Special Issue "Remote Sensing of Arctic Tundra"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 October 2017).

Special Issue Editors

Dr. Howard E. Epstein
Website
Guest Editor
Department of Environmental Sciences, University of Virginia, 291 McCormick Road, Clark Hall 211, Charlottesville, VA 22904-4123, USA
Interests: arctic tundra, dryland ecosystems, temperate old fields and forests, vegetation patterns and dynamics, plant-soil-atmosphere interactions, carbon and nitrogen cycling, optical remote sensing
Dr. Martha K. Raynolds

Guest Editor
Institute of Arctic Biology, University of Alaska Fairbanks, P.O. Box 757000, Fairbanks, AK 99775, USA
Interests: arctic ecology, vegetation mapping, remote sensing, vegetation indices

Special Issue Information

Dear Colleagues,

Arctic tundra ecosystems are undergoing dramatic changes resulting from the inter-related dynamics of climate, sea-ice, snow cover, permafrost, and terrestrial disturbances (e.g., fire, thermokarst, landslides, and industrial and civil infrastructure). Changes in the tundra surface properties of vegetation, water (e.g., lakes and ponds), and soil are crucial for projecting feedbacks to climate, yet are challenging to capture in the field due to the remoteness of the locations and the need for relatively long-term monitoring.  Remote sensing will continue to provide a valuable and insightful approach for examining the patterns and dynamics of arctic tundra surface characteristics in response to environmental factors.

We are pleased to announce a Special Issue of the journal Remote Sensing on “Remote Sensing of Arctic Tundra”. We solicit manuscripts that use the broad array of remote sensing platforms (i.e., handheld, drone, airborne, and satellite) and sensors (e.g., optical, microwave, radar, LiDAR), across spatial, temporal, and spectral resolutions and extents, to examine the patterns and dynamics of arctic tundra systems.

Dr. Howard Epstein
Dr. Martha Raynolds
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (14 papers)

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Research

Open AccessArticle
Assessment of LiDAR and Spectral Techniques for High-Resolution Mapping of Sporadic Permafrost on the Yukon-Kuskokwim Delta, Alaska
Remote Sens. 2018, 10(2), 258; https://doi.org/10.3390/rs10020258 - 07 Feb 2018
Cited by 6
Abstract
Western Alaska’s Yukon-Kuskokwim Delta (YKD) spans nearly 67,200 km2 and is among the largest and most productive coastal wetland ecosystems in the pan-Arctic. Permafrost currently forms extensive elevated plateaus on abandoned floodplain deposits of the outer delta, but is vulnerable to disturbance [...] Read more.
Western Alaska’s Yukon-Kuskokwim Delta (YKD) spans nearly 67,200 km2 and is among the largest and most productive coastal wetland ecosystems in the pan-Arctic. Permafrost currently forms extensive elevated plateaus on abandoned floodplain deposits of the outer delta, but is vulnerable to disturbance from rising air temperatures, inland storm surges, and salt-kill of vegetation. As pan-Arctic air and ground temperatures rise, accurate baseline maps of permafrost extent are critical for a variety of applications including long-term monitoring, understanding the scale and pace of permafrost degradation processes, and estimating resultant greenhouse gas dynamics. This study assesses novel, high-resolution techniques to map permafrost distribution using LiDAR and IKONOS imagery, in tandem with field-based parameterization and validation. With LiDAR, use of a simple elevation threshold provided a permafrost map with 94.9% overall accuracy; this approach was possible due to the extremely flat coastal plain of the YKD. The addition of high spatial-resolution IKONOS satellite data yielded similar results, but did not increase model performance. The methods and the results of this study enhance high-resolution permafrost mapping efforts in tundra regions in general and deltaic landscapes in particular, and provide a baseline for remote monitoring of permafrost distribution on the YKD. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Terrestrial CDOM in Lakes of Yamal Peninsula: Connection to Lake and Lake Catchment Properties
Remote Sens. 2018, 10(2), 167; https://doi.org/10.3390/rs10020167 - 25 Jan 2018
Cited by 4
Abstract
In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)CDOM) and absorption slope (S300–500) in lakes using field sampling and [...] Read more.
