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Alaska’s Yukon–Kuskokwim Delta (YKD) is among the Arctic’s warmest, most biologically productive regions, but regional decline of the normalized difference vegetation index (NDVI) has been a striking feature of spaceborne Advanced High Resolution Radiometer (AVHRR) observations since 1982. This contrast with “greening” prevalent elsewhere in the low Arctic raises questions concerning climatic and biophysical drivers of tundra productivity along maritime–continental gradients. We compared NDVI time series from AVHRR, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat for 2000–19 and identified trend drivers with reference to sea ice and climate datasets, ecosystem and disturbance mapping, field measurements of vegetation, and knowledge exchange with YKD elders. All time series showed increasing maximum NDVI; however, whereas MODIS and Landsat trends were very similar, AVHRR-observed trends were weaker and had dissimilar spatial patterns. The AVHRR and MODIS records for time-integrated NDVI were dramatically different; AVHRR indicated weak declines, whereas MODIS indicated strong increases throughout the YKD. Disagreement largely arose from observations during shoulder seasons, when there is partial snow cover and very high cloud frequency. Nonetheless, both records shared strong correlations with spring sea ice extent and summer warmth. Multiple lines of evidence indicate that, despite frequent disturbances and high interannual variability in spring sea ice and summer warmth, tundra productivity is increasing on the YKD. Although climatic drivers of tundra productivity were similar to more continental parts of the Arctic, our intercomparison highlights sources of uncertainty in maritime areas like the YKD that currently, or soon will, challenge historical concepts of “what is Arctic.”

Integrated Terrain Unit (ITU) mapping is a technique used to develop maps depicting specific ecosystem services and landscape sensitivity measures useful for land-use planning and sampling designs for spatially stratified studies. Here we describe ITU classifications, mapping techniques, and geospatial products developed from 1992 to 2018 for arctic tundra in northern Alaska in areas of ongoing oil production and exploration. We used data from 1779 field plots to classify and map geomorphology, surface form, vegetation, and disturbance history. We then derived map products for ecotypes, wildlife habitats, and U.S. National Wetland Inventory (NWI) wetlands by aggregating functionally similar ITU combinations. We evaluated the extent to which ground conditions may have changed in older portions of the ITU mapping using a map of Landsat-observed trends in Normalized Difference Vegetation Index (NDVI) for 1985–2012. The cumulative result of 26 years of study is a 3532.4 km2 area of ITU mapping. The ITU code combinations were aggregated into 66 map ecotypes, 34 wildlife habitats, and 18 wetland classes. Most of the mapping area (65.4%) experienced no significant trend in vegetation greenness. Nearly all of the remaining area (34.6%) exhibited an increase in greenness. The ITU mapping provides a baseline for monitoring future change against a backdrop of climate change and ongoing industrial activity. The map products serve as proxies for important subsurface characteristics in permafrost landscapes, and have facilitated seminal studies of landscape development and change in arctic Alaska.

Tundra vegetation productivity and composition are responding rapidly to climatic changes in the Arctic. These changes can, in turn, mitigate or amplify permafrost thaw. In this Review, we synthesize remotely sensed and field-observed vegetation change across the tundra biome, and outline how these shifts could influence permafrost thaw. Permafrost ice content appears to be an important control on local vegetation changes; woody vegetation generally increases in ice-poor uplands, whereas replacement of woody vegetation by (aquatic) graminoids following abrupt permafrost thaw is more frequent in ice-rich Arctic lowlands. These locally observed vegetation changes contribute to regional satellite-observed greening trends, although the interpretation of greening and browning is complicated. Increases in vegetation cover and height generally mitigate permafrost thaw in summer, yet, increase annual soil temperatures through snow-related winter soil warming effects. Strong vegetation–soil feedbacks currently alleviate the consequences of thaw-related disturbances. However, if the increasing scale and frequency of disturbances in a warming Arctic exceeds the capacity for vegetation and permafrost recovery, changes to Arctic ecosystems could be irreversible. To better disentangle vegetation–soil–permafrost interactions, ecological field studies remain crucial, but require better integration with geophysical assessments.

