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In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. 

Abstract The 2015 spring flood of the Sagavanirktok River inundated large swaths of tundra as well as infrastructure near Prudhoe Bay, Alaska. Its lasting impact on permafrost, vegetation, and hydrology is unknown but compels attention in light of changing Arctic flood regimes. We combined InSAR and optical satellite observations to quantify subdecadal permafrost terrain changes and identify their controls. While the flood locally induced quasi‐instantaneous ice‐wedge melt, much larger areas were characterized by subtle, spatially variable post‐flood changes. Surface deformation from 2015 to 2019 estimated from ALOS‐2 and Sentinel‐1 InSAR varied substantially within and across terrain units, with greater subsidence on average in flooded locations. Subsidence exceeding 5 cm was locally observed in inundated ice‐rich units and also in inactive floodplains. Overall, subsidence increased with deposit age and thus ground ice content, but many flooded ice‐rich units remained stable, indicating variable drivers of deformation. On average, subsiding ice‐rich locations showed increases in observed greenness and wetness. Conversely, many ice‐poor floodplains greened without deforming. Ice wedge degradation in flooded locations with elevated subsidence was mostly of limited intensity, and the observed subsidence largely stopped within 2 years. Based on remote sensing and limited field observations, we propose that the disparate subdecadal changes were influenced by spatially variable drivers (e.g., sediment deposition, organic layer), controls (ground ice and its degree of protection), and feedback processes. Remote sensing helps quantify the heterogeneous interactions between permafrost, vegetation, and hydrology across permafrost‐affected fluvial landscapes. Interdisciplinary monitoring is needed to improve predictions of landscape dynamics and to constrain sediment, nutrient, and carbon budgets. 

Abstract The Mw 7.1 47 km deep earthquake that occurred on 30 November 2018 had deep societal impacts across southcentral Alaska and exhibited phenomena of broad scientific interest. We document observations that point to future directions of research and hazard mitigation. The rupture mechanism, aftershocks, and deformation of the mainshock are consistent with extension inside the Pacific plate near the down‐dip limit of flat‐slab subduction. Peak ground motions >25%g were observed across more than 8000  km2, though the most violent near‐fault shaking was avoided because the hypocenter was nearly 50 km below the surface. The ground motions show substantial variation, highlighting the influence of regional geology and near‐surface soil conditions. Aftershock activity was vigorous with roughly 300 felt events in the first six months, including two dozen aftershocks exceeding M 4.5. Broad subsidence of up to 5 cm across the region is consistent with the rupture mechanism. The passage of seismic waves and possibly the coseismic subsidence mobilized ground waters, resulting in temporary increases in stream flow. Although there were many failures of natural slopes and soils, the shaking was insufficient to reactivate many of the failures observed during the 1964 M 9.2 earthquake. This is explained by the much shorter duration of shaking as well as the lower amplitude long‐period motions in 2018. The majority of observed soil failures were in anthropogenically placed fill soils. Structural damage is attributed to both the failure of these emplaced soils as well as to the ground motion, which shows some spatial correlation to damage. However, the paucity of instrumental ground‐motion recordings outside of downtown Anchorage makes these comparisons challenging. The earthquake demonstrated the challenge of issuing tsunami warnings in complex coastal geographies and highlights the need for a targeted tsunami hazard evaluation of the region. The event also demonstrates the challenge of estimating the probabilistic hazard posed by intraslab earthquakes. 

Abstract In April 2022, a seismic swarm near Mt. Edgecumbe in southeast Alaska suggested renewed activity at this transform fault volcano, which was last active ≈800 years ago. Previously, thin rhyolitic tephras were deposited 5 and 4 ka. Satellite radar data from 2014 to 2022 resolves line‐of‐sight rapid inflation up to 7.1 cm/yr beginning in August 2018. Bayesian modeling suggests a transcrustal system of a deflating (−0.528 km³) dipping sill at 20 km depth recharging a magma chamber at 10 km (0.222 km³). A near‐vertical conduit could capture the volume difference without noticeable surface deformation. Reanalyzed seismicity, recorded 25 km away, shows increases since July 2019. Magma ascent through ductile material and brittle strain release in a stressed overburden could explain the time delay. Cloud‐native open data and workflows enabled discovery and analysis of this signal within days after going unnoticed for >3 years.