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Special Issue "Remote Sensing of Changing Northern High Latitude Ecosystems"

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

Deadline for manuscript submissions: closed (30 June 2015)

Special Issue Editors

Guest Editor
Dr. Santonu Goswami

Climate Change Science Institute (CCSI) and Environmental Sciences Division, Oak Ridge National Laboratory, P.O. Box: 2008, MS 6301 Oak Ridge, TN 37831, USA
Website | E-Mail
Phone: 8652411296 Fax: 1 865-241-3685
Fax: +865 241 9910
Interests: impacts of global change on high latitude terrestrial ecosystem structure and functions using remote sensing and geospatial techniques; scaling issues in ecology; carbon cycle science; environmental cyberinfrastructure
Guest Editor
Dr. Daniel J. Hayes

Climate Change Science Institute (CCSI) and Environmental Sciences, Division Oak Ridge National Laboratory, P.O. Box: 2008, MS 6301 Oak Ridge, TN 37831, USA
Website | E-Mail
Fax: +865 574 2232
Interests: carbon cycle science; impact of disturbance processes on ecosystem carbon dynamics; ecosystem modeling; remote sensing and geospatial analysis
Guest Editor
Dr. Guido Grosse

Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Telegrafenberg A45, 14473 Potsdam, Germany
Website | E-Mail
Interests: Arctic terrestrial landscape dynamics; Remote sensing of permafrost regions; Permafrost thaw; Permafrost geomorphology and hydrology; High latitude soil carbon dynamics GIS
Guest Editor
Dr. Benjamin Jones

USGS Alaska Science Center, 4210 University Dr. Anchorage, AK 99508, USA
Website | E-Mail
Fax: +907 786 7150
Interests: multi-sensor remote sensing of arctic landscapes; combining ground-based and space-based observations; thermokarst and other thaw related landscape dynamics; arctic lakes

Special Issue Information

Dear Colleagues,

Northern high-latitude terrestrial and aquatic ecosystems are undergoing unprecedented change in structure and function as a result of rapid climate warming. The response is manifest in myriad ways, including the melting of ice and the thawing of permafrost, an increase in the frequency and severity of wildfire, as well as through changes in vegetation productivity, lake abundance and cover, runoff patterns, lake and river ice thickness and cover duration, and snow cover. Such changes have substantially altered energy, water and biogeochemical cycling in the region, which has important global-scale consequences for climate and society.
Scientists need to understand the indicators of these changes, and use existing and new technologies and methodologies to observe and monitor them. Remote sensing offers repeat observations of dynamic land surface properties from local to regional scales over multi-decadal time periods. Therefore, the changes occurring in the northern high latitude ecosystems can be characterized and quantified using remote sensing techniques based on information from various active and passive sensors on ground-, airborne- and satellite- based platforms. Remote sensing can also play a critical role for scaling field measurements to landscape and regional scales, parameterizing and evaluating models, and testing hypothesis of dynamic landscape processes in these vulnerable ecosystems.
Recognizing the sensitivity, vulnerability and global importance of these changes, there is a growing interest in studying northern high latitude ecosystems. The goal is to better understand and quantify changes and provide both the modeling community and decision-makers the necessary information to improve climate prediction and inform the development of policies for a sustainable future. Several high-profile national and international research activities, currently underway or in the planning stage (e.g. NGEE-Arctic, DUE Permafrost, ABoVE), focus in part on observing and monitoring rapid change in northern high latitude ecosystems using remote sensing tools and techniques
The special issue seeks to invite contributions from studies that focus on understanding the dynamic landscape processes in northern high latitude ecosystems using remote sensing information from multi-scale platforms, i.e. ground based, aircraft and various satellite platforms. Contributions that demonstrate the development of new techniques, data products and/or highlight the challenges of remote sensing in high latitudes are also encouraged.

Dr. Santonu Goswami
Dr. Daniel J. Hayes
Dr. Guido Grosse
Mr. Benjamin Jones
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly 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 1600 CHF (Swiss Francs).