The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km2). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ∼1250 km2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ∼65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement.

Accurate identification of the relationships between permafrost extent and landscape patterns helps develop airborne geophysical or remote sensing tools to map permafrost in remote locations or across large areas. These tools are particularly applicable in discontinuous permafrost where climate warming or disturbances such as human development or fire can lead to rapid permafrost degradation. We linked field-based geophysical, point-scale, and imagery surveying measurements to map permafrost at five fire scars on the Tanana Flats in central Alaska. Ground-based elevation surveys, seasonal thaw-depth profiles, and electrical resistivity tomography (ERT) measurements were combined with airborne imagery and light detection and ranging (LiDAR) to identify relationships between permafrost geomorphology and elapsed time since fire disturbance. ERT was a robust technique for mapping the presence or absence of permafrost because of the marked difference in resistivity values for frozen versus unfrozen material. There was no clear relationship between elapsed time since fire and permafrost extent at our sites. The transition zone boundaries between permafrost soils and unfrozen soils in the collapse-scar bogs at our sites had complex and unpredictable morphologies, suggesting attempts to quantify the presence or absence of permafrost using aerial measurements alone could lead to incomplete results. The results from our study indicated limitations in being able to apply airborne surveying measurements at the landscape scale toward accurately estimating permafrost extent.

Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.

Soil respiration (i.e. from soils and roots) provides one of the largest global fluxes of carbon dioxide (CO2) to the atmosphere and is likely to increase with warming, yet the magnitude of soil respiration from rapidly thawing Arctic-boreal regions is not well understood. To address this knowledge gap, we first compiled a new CO2 flux database for permafrost-affected tundra and boreal ecosystems in Alaska and Northwest Canada. We then used the CO2 database, multi-sensor satellite imagery, and random forest models to assess the regional magnitude of soil respiration. The flux database includes a new Soil Respiration Station network of chamber-based fluxes, and fluxes from eddy covariance towers. Our site-level data, spanning September 2016 to August 2017, revealed that the largest soil respiration emissions occurred during the summer (June–August) and that summer fluxes were higher in boreal sites (1.87 ± 0.67 g CO2–C m−2 d−1) relative to tundra (0.94 ± 0.4 g CO2–C m−2 d−1). We also observed considerable emissions (boreal: 0.24 ± 0.2 g CO2–C m−2 d−1; tundra: 0.18 ± 0.16 g CO2–C m−2 d−1) from soils during the winter (November–March) despite frozen surface conditions. Our model estimates indicated an annual region-wide loss from soil respiration of 591 ± 120 Tg CO2–C during the 2016–2017 period. Summer months contributed to 58% of the regional soil respiration, winter months contributed to 15%, and the shoulder months contributed to 27%. In total, soil respiration offset 54% of annual gross primary productivity (GPP) across the study domain. We also found that in tundra environments, transitional tundra/boreal ecotones, and in landscapes recently affected by fire, soil respiration often exceeded GPP, resulting in a net annual source of CO2 to the atmosphere. As this region continues to warm, soil respiration may increasingly offset GPP, further amplifying global climate change.

The Arctic is warming at double the rate of the rest of the Earth. This is causing rapid biophysical changes that are stressing ecosystems that have evolved to survive the perennially frozen conditions, strengthening positive feedbacks to the global climate system that are accelerating global warming, and challenging the capacity of society to adapt and mitigate the environmental and health effects of a changing Arctic. The Arctic is especially vulnerable because it encompasses most of the Earth’s cryosphere of sea ice, snow, glaciers, and permafrost, which have a definitive tipping point associated with phase change of melting ice. This makes the biome a harbinger for assessing the consequences of global warming on ecosystem and societal health. In this chapter, the importance of the Arctic is examined within a global context by summarizing the rapid environmental and societal changes that are occurring, identifying how changes in the Arctic provide strong feedbacks to the global climate system, and how environmental and health policy is mitigating, or failing to mitigate, the effects of a changing Arctic.