In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)CDOM) and absorption slope (S300–500) in lakes using field sampling and optical remote sensing data for an area of 350 km2 in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a(λ)CDOM data from 18 lakes sampled in the field to 356 lakes in the study area (model R2 = 0.79). Values of a(440)CDOM in 356 lakes varied from 0.48 to 8.35 m−1 with a median of 1.43 m−1. This a(λ)CDOM dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440)CDOM = 5.3 m−1). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440)CDOM = 3.8 m−1) compared to lakes located on higher terraces. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Monitoring Inter- and Intra-Seasonal Dynamics of Rapidly Degrading Ice-Rich Permafrost Riverbanks in the Lena Delta with TerraSAR-X Time Series
Remote Sens. 2018, 10(1), 51; https://doi.org/10.3390/rs10010051 - 29 Dec 2017
Cited by 12
Abstract
Arctic warming is leading to substantial changes to permafrost including rapid degradation of ice and ice-rich coasts and riverbanks. In this study, we present and evaluate a high spatiotemporal resolution three-year time series of X-Band microwave satellite data from the TerraSAR-X (TSX) satellite [...] Read more.
Arctic warming is leading to substantial changes to permafrost including rapid degradation of ice and ice-rich coasts and riverbanks. In this study, we present and evaluate a high spatiotemporal resolution three-year time series of X-Band microwave satellite data from the TerraSAR-X (TSX) satellite to quantify cliff-top erosion (CTE) of an ice-rich permafrost riverbank in the central Lena Delta. We apply a threshold on TSX backscatter images and automatically extract cliff-top lines to derive intra- and inter-annual CTE. In order to examine the drivers of erosion we statistically compare CTE with climatic baseline data using linear mixed models and analysis of variance (ANOVA). Our evaluation of TSX-derived CTE against annual optical-derived CTE and seasonal in situ measurements showed good agreement between all three datasets. We observed continuous erosion from June to September in 2014 and 2015 with no significant seasonality across the thawing season. We found the highest net annual cliff-top erosion of 6.9 m in 2014, in accordance with above-average mean temperatures and thawing degree days as well as low precipitation. We found high net annual erosion and erosion variability in 2015 associated with moderate mean temperatures but above average precipitation. According to linear mixed models, climate parameters alone could not explain intra-seasonal erosional patterns and additional factors such as ground ice content likely drive the observed erosion. Finally, mean backscatter intensity on the cliff surface decreased from −5.29 to −6.69 dB from 2013 to 2015, respectively, likely resulting from changes in surface geometry and properties that could be connected to partial slope stabilization. Overall, we conclude that X-Band backscatter time series can successfully be used to complement optical remote sensing and in situ monitoring of rapid tundra permafrost erosion at riverbanks and coasts by reliably providing information about intra-seasonal dynamics. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Short-Term Impacts of the Air Temperature on Greening and Senescence in Alaskan Arctic Plant Tundra Habitats
Remote Sens. 2017, 9(12), 1338; https://doi.org/10.3390/rs9121338 - 20 Dec 2017
Cited by 6
Abstract
Climate change is warming the temperatures and lengthening the Arctic growing season with potentially important effects on plant phenology. The ability of plant species to acclimate to changing climatic conditions will dictate the level to which their spatial coverage and habitat-type dominance is [...] Read more.