Keywords

  • high-latitude ecosystems
  • arctic and boreal
  • remote sensing
  • ecosystem modeling
  • terrestrial and aquatic ecosystems
  • disturbance
  • vegetation dynamics
  • phenology
  • permafrost
  • thaw lake
  • climate change
  • carbon cycle

Published Papers (22 papers)

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Research

Open AccessArticle Examination of Surface Temperature Modification by Open-Top Chambers along Moisture and Latitudinal Gradients in Arctic Alaska Using Thermal Infrared Photography
Remote Sens. 2016, 8(1), 54; doi:10.3390/rs8010054
Received: 25 July 2015 / Revised: 22 December 2015 / Accepted: 30 December 2015 / Published: 11 January 2016
PDF Full-text (6170 KB) | HTML Full-text | XML Full-text
Abstract
Passive warming manipulation methodologies, such as open-top chambers (OTCs), are a meaningful approach for interpretation of impacts of climate change on the Arctic tundra biome. The magnitude of OTC warming has been studied extensively, revealing an average plot-level warming of air temperature that
[...] Read more.
Passive warming manipulation methodologies, such as open-top chambers (OTCs), are a meaningful approach for interpretation of impacts of climate change on the Arctic tundra biome. The magnitude of OTC warming has been studied extensively, revealing an average plot-level warming of air temperature that ranges between 1 and 3 °C as measured by shielded resistive sensors or thermocouples. Studies have also shown that the amount of OTC warming depends in part on location climate, vegetation, and soil properties. While digital infrared thermometers have been employed in a few comparisons, most of the focus of the effectiveness of OTC warming has been on air or soil temperature rather than tissue or surface temperatures, which directly translate to metabolism. Here we used thermal infrared (TIR) photography to quantify tissue and surface temperatures and their spatial variability at a previously unavailable resolution (3–6 mm2). We analyzed plots at three locations that are part of the International Tundra Experiment (ITEX)-Arctic Observing Network (AON-ITEX) network along both moisture and latitudinal gradients spanning from the High Arctic (Barrow, AK, USA) to the Low Arctic (Toolik Lake, AK, USA). Our results show a range of OTC surface warming from 2.65 to 1.27 °C (31%–10%) at our three sites. The magnitude of surface warming detected by TIR imagery in this study was comparable to increases in air temperatures previously reported for these sites. However, the thermal images revealed wide ranges of surface temperatures within the OTCs, with some surfaces well above ambient unevenly distributed within the plots under sunny conditions. We note that analyzing radiometric temperature may be an alternative for future studies that examine data acquired at the same time of day from sites that are in close geographic proximity to avoid the requirement of emissivity or atmospheric correction for validation of results. We foresee future studies using TIR photography to describe species-level thermodynamics that could prove highly valuable toward a better understanding of species-specific responses to climate change in the Arctic. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Differentiating among Four Arctic Tundra Plant Communities at Ivotuk, Alaska Using Field Spectroscopy
Remote Sens. 2016, 8(1), 51; doi:10.3390/rs8010051
Received: 19 September 2015 / Revised: 23 December 2015 / Accepted: 25 December 2015 / Published: 8 January 2016
PDF Full-text (4134 KB) | HTML Full-text | XML Full-text
Abstract
Warming in the Arctic has resulted in changes in the distribution and composition of vegetation communities. Many of these changes are occurring at fine spatial scales and at the level of individual species. Broad-band, coarse-scale remote sensing methods are commonly used to assess
[...] Read more.
Warming in the Arctic has resulted in changes in the distribution and composition of vegetation communities. Many of these changes are occurring at fine spatial scales and at the level of individual species. Broad-band, coarse-scale remote sensing methods are commonly used to assess vegetation changes in the Arctic, and may not be appropriate for detecting these fine-scale changes; however, the use of hyperspectral, high resolution data for assessing vegetation dynamics remains scarce. The aim of this paper is to assess the ability of field spectroscopy to differentiate among four vegetation communities in the Low Arctic of Alaska. Primary data were collected from the North Slope site of Ivotuk, Alaska (68.49°N, 155.74°W) and analyzed using spectrally resampled hyperspectral narrowbands (HNBs). A two-step sparse partial least squares (SPLS) and linear discriminant analysis (LDA) was used for community separation. Results from Ivotuk were then used to predict community membership at five other sites along the Dalton Highway in Arctic Alaska. Overall classification accuracy at Ivotuk ranged from 84%–94% and from 55%–91% for the Dalton Highway test sites. The results of this study suggest that hyperspectral data acquired at the field level, along with the SPLS and LDA methodology, can be used to successfully discriminate among Arctic tundra vegetation communities in Alaska, and present an improvement over broad-band, coarse-scale methods for community classification. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle An Automated Approach for Mapping Persistent Ice and Snow Cover over High Latitude Regions
Remote Sens. 2016, 8(1), 16; doi:10.3390/rs8010016
Received: 30 June 2015 / Revised: 4 December 2015 / Accepted: 21 December 2015 / Published: 25 December 2015
Cited by 3 | PDF Full-text (5891 KB) | HTML Full-text | XML Full-text
Abstract
We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat
[...] Read more.
We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study
Remote Sens. 2015, 7(10), 13664-13691; doi:10.3390/rs71013664
Received: 17 June 2015 / Revised: 28 September 2015 / Accepted: 10 October 2015 / Published: 20 October 2015
Cited by 1 | PDF Full-text (2483 KB) | HTML Full-text | XML Full-text
Abstract
The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in
[...] Read more.
The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013–2014 and 2014–2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013–2014 winter had the higher variation than the 2014–2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska
Remote Sens. 2015, 7(10), 13692-13710; doi:10.3390/rs71013692
Received: 29 June 2015 / Revised: 13 October 2015 / Accepted: 14 October 2015 / Published: 20 October 2015
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Abstract
Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased
[...] Read more.
Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased glacial runoff have been shown to reduce salmon production via reduced abundance of zooplankton and macroinvertebrates. In this study, we reconstruct long-term, lake-wide water clarity for Lake Clark using the Landsat TM and ETM+ surface reflectance products (1985–2014) and in situ water clarity data collected between 2009 and 2013. Analysis of a Landsat scene acquired in 2009, coincident with in situ measurements in the lake, and uncertainty analysis with four scenes acquired within two weeks of field data collection showed that Band 3 surface reflectance was the best indicator of turbidity (r2 = 0.55, RMSE << 0.01). We then processed 151 (98 partial- and 53 whole-lake) Landsat scenes using this relation and detected no significant long-term trend in mean turbidity for Lake Clark between 1991 and 2014. We did, however, detect interannual variation that exhibited a non-significant (r2 = 0.20) but positive correlation (r = 0.20) with regional mean summer air temperature and found the month of May exhibited a significant positive trend (r2 = 0.68, p = 0.02) in turbidity between 2000 and 2014. This study demonstrates the utility of hindcasting turbidity in a glacially influenced lake using the Landsat surface reflectance products. It may also help land and resource managers reconstruct turbidity records for lakes that lack in situ monitoring, and may be useful in predicting future water clarity conditions based on projected climate scenarios. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Deriving Snow Cover Metrics for Alaska from MODIS
Remote Sens. 2015, 7(10), 12961-12985; doi:10.3390/rs71012961
Received: 27 May 2015 / Revised: 11 September 2015 / Accepted: 26 September 2015 / Published: 30 September 2015
Cited by 3 | PDF Full-text (11511 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates
[...] Read more.
Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products provide an opportunity for determining snow onset and melt dates across broad geographic regions; however, cloud cover and polar darkness are limiting factors at higher latitudes. This study presents snow onset and melt dates for Alaska, portions of western Canada and the Russian Far East derived from Terra MODIS snow cover daily 500 m grid data (MOD10A1) and evaluates our method for filling data gaps caused by clouds or polar darkness. Pixels classified as cloud or no data were reclassified by: spatial filtering using neighboring pixel values; temporal filtering using pixel values for days before/after cloud cover; and snow-cycle filtering based on a time series assessment of a pixel’s position within snow accumulation, cover or melt periods. During the 2012 snow year, these gap-filling methods reduced cloud pixels from 27.7% to 3.1%. A total of 12 metrics (e.g., date of first and last snow, date of persistent snow cover and periods of intermittence) for each pixel were calculated by snow year. A comparison of MODIS-derived snow onset and melt dates with in situ observations from 244 weather stations generally showed an early bias in MODIS-derived dates and an effect of increasing cloudiness exacerbating bias. Our results show that mean regional duration of seasonal snow cover is 179–311 days/year and that snow cover is often intermittent, with 41% of the area experiencing ≥2 snow-covered periods during a snow season. Other regional-scale patterns in the timing of snow onset and melt are evident in the yearly 500 m gridded products publically available at http://static.gina.alaska.edu/NPS_products/MODIS_snow/. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Spatial and Temporal Changes in Vegetation Phenology at Middle and High Latitudes of the Northern Hemisphere over the Past Three Decades
Remote Sens. 2015, 7(8), 10973-10995; doi:10.3390/rs70810973
Received: 8 April 2015 / Revised: 15 August 2015 / Accepted: 20 August 2015 / Published: 24 August 2015
Cited by 3 | PDF Full-text (1627 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation phenology is a key biological indicator for monitoring terrestrial ecosystems and global change, and regions with the most obvious phenological changes in vegetation are primarily located at high latitudes and altitudes. Over the past three decades, investigations of obvious phenological changes in
[...] Read more.
Vegetation phenology is a key biological indicator for monitoring terrestrial ecosystems and global change, and regions with the most obvious phenological changes in vegetation are primarily located at high latitudes and altitudes. Over the past three decades, investigations of obvious phenological changes in vegetation at middle and high latitudes in the Northern Hemisphere have provided significant contributions to understanding global climate change. In this study, phenological parameters were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) to analyze the spatial and temporal characteristics of vegetation phenological changes above 40°N in the Northern Hemisphere from 1982–2013. The results showed that the start of season (SOS) was significantly advanced (−2.2 ± 0.6 days·decade−1, p < 0.05) and that the end of season (EOS) was slightly delayed (0.78 ± 0.6 days·decade−1, p = 0.21) over the entire study area in the initial 21 years (1982–2002). When the time scale was extended to 2013, the change rate of the SOS and EOS was significantly reduced; in addition, the SOS was delayed (3.2 ± 1.7 days·decade−1, p < 0.05), and the EOS was advanced (4.5 ± 0.9 days·decade−1, p < 0.05) over the entire study area in the last 11 years (2003–2013). The trends of advanced SOS and delayed EOS over the past three decades were slower than those over the initial two decades on a hemispheric scale. The change trends showed obvious variability with different vegetation types and were greater for woody plants than for herbaceous plants. For broad-leaved forest, the SOS was significantly advanced (2 ± 0.5 days·decade−1, p < 0.05) and the EOS was significantly delayed (2.7 ± 0.6 days·decade−1, p < 0.05) from 1982–2013. The trend of delayed EOS was greater than that of advanced SOS for different vegetation types. With respect to the spatial distribution of phenological trends in the Northern Hemisphere, the trends of advanced SOS and delayed EOS were strongest in Europe followed by North America, and the trends were least significant in Asia. Coniferous forest, shrub forest, grassland, and the entire study area have the same change trends for the two time periods (1982–2002 and 2003–2013), and the increased rate of the phenology parameters has decelerated over the most recent decade. The length of season (LOS) of broad-leaved forest and mixed forest over the past 32 years shows a strong increased trend, and simultaneously, the SOS and EOS show an advanced trend and a delayed trend, respectively Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Warming, Sheep and Volcanoes: Land Cover Changes in Iceland Evident in Satellite NDVI Trends
Remote Sens. 2015, 7(8), 9492-9506; doi:10.3390/rs70809492
Received: 20 February 2015 / Revised: 12 July 2015 / Accepted: 20 July 2015 / Published: 24 July 2015
Cited by 1 | PDF Full-text (7554 KB) | HTML Full-text | XML Full-text
Abstract