Although three‐dimensional (3D) seismic surveys have improved the success rate of exploratory drilling for oil and gas, the impacts have received little scientific scrutiny, despite affecting more area than any other oil and gas activity. To aid policy‐makers and scientists, we reviewed studies of the landscape impacts of 3D‐seismic surveys in the Arctic. We analyzed a proposed 3D‐seismic program in northeast Alaska, in the northern Arctic National Wildlife Refuge, which includes a grid 63,000 km of seismic trails and additional camp‐move trails. Current regulations are not adequate to eliminate impacts from these activities. We address issues related to the high‐density of 3D trails compared to 2D methods, with larger crews, more camps, and more vehicles. We focus on consequences to the hilly landscapes, including microtopography, snow, vegetation, hydrology, active layers, and permafrost. Based on studies of 2D‐seismic trails created in 1984–1985 in the same area by similar types of vehicles, under similar regulations, approximately 122 km2 would likely sustain direct medium‐ to high‐level disturbance from the proposed exploration, with possibly expanded impacts through permafrost degradation and hydrological connectivity. Strong winds are common, and snow cover necessary to minimize impacts from vehicles is windblown and inadequate to protect much of the area. Studies of 2D‐seismic impacts have shown that moist vegetation types, which dominate the area, sustain longer‐lasting damage than wet or dry types, and that the heavy vehicles used for mobile camps caused the most damage. The permafrost is ice rich, which combined with the hilly topography, makes it especially susceptible to thermokarst and erosion triggered by winter vehicle traffic. The effects of climate warming will exacerbate the impacts of winter travel due to warmer permafrost and a shift of precipitation from snow to rain. The cumulative impacts of 3D‐seismic traffic in tundra areas need to be better assessed, together with the effects of climate change and the industrial development that would likely follow. Current data needs include studies of the impacts of 3D‐seismic exploration, better climate records for the Arctic National Wildlife Refuge, especially for wind and snow; and high‐resolution maps of topography, ground ice, hydrology, and vegetation.

State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellite remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs.

Alaska has diverse boreal ecosystems across heterogeneous landscapes driven by a wide range of biological and geomorphic processes associated with disturbance and successional patterns under a changing climate. To assess historical patterns and rates of change, we quantified the areal extent of ecotypes and the biophysical factors driving change through photo-interpretation of 2200 points on a time-series (∼1949, ∼1978, ∼2007, ∼2017) of geo-rectified imagery for 22 grids across central Alaska. Overall, 68.6% of the area had changes in ecotypes over ∼68 years. Most of the change resulted from increases in upland and lowland forest types, with an accompanying decrease in upland and lowland scrub types, as post-fire succession led to mid- and late-successional stages. Of 17 drivers of landscape change, fire was by far the largest, affecting 46.5% of the region overall from 1949 to 2017. Fire was notably more extensive in the early 1900s. Thermokarst nearly doubled from 3.9% in 1949 to 6.3% in 2017. Riverine ecotypes covered 7.8% area and showed dynamic changes related to channel migration and succession. Using past rates of ecotype transitions, we developed four state-transition models to project future ecotype extent based on historical rates, increasing temperatures, and driver associations. Ecotype changes from 2017 to 2100, nearly tripled for the driver-adjusted RCP6.0 temperature model (30.6%) compared to the historical rate model (11.5%), and the RCP4.5 (12.4%) and RCP8.0 (14.7%) temperature models. The historical-rate model projected 38 ecotypes will gain area and 24 will lose area by 2100. Overall, disturbance and recovery associated with a wide range of drivers across the patchy mosaic of differing aged ecotypes led to a fairly stable overall composition of most ecotypes over long intervals, although fire caused large temporal fluctuations for many ecotypes. Thermokarst, however, is accelerating and projected to have increasingly transformative effects on future ecotype distributions.

