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Groundwater upwellings provide warmer, stable overwinter temperatures for developing salmon embryos, which may be particularly important in cold, braided, gravel-bed sub-Arctic rivers. We used a three-year time series of aerial counts and remote sensing to estimate the distribution of low and high aggregations of spawning fall chum salmon (Oncorhynchus keta), classify approximately 0.5 km long river segments by geomorphic channel type, and map thermal variability along a 25.4 km stretch of the Teedriinjik River, Alaska. We used a dynamic multistate occupancy model to estimate detectability, occupancy, and the dynamics of spawning aggregations among river segments. Detectability was higher for large (>150) relative to smaller aggregations. Unoccupied segments were likely to remain so from year to year; low abundance spawning segments were dynamic and rarely remained in that state for multiple years, while ∼20%–35% of high abundance segments remained stable, indicating the presence of high-quality spawning habitat. Spawning habitat use was associated with warmer water temperatures likely caused by groundwater upwellings. We identified spawning habitat characteristics and trends in usage by fall chum salmon, which will inform land management decisions and assist in evaluating impacts of shifting climate conditions and resource management on Arctic salmon populations.

Infrared imaging spectrometers are used to map and characterize wildland fire based on their sensitivity to fire-emitted thermal radiation and ability to resolve spectral emission or absorption features. There is a general paucity of research on the use of space-borne imaging spectroscopy to study active fires in the North American boreal forest. We used hyperspectral data acquired by the Hyperion sensor on the EO-1 satellite over three wildfires in Alaska's boreal forest to evaluate three fire detection methods: a metric to detect an emission feature from potassium emitted by biomass burning; a continuum-interpolated band ratio (CIBR) that measures the depth of a carbon dioxide absorption line at 2010 nm; and the Hyperspectral Fire Detection Index (HFDI), which is a normalized difference index based on spectral radiance in the short-wave infrared range. We found that a modified version of the HFDI produces a well-defined map of the active fire areas. The CO2 CIBR, though affected by sensor noise and smoke, contributes a slight improvement to the fire detection performance when combined with HFDI-type indices. In contrast, detecting a fire signal from potassium emission was not reliably possible in a practically useful way. We furthermore retrieved fire temperatures by modeling the at-sensor radiance as a linear mixture of two emitted and two reflected spectral radiance endmembers. High-temperature fire areas (the high-intensity fire front, modeled at 800–900 K) and low-temperature combustion (residual fire at 500–600 K), were mapped. High-temperature burning areas as small as half a percent of a Hyperion pixel (approx. 5 m2) were detectable. These techniques are of potential interest for fire characterization in the boreal areas of the circumpolar North using current and future satellite-borne imaging spectrometers.

Six DEMs over a 10-year period were used to estimate flood-related sedimentation in the Japanese Creek drainage located in Seward, Alaska. We analyze two existing LiDAR DEMs and one GNSS-derived DEM along with three additional DEMs that we generated using differential Global Navigation Satellite System (dGNSS) and Structure from Motion (SfM) techniques. Uncertainties in each DEM were accounted for, and a DEMs of Difference (DoD) technique was used to quantify the amount and pattern of sediment introduced, redistributed, or exiting the system. Through correlating the changes in sediment budget with rainfall data and flood events, the study demonstrates that the major flood events in 2006 and 2012—the 7th and 5th highest precipitation events on record—resulted in an increased sedimentation in the drainage as a whole. At a minimum the 2006 and 2012 events increased the sediment in the lower reaches by 70,100 and 53,900 cubic meters, respectively. The study shows that the DoD method and using multiple technologies to create DEMs is effective in quantifying the volumetric change and general spatial patterns of sediment redistribution between the acquisition of DEMs.