Climate change is warming the temperatures and lengthening the Arctic growing season with potentially important effects on plant phenology. The ability of plant species to acclimate to changing climatic conditions will dictate the level to which their spatial coverage and habitat-type dominance is different in the future. While the effect of changes in temperature on phenology and species composition have been observed at the plot and at the regional scale, a systematic assessment at medium spatial scales using new noninvasive sensor techniques has not been performed yet. At four sites across the North Slope of Alaska, changes in the Normalized Difference Vegetation Index (NDVI) signal were observed by Mobile Instrumented Sensor Platforms (MISP) that are suspended over 50 m transects spanning local moisture gradients. The rates of greening (measured in June) and senescence (measured in August) in response to the air temperature was estimated by changes in NDVI measured as the difference between the NDVI on a specific date and three days later. In June, graminoid- and shrub-dominated habitats showed the greatest rates of NDVI increase in response to the high air temperatures, while forb- and lichen-dominated habitats were less responsive. In August, the NDVI was more responsive to variations in the daily average temperature than spring greening at all sites. For graminoid- and shrub-dominated habitats, we observed a delayed decrease of the NDVI, reflecting a prolonged growing season, in response to high August temperatures. Consequently, the annual C assimilation capacity of these habitats is increased, which in turn may be partially responsible for shrub expansion and further increases in net summer CO2 fixation. Strong interannual differences highlight that long-term and noninvasive measurements of such complex feedback mechanisms in arctic ecosystems are critical to fully articulate the net effects of climate variability and climate change on plant community and ecosystem processes. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Modeling the Observed Microwave Emission from Shallow Multi-Layer Tundra Snow Using DMRT-ML
Remote Sens. 2017, 9(12), 1327; https://doi.org/10.3390/rs9121327 - 16 Dec 2017
Cited by 4
Abstract
The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, [...] Read more.
The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory for Multi Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. Airborne radiometer observations coordinated with ground-based in situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The DMRT-ML was parameterized with the in situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. With these two configurations, the calibrated DMRT-ML successfully predicted the Tb V 37 GHz response (R correlation of 0.83) when compared with the observed airborne Tb footprints containing snow pits measurements. Using this calibrated model, the DMRT-ML was applied to the whole study region. At the satellite observation scale, observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) over the study area reflected seasonal differences between Tb V 37 GHz and Tb V 19 GHz that supports the hypothesis of the development of an early season volume scattering depth hoar layer, followed by the growth of the late season emission-dominated wind slab layer. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack at 37 GHz Tb. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants
Remote Sens. 2017, 9(12), 1289; https://doi.org/10.3390/rs9121289 - 11 Dec 2017
Cited by 21
Abstract
Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, [...] Read more.
Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, which could easily be identified in the field as well as on RapidEye satellite imagery. Spectral measurements of individual species were acquired, and heavy metal contamination stress factors were measured contemporaneously. As a result, a unique spectral library of dominant plant species, heavy metal concentrations and damage ratios were achieved with an indication that species-specific changes due to environmental conditions can best be differentiated in the 1401–2400 nm spectral region. Two key arctic tundra species, Cassiope tetragona and Dryas octopetala, exhibited significant differences in this spectral region that were linked to a changing health status. Relationships between field and satellite measurements were comparable, e.g., the Red Edge Normalized Difference Vegetation Index (RENDVI) showed a strong and significant relationship (R2 = 0.82; p = 0.036) for the species Dryas octopetala. Cadmium and Lead were below detection levels while manganese, copper and zinc acquired near Longyearbyen were at concentrations comparable to other places in Svalbard. There were high levels of nickel near Longyearbyen (0.014 mg/g), while it was low (0.004 mg/g) elsewhere. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems
Remote Sens. 2017, 9(12), 1227; https://doi.org/10.3390/rs9121227 - 28 Nov 2017
Cited by 13
Abstract
Arctic tundra ecosystems are a major source of methane (CH4), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect [...] Read more.