The stability of permafrost is of fundamental importance to socio-economic well-being and ecological services, involving broad impacts to hydrological cycling, global budgets of greenhouse gases and infrastructure safety. This study presents a biophysical permafrost zonation map that uses a rule-based geographic information system (GIS) model integrating global climate and ecological datasets to classify and map permafrost regions (totaling 19.76 × 106 km2, excluding glaciers and lakes) in the Northern Hemisphere into five types: climate-driven (CD) (19% of area), CD/ecosystem-modified (41%), CD/ecosystem protected (3%), ecosystem-driven (29%), and ecosystem-protected (8%). Overall, 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai–Tibet Plateau. This highlights the importance of reducing ecosystem disturbances (natural and human activity) to help slow permafrost degradation and lower the related risks from a warming climate.

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using AVHRR and MODIS data (reflectance and NDVI) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the U.S. The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-km pixels interpreted from 6 Landsat scenes showed an average of 70% accuracy, up from 39% for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems. The final product is publicly available at www.geobotany.uaf.edu and at Mendeley Data, DOI: 10.17632/c4xj5rv6kv.1 .

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems.

Accurate identification of the relationships between permafrost extent and landscape patterns can help to develop airborne geophysical or remote sensing tools to map permafrost in remote locations or across large areas. These tools will be particularly applicable in discontinuous permafrost where climate warming or disturbances such as human development or fire can lead to rapid permafrost degradation. We have linked field-based geophysical, point-scale, and imagery surveying measurements to map permafrost at five fire scars (1930, 1975, 1988, 2001, and 2010) on the Tanana Flats in central Alaska. Ground-based elevation surveys, seasonal thaw-depth profiles, and electrical resistivity tomography (ERT) measurements were combined with airborne imagery and light detection and ranging (LiDAR) to identify relationships between permafrost geomorphology and elapsed time since fire disturbance. ERT proved to be a robust technique for mapping the presence or absence of permafrost because of the marked difference in resistivity values for frozen versus unfrozen material. There was no clear relationship between elapsed time since fire and permafrost extent at our sites. However, we have found that the transition zone boundaries between permafrost soils and unfrozen soils in the collapse-scar bogs at our sites had complex and unpredictable morphologies. This result suggested that attempts to quantify the presence or absence of permafrost using aerial measurements alone could lead to incomplete results. Taken in total, the results from our study indicated that although ground-based ERT measurements were the most rapid means of mapping permafrost, we were still limited in being able to apply airborne surveying measurements at the landscape scale toward accurately estimating permafrost extent.

Rapid warming has occurred over the past 50 years in Arctic Alaska, where temperature strongly affects ecological patterns and processes. To document landscape change over a half century in the Arctic National Wildlife Refuge, Alaska, we visually interpreted geomorphic and vegetation changes on time series of coregistered high-resolution imagery. We used aerial photographs for two time periods, 1947–1955 and 1978–1988, and Quick Bird and IKONOS satellite images for a third period, 2000–2007. The stratified random sample had five sites in each of seven ecoregions, with a systematic grid of 100 points per site. At each point in each time period, we recorded vegetation type, microtopography, and surface water. Change types were then assigned based on differences detected between the images. Overall, 23% of the points underwent some type of change over the ~50-year study period. Weighted by area of each ecoregion, we estimated that 18% of the Refuge had changed. The most common changes were wildfire and postfire succession, shrub and tree increase in the absence of fire, river erosion and deposition, and ice-wedge degradation. Ice-wedge degradation occurred mainly in the Tundra Biome, shrub increase and river changes in the Mountain Biome, and fire and postfire succession in the Boreal Biome. Changes in the Tundra Biome tended to be related to landscape wetting, mainly from increased wet troughs caused by ice-wedge degradation. The Boreal Biome tended to have changes associated with landscape drying, including recent wildfire, lake area decrease, and land surface drying. The second time interval, after ~1982, coincided with accelerated climate warming and had slightly greater rates of change.

