skip to main content

Title: 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 more » 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. « less
Authors:
; ; ; ; ; ; ; ; ; ; ;
Award ID(s):
1806213 1806287 1820883
Publication Date:
NSF-PAR ID:
10277616
Journal Name:
Remote Sensing
Volume:
13
Issue:
13
Page Range or eLocation-ID:
2539
ISSN:
2072-4292
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract
    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.
  2. The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70more »and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system.« less
  3. Abstract. Northwestern Alaska has been highly affected by changing climatic patternswith new temperature and precipitation maxima over the recent years. Inparticular, the Baldwin and northern Seward peninsulas are characterized byan abundance of thermokarst lakes that are highly dynamic and prone to lakedrainage like many other regions at the southern margins of continuouspermafrost. We used Sentinel-1 synthetic aperture radar (SAR) and PlanetCubeSat optical remote sensing data to analyze recently observed widespreadlake drainage. We then used synoptic weather data, climate model outputs andlake ice growth simulations to analyze potential drivers and future pathwaysof lake drainage in this region. Following the warmest and wettest winter onrecord in 2017/2018, 192 lakes were identified as having completely orpartially drained by early summer 2018, which exceeded the average drainagerate by a factor of ∼ 10 and doubled the rates of the previousextreme lake drainage years of 2005 and 2006. The combination of abundantrain- and snowfall and extremely warm mean annual air temperatures (MAATs),close to 0 ∘C, may have led to the destabilization of permafrostaround the lake margins. Rapid snow melt and high amounts of excessmeltwater further promoted rapid lateral breaching at lake shores andconsequently sudden drainage of some of the largest lakes of the studyregion that have likelymore »persisted for millennia. We hypothesize that permafrostdestabilization and lake drainage will accelerate and become the dominantdrivers of landscape change in this region. Recent MAATs are already withinthe range of the predictions by the University of Alaska Fairbanks' Scenarios Network for Alaska and Arctic Planning (UAF SNAP) ensemble climate predictions inscenario RCP6.0 for 2100. With MAAT in 2019 just below 0 ∘C at the nearby Kotzebue, Alaska, climate station, permafrost aggradation in drained lake basins will become less likely after drainage, strongly decreasing the potential for freeze-locking carbon sequestered in lake sediments, signifying a prominent regime shift in ice-rich permafrost lowland regions.« less
  4. Abstract
    Assessment of lakes for their future potential to drain relied on the 2002/03 airborne Interferometric Synthetic Aperture Radar (IFSAR) Digital Surface Model (DSM) data for the western Arctic Coastal Plain in northern Alaska. Lakes were extracted from the IfSAR DSM using a slope derivative and manual correction (Jones et al., 2017). The vertical uncertainty for correctly detecting lake-based drainage gradients with the IfSAR DSM was defined by comparing surface elevation differences of several overlapping DSM tile edges. This comparison showed standard deviations of elevation between overlapping IfSAR tiles ranging from 0.0 to 0.6 meters (m). Thus, we chose a minimum height difference of 0.6 m to represent a detectable elevation gradient adjacent to a lake as being most likely to contribute to a rapid drainage event. This value is also in agreement with field verified estimates of the relative vertical accuracy (~0.5 m) of the DSM dataset around Utqiaġvik (formerly Barrow) (Manley et al., 2005) and the stated vertical RMSE (~1.0 m) of the DSM data (Intermap, 2010). Development of the potential lake drainage dataset involved several processing steps. First, lakes were classified as potential future drainage candidates if the difference between the elevation of the lake surface andMore>>
  5. Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers areexpected to increase in response to warming and associated permafrost thaw.However, the lack of appropriate land cover datasets for scalingfield-measured methane emissions to circumpolar scales has contributed to alarge uncertainty for our understanding of present-day and future methaneemissions. Here we present the Boreal–Arctic Wetland and Lake Dataset(BAWLD), a land cover dataset based on an expert assessment, extrapolatedusing random forest modelling from available spatial datasets of climate,topography, soils, permafrost conditions, vegetation, wetlands, and surfacewater extents and dynamics. In BAWLD, we estimate the fractional coverage offive wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes(17 % of the global land surface). Land cover classes were defined usingcriteria that ensured distinct methane emissions among classes, as indicatedby a co-developed comprehensive dataset of methane flux observations. InBAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain)with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetlandclasses, covering ∼ 28 % each of the total wetland area,while the highest-methane-emitting marsh and tundra wetland classes occupied5 % and 12 %, respectively. Lakes, defined to include all lentic open-waterecosystems regardless of size, covered 1.4 × 106 km2(6 % of domain).more »Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lakearea, while high-emitting peatland and yedoma lakes covered 18 % and 4 %,respectively. Small (<0.1 km2) glacial, peatland, and yedomalakes combined covered 17 % of the total lake area but contributeddisproportionally to the overall spatial uncertainty in lake area with a95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12  × 106 km2 (0.5 % of domain), of which 8 % was associated withhigh-methane-emitting headwaters that drain organic-rich landscapes.Distinct combinations of spatially co-occurring wetland and lake classeswere identified across the BAWLD domain, allowing for the mapping of“wetscapes” that have characteristic methane emission magnitudes andsensitivities to climate change at regional scales. With BAWLD, we provide adataset which avoids double-accounting of wetland, lake, and river extentsand which includes confidence intervals for each land cover class. As such,BAWLD will be suitable for many hydrological and biogeochemical modellingand upscaling efforts for the northern boreal and arctic region, inparticular those aimed at improving assessments of current and futuremethane emissions. Data are freely available athttps://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).« less