Arctic tundra ecosystems are a major source of methane (CH4), the variability of which is affected by local environmental and climatic factors, such as water table depth, microtopography, and the spatial heterogeneity of the vegetation communities present. There is a disconnect between the measurement scales for CH4 fluxes, which can be measured with chambers at one-meter resolution and eddy covariance towers at 100–1000 m, whereas model estimates are typically made at the ~100 km scale. Therefore, it is critical to upscale site level measurements to the larger scale for model comparison. As vegetation has a critical role in explaining the variability of CH4 fluxes across the tundra landscape, we tested whether remotely-sensed maps of vegetation could be used to upscale fluxes to larger scales. The objectives of this study are to compare four different methods for mapping and two methods for upscaling plot-level CH4 emissions to the measurements from EC towers. We show that linear discriminant analysis (LDA) provides the most accurate representation of the tundra vegetation within the EC tower footprints (classification accuracies of between 65% and 88%). The upscaled CH4 emissions using the areal fraction of the vegetation communities showed a positive correlation (between 0.57 and 0.81) with EC tower measurements, irrespective of the mapping method. The area-weighted footprint model outperformed the simple area-weighted method, achieving a correlation of 0.88 when using the vegetation map produced with the LDA classifier. These results suggest that the high spatial heterogeneity of the tundra vegetation has a strong impact on the flux, and variation indicates the potential impact of environmental or climatic parameters on the fluxes. Nonetheless, assimilating remotely-sensed vegetation maps of tundra in a footprint model was successful in upscaling fluxes across scales. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska
Remote Sens. 2017, 9(11), 1200; https://doi.org/10.3390/rs9111200 - 22 Nov 2017
Cited by 6
Abstract
Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture. These attributes result in similar spectral characteristics between distinct vegetation communities. In this study we examine spectral variability at three phenological phases of [...] Read more.
Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture. These attributes result in similar spectral characteristics between distinct vegetation communities. In this study we examine spectral variability at three phenological phases of leaf-out, maximum canopy, and senescence of ground-based spectroscopy, as well as a simulated Environmental Mapping and Analysis Program (EnMAP) and simulated Sentinel-2 reflectance spectra, from five dominant low-Arctic tundra vegetation communities in the Toolik Lake Research Area, Alaska, in order to inform spectral differentiation and subsequent vegetation classification at both the ground and satellite scale. We used the InStability Index (ISI), a ratio of between endmember and within endmember variability, to determine the most discriminative phenophase and wavelength regions for identification of each vegetation community. Our results show that the senescent phase was the most discriminative phenophase for the identification of the majority of communities when using both ground-based and simulated EnMAP reflectance spectra. Maximum canopy was the most discriminative phenophase for the majority of simulated Sentinel-2 reflectance data. As with previous ground-based spectral characterization of Alaskan low-Arctic tundra, the blue, red, and red-edge parts of the spectrum were most discriminative for all three reflectance datasets. Differences in vegetation colour driven by pigment dynamics appear to be the optimal areas of the spectrum for differentiation using high spectral resolution field spectroscopy and simulated hyperspectral EnMAP and multispectral Sentinel-2 reflectance spectra. The phenological aspect of this study highlights the potential exploitation of more extreme colour differences in vegetation observed during senescence when hyperspectral data is available. The results provide insight into both the community and seasonal dynamics of spectral variability to better understand and interpret currently used broadband vegetation indices and also for improved spectral unmixing of hyperspectral aerial and satellite data which is useful for a wide range of applications from fine-scale monitoring of shifting vegetation composition to the identification of vegetation vigor. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
The Geometry of Large Tundra Lakes Observed in Historical Maps and Satellite Images
Remote Sens. 2017, 9(10), 1072; https://doi.org/10.3390/rs9101072 - 21 Oct 2017
Cited by 2
Abstract
The climate of the Arctic is warming rapidly and this is causing major changes to the cycling of carbon and the distribution of permafrost in this region. Tundra lakes are key components of the Arctic climate system because they represent a source of [...] Read more.
