periglacial landscape conditions, as well as to quantify aerial coverage of DLBs on the North Slope of Alaska. The results were consistent with previously published regional DLB datasets (up to 87% agreement) and showed high agreement with manually classified random points (64.4–95.5% for DLB and 83.2–95.4% for non-DLB areas). Validation of the remote sensing-based statistical approach on the North Slope of Alaska indicated that it may be possible to extend this methodology to conduct a comprehensive assessment of DLBs in pan-Arctic lowland permafrost regions. Better resolution of the spatial distribution of DLBs in lowland permafrost regions is important for quantitative studies on landscape diversity, wildlife habitat, permafrost, hydrology, geotechnical conditions, and high-latitude carbon cycling.
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- PAR ID:
- 10277616
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 13
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2539
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This data set contains a classification of the North Slope, Alaska for drained lake basins (DLBs) based on Landsat-8 imagery of the years 2014-2019 and Arctic Digital Elevation Model (ArcticDEM) data. Drained lake basins (DLBs) are often the most common landforms in lowland permafrost regions in the Arctic (50% to 75% of the landscape). However, detailed assessments of DLB distribution and abundance are limited. This data set is based on a novel and scalable remote sensing-based approach to identify DLBs in lowland permafrost regions, using the North Slope of Alaska as a case study. The data set was validated against several prior sub-regional scale datasets and manually classified points. The study area covers greater than 71,000 square kilometers (km2), including a greater than 39,000 km2 area not previously covered in existing DLB data sets. Within the data set, three classes are present: DLB/ambiguous/noDLB. Areas classified as ambiguous could not be classified as DLB or noDLB with sufficient certainty. Users may decide on a case by case basis if they wish to use the conservative estimate of DLB area, therefore omitting areas classified as ambiguous, or to use all three classes.more » « less
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Abstract Wetlands in Arctic drained lake basins (DLBs) have a high potential for carbon storage in vegetation and peat as well as for elevated greenhouse gas emissions. However, the evolution of vegetation and organic matter is rarely studied in DLBs, making these abundant wetlands especially uncertain elements of the permafrost carbon budget. We surveyed multiple DLB generations in northern Alaska with the goal to assess vegetation, microtopography, and organic matter in surface sediment and pond water in DLBs and to provide the first high-resolution land cover classification for a DLB system focussing on moisture-related vegetation classes for the Teshekpuk Lake region. We associated sediment properties and methane concentrations along a post-drainage succession gradient with remote sensing-derived land cover classes. Our study distinguished five eco-hydrological classes using statistical clustering of vegetation data, which corresponded to the land cover classes. We identified surface wetness and time since drainage as predictors of vegetation composition. Microtopographic complexity increased after drainage. Organic carbon and nitrogen contents in sediment, and dissolved organic carbon (DOC) and dissolved nitrogen (DN) in ponds were high throughout, indicating high organic matter availability and decomposition. We confirmed wetness as a predictor of sediment methane concentrations. Our findings suggest moderate to high methane concentrations independent of drainage age, with particularly high concentrations beneath submerged patches (up to 200μmol l−1) and in pond water (up to 22μmol l−1). In our DLB system, wet and shallow submerged patches with high methane concentrations occupied 54% of the area, and ponds with high DOC, DN and methane occupied another 11%. In conclusion, we demonstrate that DLB wetlands are highly productive regarding organic matter decomposition and methane production. Machine learning-aided land cover classification using high-resolution multispectral satellite imagery provides a useful tool for future upscaling of sediment properties and methane emission potentials from Arctic DLBs.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs13132539