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts. info:eu-repo/semantics/publishedVersion

Recent Arctic warming has led to changes in the hydrological cycle. Circum-Arctic and circumboreal ecosystems are showing evidence of “greening” and “browning” due to temperature warming leading to shrub encroachment, tree mortality and deciduousness. Increases in latent heat flux from increased evapotranspiration rates associated with deciduous-dominated ecosystems may be significant, because deciduous vegetation has extremely high-water use and water storage capacity compared to coniferous and herbaceous plant species. Thus, the impact of vegetation change in boreal ecosystems on regional surface energy balance is a significant knowledge gap that must be addressed to better understand observed trends in water use/availability and tree mortality. To this end, output from a two-source energy balance model (TSEB) with modifications for high latitude boreal ecosystems was evaluated using flux tower measurements and Terra/Aqua MODIS remote sensing data collected over the two largest boreal forest types in Alaska (birch and black spruce). Data under clear and overcast days and from leaf-out to senescence from 2012 to 2016 were used for validation. Using flux tower observations and local model inputs, modifications to the model formulation for soil heat flux, net radiation partitioning, and canopy transpiration were required for the boreal forest. These improvements resulted in a mean absolute percent difference of around 23% for turbulent daytime fluxes when surface temperature from the flux towers was used, similar to errors reported in other studies conducted in warmer climates. Results when surface temperature from Terra/Aqua MODIS estimates were used as model input suggested that these model improvements are pertinent for regional scale applications. Vegetation indices and LAI time-series from the Terra/Aqua MODIS products were confirmed to be appropriate for energy flux estimation in the boreal forest to describe vegetation properties (LAI and green fraction) when field observations are not available. Model improvements for boreal settings identified in this study will be implemented operationally over North America to map surface energy fluxes at regional scales using long time series of remote sensing estimates as part of NOAA’s GOES Evapotranspiration and Drought Information System.

The Colville River is the largest river basin (∼53,000 km2) in Alaskan Arctic. The residents of Nuiqsut, an established Alaska Native community of ∼400 people, rely heavily on the Colville for their subsistence needs. Nonetheless, the changing river dynamics caused by accelerated bank erosion and consequent river siltation is making travel on the Colville increasingly difficult and in some cases dangerous. We used summer 2017 scenes from three optical sensors (PlanetScope, Sentinel 2, and Landsat) with resolution ranging from 3–30 m along with field measurements of water-depth to map shallow-water sites along a 45-km stretch of the Colville River. We found a strong correlation ( $\mathrm{R}^{2}=0.9$ ) between field-measured water depths and image-derived reflectance quantity (natural log ratio of green over red bands). We also analyzed a relative depth mapping technique which can be used to map shallow-water sites from optical images in the absence of field-measured water depths.

We used spatially continuous field-measured and remotely sensed aquatic habitat characteristics paired with weekly ground-based telemetry tracking and snorkel surveys to describe movements and habitat occupancy of adult rainbow trout (Oncorhynchus mykiss) (N = 82) in a runoff-fed, salmon-influenced southcentral Alaska river system. We found that during the ice-free feeding season (June through September) rainbow trout occurrence was associated more with fine-scale (channel unit) characteristics relative to coarse-scale (stream reach) variables. The presence of Pacific salmon (Oncorhynchus spp., which provide an important seasonal food subsidy) and habitat size were particularly useful predictors. Weekly movement distance differed between pre- and postspawning salmon arrival, but did not vary by sex. Habitat quality, season, and the arrival of spawning salmon influenced the likelihood of rainbow trout movement, and fish moved farther to seek out higher-quality habitats. Because rainbow trout respond to habitat factors at multiple scales and seek out salmon-derived subsidies, it will be important to take a multiscale approach in protecting trout and salmon populations and managing the associated fisheries.

Pacific salmon Oncorhynchus spp. face serious challenges from climate and landscape change, particularly in the southern portion of their native range. Conversely, climate warming appears to be allowing salmon to expand northwards into the Arctic. Between these geographic extremes, in the Gulf of Alaska region, salmon are at historically high abundances but face an uncertain future due to rapid environmental change. We examined changes in climate, hydrology, land cover, salmon populations, and fisheries over the past 30–70 years in this region. We focused on the Kenai River, which supports world-famous fisheries but where Chinook Salmon O. tshawytscha populations have declined, raising concerns about their future resilience. The region is warming and experiencing drier summers and wetter autumns. The landscape is also changing, with melting glaciers, wetland loss, wildfires, and human development. This environmental transformation will likely harm some salmon populations while benefiting others. Lowland s...