The climate of the Arctic is warming rapidly and this is causing major changes to the cycling of carbon and the distribution of permafrost in this region. Tundra lakes are key components of the Arctic climate system because they represent a source of methane to the atmosphere. In this paper, we aim to analyze the geometry of the patterns formed by large (> 0.8 km 2 ) tundra lakes in the Russian High Arctic. We have studied images of tundra lakes in historical maps from the State Hydrological Institute, Russia (date 1977; scale 0.21166 km/pixel) and in Landsat satellite images derived from the Google Earth Engine (G.E.E.; date 2016; scale 0.1503 km/pixel). The G.E.E. is a cloud-based platform for planetary-scale geospatial analysis on over four decades of Landsat data. We developed an image-processing algorithm to segment these maps and images, measure the area and perimeter of each lake, and compute the fractal dimension of the lakes in the images we have studied. Our results indicate that as lake size increases, their fractal dimension bifurcates. For lakes observed in historical maps, this bifurcation occurs among lakes larger than 100 km 2 (fractal dimension 1.43 to 1.87 ). For lakes observed in satellite images this bifurcation occurs among lakes larger than ∼100 km 2 (fractal dimension 1.31 to 1.95 ). Tundra lakes with a fractal dimension close to 2 have a tendency to be self-similar with respect to their area–perimeter relationships. Area–perimeter measurements indicate that lakes with a length scale greater than 70 km 2 are power-law distributed. Preliminary analysis of changes in lake size over time in paired lakes (lakes that were visually matched in both the historical map and the satellite imagery) indicate that some lakes in our study region have increased in size over time, whereas others have decreased in size over time. Lake size change during this 39-year time interval can be up to half the size of the lake as recorded in the historical map. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Regional Quantitative Cover Mapping of Tundra Plant Functional Types in Arctic Alaska
Remote Sens. 2017, 9(10), 1024; https://doi.org/10.3390/rs9101024 - 04 Oct 2017
Cited by 15
Abstract
Ecosystem maps are foundational tools that support multi-disciplinary study design and applications including wildlife habitat assessment, monitoring and Earth-system modeling. Here, we present continuous-field cover maps for tundra plant functional types (PFTs) across ~125,000 km2 of Alaska’s North Slope at 30-m resolution. [...] Read more.
Ecosystem maps are foundational tools that support multi-disciplinary study design and applications including wildlife habitat assessment, monitoring and Earth-system modeling. Here, we present continuous-field cover maps for tundra plant functional types (PFTs) across ~125,000 km2 of Alaska’s North Slope at 30-m resolution. To develop maps, we collected a field-based training dataset using a point-intercept sampling method at 225 plots spanning bioclimatic and geomorphic gradients. We stratified vegetation by nine PFTs (e.g., low deciduous shrub, dwarf evergreen shrub, sedge, lichen) and summarized measurements of the PFTs, open water, bare ground and litter using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover). We then developed 73 spectral predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May–August) and five gridded environmental predictors (e.g., summer temperature, climatological snow-free date) to model cover of PFTs using the random forest data-mining algorithm. Model performance tended to be best for canopy-forming PFTs, particularly deciduous shrubs. Our assessment of predictor importance indicated that models for low-statured PFTs were improved through the use of seasonal composites from early and late in the growing season, particularly when similar PFTs were aggregated together (e.g., total deciduous shrub, herbaceous). Continuous-field maps have many advantages over traditional thematic maps, and the methods described here are well-suited to support periodic map updates in tandem with future field and Landsat observations. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Comparison of Gas Emission Crater Geomorphodynamics on Yamal and Gydan Peninsulas (Russia), Based on Repeat Very-High-Resolution Stereopairs
Remote Sens. 2017, 9(10), 1023; https://doi.org/10.3390/rs9101023 - 04 Oct 2017
Cited by 18
Abstract
Gas Emission Craters (GEC) represent a new phenomenon in permafrost regions discovered in the north of West Siberia. In this study we use very-high-resolution Worldview satellite stereopairs and Resurs-P images to reveal and measure the geomorphic features that preceded and followed GEC formation [...] Read more.