The Yukon-Kuskokwim Delta (YKD) is the largest delta in western North America and its productive coastal ecosystems support globally significant populations of breeding birds and a large indigenous population. To quantify past landscape changes as a guide to assessing future climate impacts to the YKD and how indigenous society may adapt to change, we photo-interpreted ecotypes at 600 points within 12 grids in a 2118 km2 area along the central YKD coast using a time-series of air photos from 1948–1955 and 1980 and satellite images from 2007–2008 (IKONOS) and 2013–2016 (WorldView). We found that ecotype classes changed 16.2% (342 km2) overall during the ~62 years. Ecotypes changed 6.0% during 1953–1980, 7.2% during 1980–2007 and 3.8% during 2007–2015. Lowland Moist Birch-Ericaceous Low Scrub (−5.0%) and Coastal Saline Flat Barrens (−2.3%) showed the greatest decreases in area, while Lowland Water Sedge Meadow (+1.7%) and Lacustrine Marestail Marsh (+1.3%) showed the largest increases. Dominant processes affecting change were permafrost degradation (5.3%), channel erosion (3.0%), channel deposition (2.2%), vegetation colonization (2.3%) and lake drainage (1.5%), while sedimentation, water-level fluctuations, permafrost aggradation and shoreline paludification each affected

High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost, which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map near-surface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of ~ 85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies.

We examined the effects of fire disturbance on permafrost degradation and thaw settlement across a series of wildfires (from ~1930 to 2010) in the forested areas of collapse-scar bog complexes in the Tanana Flats lowland of interior Alaska. Field measurements were combined with numerical modeling of soil thermal dynamics to assess the roles of fire severity and climate history in postfire permafrost dynamics. Field-based calculations of potential thaw settlement following the loss of remaining ice-rich permafrost averaged 0.6 m. This subsidence would cause the surface elevations of forests to drop on average 0.1 m below the surface water level of adjacent collapse-scar features. Up to 0.5 m of thaw settlement was documented after recent fires, causing water impoundment and further thawing along forest margins. Substantial heterogeneity in soil properties (organic layer thickness, texture, moisture, and ice content) was attributed to differing site histories, which resulted in distinct soil thermal regimes by soil type. Model simulations showed increasing vulnerability of permafrost to deep thawing and thaw settlement with increased fire severity (i.e., reduced organic layer thickness). However, the thresholds of fire severity that triggered permafrost destabilization varied temporally in response to climate. Simulated permafrost dynamics underscore the importance of multiyear to multidecadal fluctuations in air temperature and snow depth in mediating the effects of fire on permafrost. Our results suggest that permafrost is becoming increasingly vulnerable to substantial thaw and collapse after moderate to high-severity fire, and the ability of permafrost to recover is diminishing as the climate continues to warm.

We project the effects of transitional changes among 60 vegetation and other land cover types (“ecotypes”) in northwest Alaska over the 21st century on habitats of 162 bird and 39 mammal species known or expected to occur regularly in the region. This analysis, encompassing a broad suite of arctic and boreal wildlife species, entailed building wildlife-habitat matrices denoting levels of use of each ecotype by each species, and projecting habitat changes under historic and expected accelerated future rates of change from increasing mean annual air temperature based on the average of 5 global climate models under the A1B emissions scenario, and from potential influence of a set of 23 biophysical drivers. Under historic rates of change, we project that 52 % of the 201 species will experience an increase in medium- and high-use habitats, 3 % no change, and 45 % a decrease, and that a greater proportion of mammal species (62 %) will experience habitat declines than will bird species (50 %). Outcomes become more dire (more species showing habitat loss) under projections made from effects of biophysical drivers and especially from increasing temperature, although species generally associated with increasing shrub and tree ecotypes will likely increase in distribution. Changes in wildlife habitats likely will also affect trophic cascades, ecosystem function, and ecosystem services; of particular significance are the projected declines in habitats of most small mammals that form the prey base for mesocarnivores and raptors, and habitat declines in 25 of the 50 bird and mammal species used for subsistence hunting and trapping.