The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. An extensive local evaluation of the two-source energy balance model (TSEB) - a remote sensing-based model using thermal infrared retrievals of land-surface temperature - was performed using tower measurements collected over two flux towers in Greenland’s tundra types and two flux tower sites in Alaska’s boreal forest in all sky conditions in summer. Evaluation results showed a mean turbulent flux errors generally less than 50 W•m-2 at half-hourly timesteps, similar to errors typically reported in surface energy balance modelling studies conducted in more temperate climatic regimes.

The computation of turbulent fluxes of heat, momentum, and greenhouse gases requires measurements taken at high sampling frequencies. An important step in this process involves the detection and removal of sudden, short-lived variations that do not represent physical processes and that contaminate the data (i.e., spikes). The objective of this study is to assess the performance of several noteworthy despiking methodologies in order to provide a benchmark assessment and to provide a recommendation that is most applicable to high-frequency micrometeorological data in terms of efficiency and simplicity. The performance of a statistical time window–based algorithm widely used in micrometeorology is compared to three other methodologies (phase space, wavelet based, and median filter). These algorithms are first applied to a synthetic signal (a clean reference version and then one with spikes) in order to assess general performance. Afterward, testing is done on a time series of actual CO2 concentrations that contains extreme systematic spikes every hour owing to instrument interference, as well as several smaller random spike points. The study finds that the median filter and wavelet threshold methods are most reliable, and that their performance by far exceeds statistical time window–based methodologies that use the median or arithmetic mean operator (−34% and −71% reduced root-mean-square deviation, respectively). Overall, the median filter is recommended, as it is most easily automatable for a variety of micrometeorological data types, including data with missing points and low-frequency coherent turbulence.

Water tracks are an intrinsic part of the surficial drainage network in the foothills of the Brooks Range, Alaska. They preferentially transport water off hill slopes and represent the interplay between hydrology, vegetation, geomorphology and permafrost characteristics. This research on mapping the location of water tracks builds on previous work which demonstrated that different types of water tracks exist due to difference primarily driven by geomorphology. We used a combination method where spectral classifications, texture and topography were fed into random forests to identify the water track classes. The most accurate distributions were obtained for the organic-rich and wide water track classes. The distinct linear shapes of the water tracks could also be visualized for many of the classes, especially in areas where the water tracks were particularly discrete. The biggest challenges to mapping the water tracks were due to class imbalances and high variability within and overlapping between classes. This research presents a significant step forward in understanding periglacial landscape dynamics.

A better understanding of water movement on hillslopes in Arctic environments is necessary for evaluating the effects of climate variability. Drainage networks include a range of features that vary in transport capacity from rills to water tracks to rivers. This research focuses on describing and classifying water tracks, which are saturated linear-curvilinear stripes that act as first-order pathways for transporting water off of hillslopes into valley bottoms and streams. Multiple factor analysis was used to develop five water tracks classes based on their geomorphic, soil and vegetation characteristics. The water track classes were then validated using conditional inference trees, to verify that the classes were repeatable. Analysis of the classes and their characteristics indicate that water tracks cover a broad spectrum of patterns and processes primarily driven by surficial geology. This research demonstrates an improved approach to quantifying water track characteristics for specific areas, which is a major step towards understanding hydrological processes and feedbacks within a region.

The Arctic has become generally a warmer place over the past decades leading to earlier snow melt, permafrost degradation and changing plant communities. Increases in precipitation and local evaporation in the Arctic, known as the acceleration components of the hydrologic cycle, coupled with land cover changes, have resulted in significant changes in the regional surface energy budget. Quantifying spatiotemporal trends in surface energy flux partitioning is key to forecasting ecological responses to changing climate conditions in the Arctic. An extensive local evaluation of the Two-Source Energy Balance model (TSEB) – a remote-sensing-based model using thermal infrared retrievals of land surface temperature – was performed using tower measurements collected over different tundra types in Alaska in all sky conditions over the full growing season from 2008 to 2012. Based on comparisons with flux tower observations, refinements in the original TSEB net radiation, soil heat flux and canopy transpiration parameterizations were identified for Arctic tundra. In particular, a revised method for estimating soil heat flux based on relationships with soil temperature was developed, resulting in significantly improved performance. These refinements result in mean turbulent flux errors generally less than 50 W m−2 at half-hourly time steps, similar to errors typically reported in surface energy balance modeling studies conducted in more temperate climatic regimes. The MODIS leaf area index (LAI) remote sensing product proved to be useful for estimating energy fluxes in Arctic tundra in the absence of field data on the local biomass amount. Model refinements found in this work at the local scale build toward a regional implementation of the TSEB model over Arctic tundra ecosystems, using thermal satellite remote sensing to assess response of surface fluxes to changing vegetation and climate conditions.

Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (Ta) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of Ta together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and Ta, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and Ta or only w models that yielded higher error and bias.

The Colville is an Arctic river in the Alaska North Slope. The residents of Nuiqsut rely heavily on the Colville for their subsistence needs. Increased erosion has been reported on the Colville, especially along bluffs, which shaped the goals of this study: to use remote sensing techniques to map and quantify erosion rates and the volume of land loss at selected bluff sites along the main channel of the Colville, and to assess the suitability of automated methods of regional erosion monitoring. We used orthomosaics from high resolution aerial photos acquired in 1955 and 1979/1982, as well as high resolution WorldView-2 images from 2015 to quantify long-term erosion rates and the cubic volume of erosion. We found that, at the selected sites, erosion rates averaged 1 to 3.5 m per year. The erosion rate remained the same at one site and increased from 1955 to 2015 at two of the four sites. We estimated the volume of land loss to be in the magnitude of 166,000 m3 to 2.5 million m3 at our largest site. We also found that estimates of erosion were comparable for manual hand-digitized and automated methods, suggesting our automated method was effective and can be extended to monitor erosion at other sites along river systems that are bordered by bluffs.

We evaluate local differences in thermal regimes and turbulent heat fluxes across the heterogeneous canopy of a black spruce boreal forest on discontinuous permafrost in interior Alaska. The data were taken during an intensive observing period in the summer of 2013 from two micrometeorological towers 600 m apart in a central section of boreal forest, one in a denser canopy (DC) and the other in a sparser canopy, but under approximately similar atmospheric boundary layer (ABL) flow conditions. Results suggest that on average 34% of the half-hourly periods in a day are nonstationary, primarily during night and during ABL transitions. Also, thermal regimes differ between the two towers; specifically between midnight and 0500 Alaska Standard Time (AKST) it is about 3°C warmer at DC. On average, the sensible heat flux at DC was greater. For midday periods, the difference between those fluxes exceeded 30% of the measured flux and over 30 W m−2 in magnitude more than 60% of the time. These differences are due to higher mechanical mixing as a result of the increased density of roughness elements at DC. Finally, the vertical distribution of turbulent heat fluxes verifies a maximum atop the canopy crown (2.6 h) when compared with the subcanopy (0.6 h) and above canopy (5.1 h), where h is the mean canopy height. We argue that these spatial and vertical variations of sensible heat fluxes result from the complex scale aggregation of energy fluxes over a heterogeneous canopy.

Documenting earthquake-induced liquefaction effects is important to validate empirical liquefaction susceptibility models and to enhance our understanding of the liquefaction process. Currently, after an earthquake, field-based mapping of liquefaction can be sporadic and limited due to inaccessibility and lack of resources. Alternatively, researchers have used change detection with remotely sensed pre- and post-earthquake satellite images to map earthquake-induced effects. We hypothesize that as liquefaction occurs in saturated granular soils due to an increase in pore pressure, liquefaction-induced surface changes should be associated with increased moisture, and spectral bands/transformations that are sensitive to soil moisture can be used to identify these areas. We verify our hypothesis using change detection with pre- and post-earthquake thermal and tasseled cap wetness images derived from available Landsat 7 Enhanced Thematic Mapper Plus (ETM + ) for the 2001 Bhuj earthquake in India. The tasseled cap wetness image is directly related to the soil moisture content, whereas the thermal image is inversely related to it. The change detection of the tasseled cap transform wetness image helped to delineate earthquake-induced liquefaction areas that corroborated well with previous studies. The extent of liquefaction varied within and between geomorphological units, which we believe can be attributed to differences in the soil moisture retention capacity within and between the geomorphological units.