Gas Emission Craters (GEC) represent a new phenomenon in permafrost regions discovered in the north of West Siberia. In this study we use very-high-resolution Worldview satellite stereopairs and Resurs-P images to reveal and measure the geomorphic features that preceded and followed GEC formation on the Yamal and Gydan peninsulas. Analysis of DEMs allowed us to: (1) distinguish different terrain positions of the GEC, at the foot of a gentle slope (Yamal), and on an upper edge of a terrace slope; (2) notice that the formation of both Yamal and Gydan GECs were preceded by mound development; (3) measure a funnel-shaped upper part and a cylindrical lower part for each crater; (4) and measure the expansion and plan form modification of GECs. Although the general characteristics of both craters are similar, there are differences when comparing both key sites in detail. The height of the mound and diameter of the resulting GEC in Yamal exceeds that in Gydan; GEC-1 was surrounded by a well-developed parapet, while AntGEC did not show any considerable accumulative body. Thus, using very-high-resolution remote sensing data allowed us to discriminate geomorphic features and relief positions characteristic for GEC formation. GECs are a potential threat to commercial facilities in permafrost and indigenous settlements, especially because at present there is no statistically significant number of study objects to identify the local environmental conditions in which the formation of new GEC is possible. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions
Remote Sens. 2017, 9(7), 640; https://doi.org/10.3390/rs9070640 - 27 Jun 2017
Cited by 43
Abstract
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial [...] Read more.
Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (−0.69%), Western Alaska (−2.82%), and Kolyma Lowland (−0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Trends in Greenness and Snow Cover in Alaska’s Arctic National Parks, 2000–2016
Remote Sens. 2017, 9(6), 514; https://doi.org/10.3390/rs9060514 - 23 May 2017
Cited by 7
Abstract
In cold-limited arctic environments, the duration and timing of the snow cover and the vegetation green season have major ecological implications. I monitored the phenology of snow cover and greenness using MODIS Terra satellite data for the years 2000 to 2016 in the [...] Read more.
In cold-limited arctic environments, the duration and timing of the snow cover and the vegetation green season have major ecological implications. I monitored the phenology of snow cover and greenness using MODIS Terra satellite data for the years 2000 to 2016 in the 5 National Parks of northern Alaska, USA. Mann-Kendall trend tests showed that the end of the continuous snow season and midpoint of spring green-up became significantly earlier in parts of the study area over the 16-year period. Using the observed relationship between thaw degree-days at Kotzebue, Alaska and dates of snow-off and half green-up in nearby lowland tundra for the 16 years of MODIS data, I reconstructed the dates of snow-off and half green-up from long-term Kotzebue weather records back to 1937. The average snow-off and green-up dates probably became earlier by about 6 days over this 80-year time interval. Remote sensing of fall vegetation senescence and establishment of the snow cover were less reliable than the spring events due to cloudiness and low sun angles. The annual maximum normalized difference vegetation index (NDVI) generally did not increase significantly from 2001 to 2016, except in places where vegetation was recovering from forest fires. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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Open AccessArticle
Multi-Decadal Surface Water Dynamics in North American Tundra
Remote Sens. 2017, 9(5), 497; https://doi.org/10.3390/rs9050497 - 18 May 2017
Cited by 16
Abstract
Over the last several decades, warming in the Arctic has outpaced the already impressive increases in global mean temperatures. The impact of these increases in temperature has been observed in a multitude of ecological changes in North American tundra including changes in vegetative [...] Read more.
Over the last several decades, warming in the Arctic has outpaced the already impressive increases in global mean temperatures. The impact of these increases in temperature has been observed in a multitude of ecological changes in North American tundra including changes in vegetative cover, depth of active layer, and surface water extent. The low topographic relief and continuous permafrost create an ideal environment for the formation of small water bodies—a definitive feature of tundra surface. In this study, water bodies in Nunavut territory in northern Canada were mapped using a long-term record of remotely sensed observations at 30 m spatial resolution from the Landsat suite of instruments. The temporal trajectories of water extent between 1985 and 2015 were assessed. Over 675,000 water bodies have been identified over the 31-year study period with over 168,000 showing a significant (p < 0.05) trend in surface area. Approximately 55% of water bodies with a significant trend were increasing in size while the remaining 45% were decreasing in size. The overall net trend for water bodies with a significant trend is 0.009 ha year−1 per water body. Full article
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
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