Modern climate change in Alaska has resulted in widespread thawing of permafrost, increased fire activity, and extensive changes in vegetation characteristics that have significant consequences for socioecological systems. Despite observations of the heightened sensitivity of these systems to change, there has not been a comprehensive assessment of factors that drive ecosystem changes throughout Alaska. Here we present research that improves our understanding of the main drivers of the spatiotemporal patterns of carbon dynamics using in situ observations, remote sensing data, and an array of modeling techniques. In the last 60 yr, Alaska has seen a large increase in mean annual air temperature (1.7°C), with the greatest warming occurring over winter and spring. Warming trends are projected to continue throughout the 21st century and will likely result in landscape-level changes to ecosystem structure and function. Wetlands, mainly bogs and fens, which are currently estimated to cover 12.5% of the landscape, strongly influence exchange of methane between Alaska's ecosystems and the atmosphere and are expected to be affected by thawing permafrost and shifts in hydrology. Simulations suggest the current proportion of near-surface (within 1 m) and deep (within 5 m) permafrost extent will be reduced by 9-74% and 33-55% by the end of the 21st century, respectively. Since 2000, an average of 678 595 ha/yr was burned, more than twice the annual average during 1950-1999. The largest increase in fire activity is projected for the boreal forest, which could result in a reduction in late-successional spruce forest (8-44%) and an increase in early-successional deciduous forest (25-113%) that would mediate future fire activity and weaken permafrost stability in the region. Climate warming will also affect vegetation communities across arctic regions, where the coverage of deciduous forest could increase (223-620%), shrub tundra may increase (4-21%), and graminoid tundra might decrease (10-24%). This study sheds light on the sensitivity of Alaska's ecosystems to change that has the potential to significantly affect local and regional carbon balance, but more research is needed to improve estimates of land-surface and subsurface properties, and to better account for ecosystem dynamics affected by a myriad of biophysical factors and interactions.

Understanding of the organic layer thickness (OLT) and organic layer carbon (OLC) stocks in subarctic ecosystems is critical due to their importance in the global carbon cycle. Moreover, post-fire OLT provides an indicator of long-term successional trajectories and permafrost susceptibility to thaw. To these ends, we 1) mapped OLT and associated uncertainty at 30 m resolution in the Yukon River Basin (YRB), Alaska, employing decision tree models linking remotely sensed imagery with field and ancillary data, 2) converted OLT to OLC using a non-linear regression, 3) evaluate landscape controls on OLT and OLC, and 4) quantified the post-fire recovery of OLT and OLC. Areas of shallow ( R 2 = 0.68; OLC: R 2 = 0.66), where an average of 16 cm OLT and 5.3 kg/m 2 OLC were consumed by fires. Strong predictors of OLT included climate, topography, near-surface permafrost distributions, soil wetness, and spectral information. Our modeling approach enabled us to produce regional maps of OLT and OLC, which will be useful in understanding risks and feedbacks associated with fires and climate feedbacks.

The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.