[1] Accelerations in the flow over forests generate coherent structures which locally enhance updrafts and downdrafts, forcing rapid exchanges of energy and matter. Here, observations of the turbulent flow are made in a highly heterogeneous black spruce boreal forest in Fairbanks, Alaska at ~2.6 h (12 m) and ~0.6 h (3 m), where h is the mean canopy height of 4.7 m. Wavelet analysis is used to detect coherent structures. The sonic temperature and wind data cover 864 half-hour periods spanning winter, spring, and summer. When mean global statistics of structures are analyzed at the two levels independently, results are similar to other studies. Specifically, an average of eight structures occurs per period, their mean duration is 85 s, and their mean heat flux contribution is 48%. However, this analysis suggests that 31% of the structures detected at 2.6 h, and 13% at 0.6 h, may be influenced by wave-like flow organization. Remarkably, less than 25% of the structures detected occur synchronously in the subcanopy and above canopy levels, which speaks robustly to the lack of flow interaction within only nine vertical meters of the forest.

Surface fires are common in coalfields where coal is mined or exposed to sunlight for long durations of time. The heat energy emitted from these fires affects the signal recorded by sensors operating in the shortwave infrared regions of the electromagnetic spectrum. The Landsat TM/ETM+ band-7 is sensitive to solar reflected radiation as well as emitted radiation from a target. The ‘maximum solar reflection threshold’ method proposed in this study uses the highest spectral radiance that can be attributed to solar reflection as the conservative threshold to segregate the pixels with emitted component from those with reflected component of the EM energy. Investigations with Landsat TM/ETM+ data indicate a reflectance value of 0.23–0.25 as the most representative highest reflectance (threshold) in coal mining areas. The method apparently has the advantage that it is based on the reflectance characteristics of materials (sandstone-shale mixtures) typically found in coal mining areas.

During an explosive volcanic eruption, accurately determining the height of a volcanic plume or cloud is essential to accurately forecast its motion because volcanic ash transport and dispersion models require the initial plume height as an input parameter. The direct use of satellite infrared temperatures for height determination, one of the most commonly employed methods at the Alaska Volcano Observatory, often does not yield unique solutions for height. This result is documented here for the 2009 eruption of Redoubt Volcano. Satellite temperature heights consistently underestimated the height of ash plumes in comparison to other methods such as ground-based radar and Multi-angle Imaging SpectroRadiometer (MISR) stereo heights. For ash plumes below the tropopause, increasing transparency of a plume begins to affect the accuracy of simple temperature height retrievals soon after eruption. With decreasing opacity, plume temperature heights become increasingly inaccurate. Comparison with dispersion models and aircraft gas flight data confirms that radar and MISR stereo heights are more accurate than basic satellite temperature heights. Even in the cases in which satellite temperature results appeared to be relatively accurate (e.g., for plumes below the tropopause), a mixed signal of plume and ground radiation still presented an issue for almost every event studied. This was true regardless of the fact that a band differencing method was used to remove presumably translucent pixels. The data presented here make a strong case for the use of data fusion in volcano monitoring, as there is a need to confirm satellite temperature heights with other height data. If only basic satellite temperature heights are available for a given eruption, then these heights must be considered with a significant margin of error.

This study investigates the effects of sub-grid lake variability on brightness temperature measurements and snow mass retrieval from passive microwave satellite data in the North Slope of Alaska. In situ snow measurements of water equivalent, depth and density collected from field surveys during 1996–2004 snow seasons were collocated with gridded SSM/I brightness temperatures. Sub-grid lake fraction was computed from a high-resolution land cover map derived from LANDSAT imagery. Another dataset was created consisting of multi-year time series of SSM/I and AMSR-E brightness temperatures and computed lake fraction. Consistent with other studies, it was found that lake fraction was negatively correlated with in situ snow depth and snow water equivalent and positively correlated with snow density, which indicates that lakes have a direct effect on snow cover distribution: They accumulate less but denser snow than surrounding land areas. Additionally, lake fraction was positively correlated with the brightness temperature measurements at 18 GHz and above, but negatively correlated with the AMSR-E brightness temperatures at low frequency channels (6 and 10 GHz), with the highest correlation values for the SSM/I (r = 0.57) and AMSR-E (r = 0.65) at 37 GHz dominating the response. Brightness temperature-based lake fraction algorithms were derived using stepwise regression. Performance assessment showed that the AMSR-E algorithm was superior to the SSM/I algorithm due to the use of the low frequency measurements not available from the SSM/I instrument. Combined lake fraction- and SSM/I brightness temperature-based snow depth and snow water equivalent algorithms were also derived using stepwise regression, with improved performance compared to conventional brightness temperature-based algorithms.

