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Abstract Tundra fires can dramatically influence plant species cover and abundance, organic layer depth, and the magnitude of seasonal permafrost thaw. However, knowledge of the impact of wildfire on short and long-term interactions between vegetation and permafrost thaw remains limited. Here, we evaluate the spatial and temporal interactions between wildfire disturbance and surface subsidence on a remotely derived proxy for species diversity (i.e. spectral diversity (SD)) of 16 fire scars within the Izaviknek and Kingaglia uplands of southwestern Alaska’s Yukon–Kuskokwim Delta with burn dates between 1971 and 2015. SD was calculated as the sum of squared spectral variance of pixel spectra from the mean spectra, within a plant community (analogous to alpha diversity), between plant communities (beta diversity), and across terrain composed of a mosaic of communities (gamma diversity). Surface subsidence was calculated from spaceborne interferometric synthetic aperture radar data from Sentinel-1. Results indicate the burn scars had consistently lower total gamma diversity and greater rates of subsidence than paired unburned reference areas, where both gamma diversity (R²= 0.74,p< 0.001) and relative subsidence (R²= 0.86,p< 0.001) decreased with the time since burn. Compared to older burn scars, young scars had higher gamma spectral diversities (0.013 and 0.005) and greater subsidence rates (−0.097 cm day⁻¹and −0.053 cm day⁻¹). Communities subsiding at higher rates had higher gamma diversities (R²= 0.81,p< 0.001). Results indicate that rates of post-fire vegetation succession are amplified by the thickening of active layers and surface subsidence that increases both spectral and species diversity over 15 years following fire. These results support the idea that SD may be used as a remotely sensed analog of species diversity, used to advance knowledge of the trajectories of plant community change in response to interacting arctic disturbance regimes. 

ABSTRACT Deep‐learning (DL) models have become increasingly beneficial for the detection of retrogressive thaw slumps (RTS) in the permafrost domain. However, comparing accuracy metrics is challenging due to unstandardized labeling guidelines. To address this, we conducted an experiment with 12 international domain experts from a broad range of scientific backgrounds. Using 3 m PlanetScope multispectral imagery, they digitized RTS footprints in two sites. We evaluated label uncertainty by comparing manually outlined RTS labels using Intersection‐over‐Union (IoU) and F1 metrics. At the Canadian Peel Plateau site, we see good agreement, particularly in the active parts of RTS. Differences were observed in the interpretation of the debris tongue and the stable vegetated sections of RTS. At the Russian Bykovsky site, we observed a larger mismatch. Here, the same differences were documented, but several participants mistakenly identified non‐RTS features. This emphasizes the importance of site‐specific knowledge for reliable label creation. The experiment highlights the need for standardized labeling procedures and definition of their scientific purpose. The most similar expert labels outperformed the accuracy metrics reported in the literature, highlighting human labeling capabilities with proper training, site knowledge, and clear guidelines. These findings lay the groundwork for DL‐based RTS monitoring in the pan‐Arctic. 

Abstract Tall deciduous shrubs are critically important to carbon and nutrient cycling in high-latitude ecosystems. As Arctic regions warm, shrubs expand heterogeneously across their ranges, including within unburned terrain experiencing isometric gradients of warming. To constrain the effects of widespread shrub expansion in terrestrial and Earth System Models, improved knowledge of local-to-regional scale patterns, rates, and controls on decadal shrub expansion is required. Using fine-scale remote sensing, we modeled the drivers of patch-scale tall-shrub expansion over 68 years across the central Seward Peninsula of Alaska. Models show the heterogeneous patterns of tall-shrub expansion are not only predictable but have an upper limit defined by permafrost, climate, and edaphic gradients, two-thirds of which have yet to be colonized. These observations suggest that increased nitrogen inputs from nitrogen-fixing alders contributed to a positive feedback that advanced overall tall-shrub expansion. These findings will be useful for constraining and projecting vegetation-climate feedbacks in the Arctic. 