Machine-learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at-sensor reflectance, thermal, TM-derived spectral indices, digital elevation models and other relevant spatial data to estimate near-surface (0–2.6-m depth) resistivity at 30-m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active-layer thickness (ALT) (< 101 cm) and the probability of near-surface (up to 123-cm depth) permafrost occurrence from field data, modelled near-surface (0–2.6 m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher-elevation loess deposits than on low-lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Permafrost landscape responses to climate change and disturbance impact local ecology and global greenhouse gas concentrations, but the nature and magnitude of response is linked with vegetation, terrain and permafrost properties that vary markedly across landscapes. As a subsurface property, permafrost conditions are difficult to characterize across landscapes, and modeled estimates rely upon relationships among permafrost characteristics and surface properties. While a general relationship among landscape and permafrost properties has been recognized throughout the Arctic, the nature of these relationships is poorly documented in many regions, limiting modeling capability. We examined relationships among terrain, vegetation and permafrost within the Brooks Range and foothills of northern Alaska using field data from diverse sites and multiple factor analysis ordination. Terrain, vegetation and permafrost conditions were correlated throughout the region, with field sites falling into four statistically-separable groups based on ordination results. Our results identify index variables for honing field sampling and statistical analysis, illustrate the nature of relationships in the region, support future modeling of permafrost properties, and suggest a state factor approach for organizing data and ideas relevant for modeling of permafrost properties at a regional scale.

The Alaska Arctic Vegetation Archive (AVA-AK, GIVD-ID: NA-US-014) is a free, publically available database archive of vegetation-plot data from the Arctic tundra region of northern Alaska. The archive currently contains 24 datasets with 3,026 non-overlapping plots. Of these, 74% have geolocation data with 25-m or better precision. Species cover data and header data are stored in a Turboveg database. A standardized Pan Arctic Species List provides a consistent nomenclature for vascular plants, bryophytes, and lichens in the archive. A web-based online Alaska Arctic Geoecological Atlas (AGA-AK) allows viewing and downloading the species data in a variety of formats, and provides access to a wide variety of ancillary data. We conducted a preliminary cluster analysis of the first 16 datasets (1,613 plots) to examine how the spectrum of derived clusters is related to the suite of datasets, habitat types, and environmental gradients. Here, we present the contents of the archive, assess its strengths and weaknesses, and provide three supplementary files that include the data dictionary, a list of habitat types, an overview of the datasets, and details of the cluster analysis.

Recent warming at high-latitudes has accelerated permafrost thaw in northern peatlands, and thaw can have profound effects on local hydrology and ecosystem carbon balance. To assess the impact of permafrost thaw on soil organic carbon (OC) dynamics, we measured soil hydrologic and thermal dynamics and soil OC stocks across a collapse-scar bog chronosequence in interior Alaska. We observed dramatic changes in the distribution of soil water associated with thawing of ice-rich frozen peat. The impoundment of warm water in collapse-scar bogs initiated talik formation and the lateral expansion of bogs over time. On average, Permafrost Plateaus stored 137 ± 37 kg C m -2 , whereas OC storage in Young Bogs and Old Bogs averaged 84 ± 13 kg C m -2 . Based on our reconstructions, the accumulation of OC in near-surface bog peat continued for nearly 1,000 years following permafrost thaw, at which point accumulation rates slowed. Rapid decomposition of thawed forest peat reduced deep OC stocks by nearly half during the first 100 years following thaw. Using a simple mass-balance model, we show that accumulation rates at the bog surface were not sufficient to balance deep OC losses, resulting in a net loss of OC from the entire peat column. An uncertainty analysis also revealed that the magnitude and timing of soil OC loss from thawed forest peat depends substantially on variation in OC input rates to bog peat and variation in decay constants for shallow and deep OC stocks. These findings suggest that permafrost thaw and the subsequent release of OC from thawed peat will likely reduce the strength of northern permafrost-affected peatlands as a carbon dioxide sink, and consequently, will likely accelerate rates of atmospheric warming.