Tidewater glaciers are glaciers that terminate in, and calve icebergs into, the ocean. In addition to the influence that tidewater glaciers have on physical and chemical oceanography, floating icebergs serve as habitat for marine animals such as harbor seals (Phoca vitulina richardii). The availability and spatial distribution of glacier ice in the fjords is likely a key environmental variable that influences the abundance and distribution of selected marine mammals; however, the amount of ice and the fine-scale characteristics of ice in fjords have not been systematically quantified. Given the predicted changes in glacier habitat, there is a need for the development of methods that could be broadly applied to quantify changes in available ice habitat in tidewater glacier fjords. We present a case study to describe a novel method that uses object-based image analysis (OBIA) to classify floating glacier ice in a tidewater glacier fjord from high-resolution aerial digital imagery. Our objectives were to (i) develop workflows and rule sets to classify high spatial resolution airborne imagery of floating glacier ice; (ii) quantify the amount and fine-scale characteristics of floating glacier ice; (iii) and develop processes for automating the object-based analysis of floating glacier ice for large number of images from a representative survey day during June 2007 in Johns Hopkins Inlet (JHI), a tidewater glacier fjord in Glacier Bay National Park, southeastern Alaska. On 18 June 2007, JHI was comprised of brash ice ([Formula: see text] = 45.2%, SD = 41.5%), water ([Formula: see text] = 52.7%, SD = 42.3%), and icebergs ([Formula: see text] = 2.1%, SD = 1.4%). Average iceberg size per scene was 5.7 m2 (SD = 2.6 m2). We estimate the total area (± uncertainty) of iceberg habitat in the fjord to be 455,400 ± 123,000 m2. The method works well for classifying icebergs across scenes (classification accuracy of 75.6%); the largest classification errors occur in areas with densely-packed ice, low contrast between neighboring ice cover, or dark or sediment-covered ice, where icebergs may be misclassified as brash ice about 20% of the time. OBIA is a powerful image classification tool, and the method we present could be adapted and applied to other ice habitats, such as sea ice, to assess changes in ice characteristics and availability.

Using imaging spectrometers at multiple scales, the USGS, in collaboration with the University of Alaska, is examining the application of hyperspectral data for identifying large-tonnage, base metal-rich deposits in Alaska. Recent studies have shown this technology can be applied to regional mineral mapping [1] and can be valuable for more local mineral exploration [2]. Passive optical remote sensing of high latitude regions faces many challenges, which include a short acquisition season and poor illumination due to low solar elevation [3]. Additional complications are encountered in the identification of surface minerals useful for mineral resource characterization because minerals of interest commonly are exposed on steep terrain, further challenging reflectance retrieval and detection of mineral signatures. Laboratory-based imaging spectrometer measurements of hand samples and field-based imaging spectrometer scans of outcrop are being analyzed to support and improve interpretations of remote sensing data collected by airborne imaging spectrometers and satellite multispectral sensors.

At northern limits of a species’ distribution, fish habitat requirements are often linked to thermal preferences, and the presence of overwintering habitat. However, logistical challenges and hydrologic processes typical of glacial systems could compromize the identification of these habitats, particularly in large river environments. Our goal was to identify and characterize spawning habitat for fall-run chum salmon Oncorhynchus keta and model habitat selection from spatial distributions of tagged individuals in the Tanana River, Alaska using an approach that combined ground surveys with remote sensing. Models included braiding, sinuosity, ice-free water surface area (indicating groundwater influence), and persistent ice-free water (i.e., consistent presence of ice-free water for a 12-year period according to satellite imagery). Candidate models containing persistent ice-free water were selected as most likely, highlighting the utility of remote sensing for monitoring and identifying salmon habi...