Abstract Earlier snowmelt, warmer temperatures and herbivory are among the factors that influence high-latitude tundra productivity near the town of Utqiaġvik in northern Alaska. However, our understanding of the potential interactions between these factors is limited. MODIS observations provide cover fractions of vegetation, snow, standing water, and soil, and fractional absorption of photosynthetically active radiation by canopy chlorophyll (fAPAR_chl) per pixel. Here, we evaluated a recent time-period (2001–2014) that the tundra experienced large interannual variability in vegetation productivity metrics (i.e. fAPAR_chland APAR_chl), which was explainable by both abiotic and biotic factors. We found earlier snowmelt to increase soil and vegetation cover, and productivity in June, while warmer temperatures significantly increased monthly productivity. However, abiotic factors failed to explain stark decreases in productivity during August of 2008, which coincided with a severe lemming outbreak. MODIS observations found this tundra ecosystem to completely recover two years later, resulting in elevated productivity. This study highlights the potential roles of both climate and herbivory in modulating the interannual variability of remotely retrieved plant productivity metrics in Arctic coastal tundra ecosystems. 

Abstract Lakes represent as much as ∼25% of the total land surface area in lowland permafrost regions. Though decreasing lake area has become a widespread phenomenon in permafrost regions, our ability to forecast future patterns of lake drainage spanning gradients of space and time remain limited. Here, we modeled the drivers of gradual (steady declining lake area) and catastrophic (temporally abrupt decrease in lake area) lake drainage using 45 years of Landsat observations (i.e. 1975–2019) across 32 690 lakes spanning climate and environmental gradients across northern Alaska. We mapped lake area using supervised support vector machine classifiers and object based image analyses using five-year Landsat image composites spanning 388 968 km². Drivers of lake drainage were determined with boosted regression tree models, using both static (e.g. lake morphology, proximity to drainage gradient) and dynamic predictor variables (e.g. temperature, precipitation, wildfire). Over the past 45 years, gradual drainage decreased lake area between 10% and 16%, but rates varied over time as the 1990s recorded the highest rates of gradual lake area losses associated with warm periods. Interestingly, the number of catastrophically drained lakes progressively decreased at a rate of ∼37% decade⁻¹from 1975–1979 (102–273 lakes draining year⁻¹) to 2010–2014 (3–8 lakes draining year⁻¹). However this 40 year negative trend was reversed during the most recent time-period (2015–2019), with observations of catastrophic drainage among the highest on record (i.e. 100–250 lakes draining year⁻¹), the majority of which occurred in northwestern Alaska. Gradual drainage processes were driven by lake morphology, summer air and lake temperature, snow cover, active layer depth, and the thermokarst lake settlement index (R²_adj= 0.42, CV = 0.35,p< 0.0001), whereas, catastrophic drainage was driven by the thawing season length, total precipitation, permafrost thickness, and lake temperature (R²_adj= 0.75, CV = 0.67,p< 0.0001). Models forecast a continued decline in lake area across northern Alaska by 15%–21% by 2050. However these estimates are conservative, as the anticipated amplitude of future climate change were well-beyond historical variability and thus insufficient to forecast abrupt ‘catastrophic’ drainage processes. Results highlight the urgency to understand the potential ecological responses and feedbacks linked with ongoing Arctic landscape reorganization. 

Abstract The expansion of shrubs across the Arctic tundra may fundamentally modify land–atmosphere interactions. However, it remains unclear how shrub expansion pattern is linked with key environmental drivers, such as climate change and fire disturbance. Here we used 40+ years of high‐resolution (~1.0 m) aerial and satellite imagery to estimate shrub‐cover change in 114 study sites across four burned and unburned upland (ice‐poor) and lowland (ice‐rich) tundra ecosystems in northern Alaska. Validated with data from four additional upland and lowland tundra fires, our results reveal that summer precipitation was the most important climatic driver (r = 0.67,p < 0.001), responsible for 30.8% of shrub expansion in the upland tundra between 1971 and 2016. Shrub expansion in the uplands was largely enhanced by wildfire (p < 0.001) and it exhibited positive correlation with fire severity (r = 0.83,p < 0.001). Three decades after fire disturbance, the upland shrub cover increased by 1077.2 ± 83.6 m² ha⁻¹, ~7 times the amount identified in adjacent unburned upland tundra (155.1 ± 55.4 m² ha⁻¹). In contrast, shrub cover markedly decreased in lowland tundra after fire disturbance, which triggered thermokarst‐associated water impounding and resulted in 52.4% loss of shrub cover over three decades. No correlation was found between lowland shrub cover with fire severity (r = 0.01). Mean summer air temperature (MSAT) was the principal factor driving lowland shrub‐cover dynamics between 1951 and 2007. Warmer MSAT facilitated shrub expansion in unburned lowlands (r = 0.78,p < 0.001), but accelerated shrub‐cover losses in burned lowlands (r = −0.82,p < 0.001). These results highlight divergent pathways of shrub‐cover responses to fire disturbance and climate change, depending on near‐surface permafrost and drainage conditions. Our study offers new insights into the land–atmosphere interactions as climate warming and burning intensify in high latitudes. 