The direction and magnitude of soil organic carbon (SOC) changes in response to climate change remain unclear and depend on the spatial distribution of SOC across landscapes. Uncertainties regarding the fate of SOC are greater in high-latitude systems where data are sparse and the soils are affected by sub-zero temperatures. To address these issues in Alaska, a first-order assessment of data gaps and spatial distributions of SOC was conducted from a recently compiled soil carbon database. Temperature and landform type were the dominant controls on SOC distribution for selected ecoregions. Mean SOC pools (to a depth of 1-m) varied by three, seven and ten-fold across ecoregion, landform, and ecosystem types, respectively. Climate interactions with landform type and SOC were greatest in the uplands. For upland SOC there was a six-fold non-linear increase in SOC with latitude (i.e., temperature) where SOC was lowest in the Intermontane Boreal compared to the Arctic Tundra and Coastal Rainforest. Additionally, in upland systems mineral SOC pools decreased as climate became more continental, suggesting that the lower productivity, higher decomposition rates and fire activity, common in continental climates, interacted to reduce mineral SOC. For lowland systems, in contrast, these interactions and their impacts on SOC were muted or absent making SOC in these environments more comparable across latitudes. Thus, the magnitudes of SOC change across temperature gradients were non-uniform and depended on landform type. Additional factors that appeared to be related to SOC distribution within ecoregions included stand age, aspect, and permafrost presence or absence in black spruce stands. Overall, these results indicate the influence of major interactions between temperature-controlled decomposition and topography on SOC in high-latitude systems. However, there remains a need for more SOC data from wetlands and boreal-region permafrost soils, especially at depths > 1 m in order to fully understand the effects of climate on soil carbon in Alaska.

High-latitude regions store large amounts of organic carbon (OC) in active-layer soils and permafrost, accounting for nearly half of the global belowground OC pool. In the boreal region, recent warming has promoted changes in the fire regime, which may exacerbate rates of permafrost thaw and alter soil OC dynamics in both organic and mineral soil. We examined how interactions between fire and permafrost govern rates of soil OC accumulation in organic horizons, mineral soil of the active layer, and near-surface permafrost in a black spruce ecosystem of interior Alaska. To estimate OC accumulation rates, we used chronosequence, radiocarbon, and modeling approaches. We also developed a simple model to track long-term changes in soil OC stocks over past fire cycles and to evaluate the response of OC stocks to future changes in the fire regime. Our chronosequence and radiocarbon data indicate that OC turnover varies with soil depth, with fastest turnover occurring in shallow organic horizons (� 60 years) and slowest turnover in nearsurface permafrost (43000 years). Modeling analysis indicates that OC accumulation in organic horizons was strongly governed by carbon losses via combustion and burial of charred remains in deep organic horizons. OC accumulation in mineral soil was influenced by active layer depth, which determined the proportion of mineral OC in a thawed or frozen state and thus, determined loss rates via decomposition. Our model results suggest that future changes in fire regime will result in substantial reductions in OC stocks, largely from the deep organic horizon. Additional OC losses will result from fire-induced thawing of near-surface permafrost. From these findings, we conclude that the vulnerability of deep OC stocks to future warming is closely linked to the sensitivity of permafrost to wildfire disturbance.

The resilience and vulnerability of permafrost to climate change depends on complex interactions among topography, water, soil, vegetation, and snow, which allow permafrost to persist at mean annual air temperatures (MAATs) as high as +2 °C and degrade at MAATs as low as –20 °C. To assess these interactions, we compiled existing data and tested effects of varying conditions on mean annual surface temperatures (MASTs) and 2 m deep temperatures (MADTs) through modeling. Surface water had the largest effect, with water sediment temperatures being ~10 °C above MAAT. A 50% reduction in snow depth reduces MADT by 2 °C. Elevation changes between 200 and 800 m increases MAAT by up to 2.3 °C and snow depths by ~40%. Aspect caused only a ~1 °C difference in MAST. Covarying vegetation structure, organic matter thickness, soil moisture, and snow depth of terrestrial ecosystems, ranging from barren silt to white spruce ( Picea glauca (Moench) Voss) forest to tussock shrub, affect MASTs by ~6 °C and MADTs by ~7 °C. Groundwater at 2–7 °C greatly affects lateral and internal permafrost thawing. Analyses show that vegetation succession provides strong negative feedbacks that make permafrost resilient to even large increases in air temperatures. Surface water, which is affected by topography and ground ice, provides even stronger negative feedbacks that make permafrost vulnerable to thawing even under cold temperatures.