A lake-centric geospatial database to guide research and inform management decisions in an Arctic watershed in northern Alaska experiencing climate and land-use changes

Variations in surface area and biogeochemistry of subarctic-arctic lakes established through satellite and in-situ observations: An overview of published research from the past 30 years

Human activities have strongly impacted the global climate, and during the last few decades the global average temperature has risen at a rate faster than at any time on record. High latitude lakes in the subarctic and arctic permafrost regions have particularly been vulnerable given the "Arctic amplification" phenomenon and acceleration in warming rate in the northern hemisphere (0.2-0.8 °C/decade). This paper presents a comprehensive overview of the last 30 years of research investigating how subarctic and Arctic lakes respond to climate warming. The review focused on studies where remote sensing technology was used to quantify these responses. The difference between summer lake water temperature and air temperature varied between 1.7 and 5.4 °C in subarctic lakes and 2.4-3.2 °C in Arctic lakes. Overall, the freezing date of lake ice is generally delayed and the date of lake thawing occurs earlier. Lake surface area (4-48.5 %), and abundance in the subarctic and Arctic region have increased significantly due to rising temperature, permafrost thawing, increased precipitation and other localized surface disturbances. However, in recent years, instances of lake shrinkage (between -0.4 % and -40 %) have also been reported, likely due to riparian overflow, groundwater infiltration and lateral drainage. Furthermore, in subarctic and Arctic lakes, climate change and permafrost thawing would release CO2 and CH4, and alter carbon dynamics in impacted lakes through various interconnected processes which could potentially affect the quality of carbon (terrestrial, algae) entering a lake system. The review also highlighted a potential intersection between permafrost melting and public health through human exposure to long-buried viruses. Subarctic and arctic ecosystems' responses to climate change will continue to be an area of intense research interest, and this review has highlighted priority areas for research and how remote sensing technologies can facilitate the pursuit of such a research agenda.

Trophic structure of apex fish communities in closed versus leaky lakes of arctic Alaska

Despite low species diversity and primary production, trophic structure (e.g., top predator species, predator size) is surprisingly variable among Arctic lakes. We investigated trophic structure in lakes of arctic Alaska containing arctic char Salvelinus alpinus using stomach contents and stable isotope ratios in two geographically-close but hydrologically-distinct lake clusters to investigate how these fish may interact and compete for limited food resources. Aside from different lake connectivity patterns ('leaky' versus 'closed'), differing fish communities (up to five versus only two species) between lake clusters allowed us to test trophic hypotheses including: (1) arctic char are more piscivorous, and thereby grow larger and obtain higher trophic positions, in the presence of other fish species; and, (2) between arctic char size classes, resource polymorphism is more prominent, and thereby trophic niches are narrower and overlap less, in the absence of other predators. Regardless of lake cluster, we observed little direct evidence of arctic char consuming other fishes, but char were larger (mean TL = 468 vs 264 mm) and trophic position was higher (mean TP = 4.0 vs 3.8 for large char) in lakes with other fishes. Further, char demonstrated less intraspecific overlap when other predators were present whereas niche overlap was up to 100% in closed, char only lakes. As hydrologic characteristics (e.g., lake connectivity, water temperatures) will change across the Arctic owing to climate change, our results provide insight regarding potential concomitant changes to fish interactions and increase our understanding of lake trophic structure to guide management and conservation goals.

Identifying historical and future potential lake drainage events on the western Arctic coastal plain of Alaska

Arctic lakes located in permafrost regions are susceptible to catastrophic drainage. In this study, we reconstructed historical lake drainage events on the western Arctic Coastal Plain of Alaska between 1955 and 2017 using USGS topographic maps, historical aerial photography (1955), and Landsat Imagery (ca. 1975, ca. 2000, and annually since 2000). We identified 98 lakes larger than 10 ha that partially (>25% of area) or completely drained during the 62-year period. Decadal-scale lake drainage rates progressively declined from 2.0 lakes/yr (1955-1975), to 1.6 lakes/yr (1975-2000), and to 1.2 lakes/yr (2000-2017) in the ~30,000-km2 study area. Detailed Landsat trend analysis between 2000 and 2017 identified two years, 2004 and 2006, with a cluster (five or more) of lake drainages probably associated with bank overtopping or headward erosion. To identify future potential lake drainages, we combined the historical lake drainage observations with a geospatial dataset describing lake elevation, hydrologic connectivity, and adjacent lake margin topographic gradients developed with a 5-m-resolution digital surface model. We identified ~1900 lakes likely to be prone to drainage in the future. Of the 20 lakes that drained in the most recent study period, 85% were identified in this future lake drainage potential dataset. Our assessment of historical lake drainage magnitude, mechanisms and pathways, and identification of potential future lake drainages provides insights into how arctic lowland landscapes may change and evolve in the coming decades to centuries.

The role of lake size and local phenomena for monitoring ground-fast lake ice

In this study, we assess the effect of the lake size on the accuracy of a threshold-based classification of ground-fast and floating lake ice from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. For that purpose, two new methods (flood-fill and watershed method) are introduced and the results between the three classification approaches are compared regarding different lake size classes for a study area covering most of the Yamal Peninsula in Western Siberia. The focus is on April, the stage of maximum lake ice thickness, for the years 2016 and 2017. The results indicate that the largest lakes are likely most prone to errors by the threshold classification. The newly introduced methods seem to improve classification results. The results also show differences in fractions of ground-fast lake ice between 2016 and 2017, which might reflect differences in temperatures between the winters with severe impact on wildlife and freshwater fish resources in the region. Patterns of low backscatter responsible for the classification errors in the centre of the lakes were investigated and compared to the optical Sentinel-2 imagery of late-winter. Strong similarities between some patterns in the optical and SAR data were identified. They might be zones of thin ice, but further research is required for clarification of this phenomenon and its causes.

Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery

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 0.70 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.