Delineations of Retrogressive Thaw Slump (RTS) expansion and light detection and ranging (LiDAR) datasets (LAS files) of RTS sites were used to model how rates of RTS change are influenced by topographic and climatic characteristics across northern Alaska. LiDAR data were collected at ten sites, where five were collected from an uncrewed aerial system (UAS) and five were collected from a terrestrial LiDAR systems (TLS). LiDAR datasets were used to bias correct the open-source ArcticDEM (2 meter-resolution) for calculating annual rates of RTS volumetric losses across all sites. RTS Delineations were used to calculate annual rates of RTS areal expansion and summarize topographic characteristics calculated from the corrected ArcticDEM. Two shapefiles were created from historic satellite and aerial imagery (1949-2021) to summarize RTS areal change across 44 slumps: AK_RTS_ExansionDelineations.shp summarizes the area of RTS expansion between two time periods and AK_RTS_Delineations.shp summarizes the total RTS outline in each year where RTS expansion occurs. LiDAR UAS and TLS data are provided as LAS files from 12 slumps (five sites) near Toolik Lake and 9 slumps (5 sites) within the Noatak National Preserve. 

Tall deciduous shrubs are critically important to carbon and nutrient cycling in high-latitude ecosystems. As Arctic regions warm, shrubs expand heterogeneously across their ranges, including within unburned terrain experiencing isometric gradients of warming. To constrain the effects of widespread shrub expansion in terrestrial and Earth System Models, improved knowledge of local to regional-scale patterns, rates, and controls on decadal shrub expansion is required. Here we map tall deciduous shrub canopies in the central Seward Peninsula of Alaska in 1950 using ~1 meter (m)-resolution aerial photographs from US Navy missions in three subsites (1950ShrubClass.tif and 1950AlderClass.tif) and in 2018 using 3m-resolution PlanetScope satellite imagery for the entire study region (SummerShrubExtent.tif and AlderExtent2017.tif). The timing of alder shrub senescence allowed us to separate the classification into alder and non-alder categories. We computed two change maps: one exclusively for alder and one including all deciduous tall shrubs. The change maps were modeled against a suite of environmental factors and the shrub change model was extended across the study region (SewardShrub.tif). The model was rerun for future scenarios with 10 (SewardMinus10PF.tif) and 30 (SewardMinus30PF.tif) percent reductions in permafrost probability to determine the likely effects of permafrost degradation on shrub extent. 

Peatlands cover 3% of the global land surface, yet store 25% of the world’s soil organic carbon. These organic-rich soils are widespread across permafrost regions, representing nearly 18% of land surface and storing between 500 and 600 petagrams of carbon (PgC). Peat (i.e., partially decomposed thick organic layers) accumulates due to the imbalance between plant production and decomposition often within saturated, nutrient deficient, and acidic soils, which limit decomposition. As warmer and drier conditions become more prevalent across northern ecosystems, the vulnerability of peatland soils may increase with the susceptibility of peat-fire ignitions, yet the distribution of peatlands across Alaska remains uncertain. Here we develop a new high-resolution (20 meter (m) resolution) wall-to-wall ~1.5 million square kilometer (km2) peatland map of Alaska, using a combination of Sentinel-1 (Dual-polarized Synthetic Aperture Radar), Sentinel-2 (Multi-Spectral Imager), and derivatives from the Arctic Digital Elevation Model (ArcticDEM). Machine learning classifiers were trained and tested using peat cores, ground observations, and sub-meter resolution image interpretation, which was spatially constrained by a peatland suitability model that described the extent of terrain suitable for peat accumulation. This product identifies peatlands in Polar, Boreal, and Maritime ecoregions in Alaska to cover 26,842 (4.6%), 69,783 (10.4%), and 13,506 (5.3%) km2, respectively.