A combination of binary logistic regression (BLR) and remote sensing techniques was used to generate a high-resolution spatially continuous near-surface (< 1.6 m) permafrost map. The BLR model was ...

Ground-based surveys of three coal fires and airborne surveys of two of the fires were conducted near Sheridan, Wyoming. The fires occur in natural outcrops and in abandoned mines, all containing Paleocene-age subbituminous coals. Diffuse (carbon dioxide (CO(2)) only) and vent (CO(2), carbon monoxide (CO), methane, hydrogen sulfide (H(2)S), and elemental mercury) emission estimates were made for each of the fires. Additionally, gas samples were collected for volatile organic compound (VOC) analysis and showed a large range in variation between vents. The fires produce locally dangerous levels of CO, CO(2), H(2)S, and benzene, among other gases. At one fire in an abandoned coal mine, trends in gas and tar composition followed a change in topography. Total CO(2) fluxes for the fires from airborne, ground-based, and rate of fire advancement estimates ranged from 0.9 to 780mg/s/m(2) and are comparable to other coal fires worldwide. Samples of tar and coal-fire minerals collected from the mouth of vents provided insight into the behavior and formation of the coal fires.

Coal fires occur in all coal-bearing regions of the world and number, conservatively, in the thousands. These fires emit a variety of compounds including greenhouse gases. However, the magnitude of the contribution of combustion gases from coal fires to the environment is highly uncertain, because adequate data and methods for assessing emissions are lacking. This study demonstrates the ability to estimate CO 2 and CH 4 emissions for the Welch Ranch coal fire, Powder River Basin, Wyoming, USA, using two independent methods: (a) heat flux calculated from aerial thermal infrared imaging (3.7–4.4 t d −1 of CO 2 equivalent emissions) and (b) direct, ground-based measurements (7.3–9.5 t d −1 of CO 2 equivalent emissions). Both approaches offer the potential for conducting inventories of coal fires to assess their gas emissions and to evaluate and prioritize fires for mitigation.

A coal seam fire in interior Alaska was suspected to have started the Rex Creek forest fire in the summer of 2009. With prevailing winds, the forest fire spread rapidly to the north and within eleven days it burned about 410 km 2 of boreal forest. Coal seam fires can go unnoticed and unreported when present in remote and inaccessible areas. However, they still pose a serious threat to the surroundings. We used summer-time thermal infrared images from 1999 through 2009 acquired by the Landsat satellite and, through the process of image stacking, identified a region where the surface persistently showed temperatures 5 °C to 14 °C higher than the background areas. Field validation confirmed that this thermal anomaly area corresponds to a previously undocumented shallow coal seam fire. Superimposing the boundary of the Rex Creek forest fire revealed that the coal seam fire was at the southern end of the burn area where the forest fire originated. Plotting the location of all lightning strikes during this period helped to rule out lightning as the cause of the forest fire. Coal fires and forest fires can have a complex and dynamic relationship, one being the possible cause of the other. A thorough inventory of all past and present known coal seam fire locations can help to update forest fire hazard maps. A detailed map of shallow coal seam areas can help to prioritize fire fighting operations in order to avoid the chance of starting a new coal seam fire.

Multiple Regressive Pattern Recognition Technique (MRPRT) is an adapted approach for improved geologic resource estimation. We developed and tested this approach for the Platinum (Pt) bearing region near Goodnews Bay, Alaska, which presents an example of a complex depositional environment. We applied geospatial and pattern recognition methods to assess the spatial distribution of offshore Pt in the Goodnews Bay area from point data collected by various agencies. We used the coefficient of correlation (r) and the Nash–Sutcliffe efficiency (E) to quantitatively assess the degree of accuracy of the estimated Pt distribution. We split the study area, based on trend analysis, into two regions: inside the Bay and outside the Bay. We could not obtain appreciable estimates from the geospatial and pattern recognition methods. Using MRPRT, we were able to improve r from 0.57 to 0.93 and the E from 28.31 to 92.91 inside the Bay. We achieved improvement in r from 0.55 to 0.61 and E from 28.46 to 34.52 outside the Bay. The reasons for a non-significant improvement outside the Bay have been discussed. The results indicate that the proposed MRPRT has wide application potential in georesource estimation where input data is often scarce.