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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.

 

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.

 

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.

 

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.

 

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. 

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. 

Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers. 

Nearly 25% of all lakes on earth are located at high latitudes. These lakes are formed by a combination of thermokarst, glacial, and geological processes. Evidence suggests that the origin of periglacial lake formation may be an important factor controlling the likelihood of lakes to drain. However, geospatial data regarding the spatial distribution of these dominant Arctic and subarctic lakes are limited or do not exist. Here, we use lake-specific morphological properties using the Arctic Digital Elevation Model (DEM) and Landsat imagery to develop a Thermokarst lake Settlement Index (TSI), which was used in combination with available geospatial datasets of glacier history and yedoma permafrost extent to classify Arctic and subarctic lakes into Thermokarst (non-yedoma), Yedoma, Glacial, and Maar lakes, respectively. This lake origin dataset was used to evaluate the influence of lake origin on drainage between 1985 and 2019 in northern Alaska. The lake origin map and lake drainage datasets were synthesized using five-year seamless Landsat ETM+ and OLI image composites. Nearly 35,000 lakes and their properties were characterized from Landsat mosaics using an object-based image analysis. Results indicate that the pattern of lake drainage varied by lake origin, and the proportion of lakes that completely drained (i.e., >60% area loss) between 1985 and 2019 in Thermokarst (non-yedoma), Yedoma, Glacial, and Maar lakes were 12.1, 9.5, 8.7, and 0.0%, respectively. The lakes most vulnerable to draining were small thermokarst (non-yedoma) lakes (12.7%) and large yedoma lakes (12.5%), while the most resilient were large and medium-sized glacial lakes (4.9 and 4.1%) and Maar lakes (0.0%). This analysis provides a simple remote sensing approach to estimate the spatial distribution of dominant lake origins across variable physiography and surficial geology, useful for discriminating between vulnerable versus resilient Arctic and subarctic lakes that are likely to change in warmer and wetter climates.