In a greening Arctic, Iceland stands out as an area with very high increases in the AVHRR Normalized Difference Vegetation Index (NDVI, 1982–2010). We investigated the possible sources of this anomalous greening in Iceland’s dynamic landscape, analyzing changes due to volcanism and warming
[...] Read more.
In a greening Arctic, Iceland stands out as an area with very high increases in the AVHRR Normalized Difference Vegetation Index (NDVI, 1982–2010). We investigated the possible sources of this anomalous greening in Iceland’s dynamic landscape, analyzing changes due to volcanism and warming temperatures, and the effects of agricultural and industrial land use changes. The analysis showed the increases were likely due to reductions in grazing in erosion-prone rangelands, extensive reclamation and afforestation efforts, as well as a response to warming climate, including glacial retreat. Like Scandinavia and much of the rest of the Arctic, Iceland has shown a recent reduction in NDVI since 2002, but still above pre-2000 levels. Theil-Sen robust regression analysis of MODIS NDVI trends from 2002 to 2013 showed Iceland had a slightly negative NDVI trend of 0.003 NDVI units/year (p < 0.05), with significant decreases in an area three times greater (29,809 km2) than that with increases (9419 km2). Specific areas with large decreases in NDVI during the last decade were due to the formation of a large reservoir as a part of a hydroelectric power project (Kárahnjúkar, 2002–2009), and due to ashfall from two volcanic eruptions (Eyjafjallajökull, 2010; Grímsvötn, 2011). Increases in NDVI in the last decade were found in erosion control areas, around retreating glaciers, and in other areas of plant colonization following natural disturbance. Our analysis demonstrates the effectiveness of MODIS NDVI for identifying the causes of changes in land cover, and confirms the reduction in NDVI in the last decade using both the AVHRR and MODIS satellite data. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Potential of C and X Band SAR for Shrub Growth Monitoring in Sub-Arctic Environments
Remote Sens. 2015, 7(7), 9410-9430; doi:10.3390/rs70709410
Received: 8 June 2015 / Revised: 8 July 2015 / Accepted: 15 July 2015 / Published: 22 July 2015
Cited by 4 | PDF Full-text (4267 KB) | HTML Full-text | XML Full-text
Abstract
The Arctic and sub-Arctic environments have seen a rapid growth of shrub vegetation at the expense of the Arctic tundra in recent decades. In order to develop better tools to assess and understand this phenomenon, the sensitivity of multi-polarized SAR backscattering at C
[...] Read more.
The Arctic and sub-Arctic environments have seen a rapid growth of shrub vegetation at the expense of the Arctic tundra in recent decades. In order to develop better tools to assess and understand this phenomenon, the sensitivity of multi-polarized SAR backscattering at C and X band to shrub density and height is studied under various conditions. RADARSAT-2 and TerraSAR-X images were acquired from November 2011 to March 2012 over the Umiujaq community in northern Quebec (56.55°N, 76.55°W) and compared to in situ measurements of shrub vegetation density and height collected during the summer of 2009. The results show that σ0 is sensitive to changes in shrub coverage up to 20% and is sensitive to changes in shrub height up to around 1 m. The cross-polarized backscattering (σ0 HV ) displays the best sensitivity to both shrub height and density, and RADARSAT-2 is more sensitive to shrub height, as TerraSAR-X tends to saturate more rapidly with increasing volume scattering from the shrub branches. These results demonstrate that SAR data could provide essential information, not only on the spatial expansion of shrub vegetation, but also on its vertical growth, especially at early stages of colonization. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Remotely Sensed Active Layer Thickness (ReSALT) at Barrow, Alaska Using Interferometric Synthetic Aperture Radar
Remote Sens. 2015, 7(4), 3735-3759; doi:10.3390/rs70403735
Received: 3 August 2014 / Accepted: 10 February 2015 / Published: 27 March 2015
Cited by 6 | PDF Full-text (59388 KB) | HTML Full-text | XML Full-text
Abstract
Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. Here we evaluated the Remotely Sensed Active Layer Thickness (ReSALT) product that
[...] Read more.
Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. Here we evaluated the Remotely Sensed Active Layer Thickness (ReSALT) product that uses the Interferometric Synthetic Aperture Radar technique to measure seasonal surface subsidence and infer ALT around Barrow, Alaska. We compared ReSALT with ground-based ALT obtained using probing and calibrated, 500 MHz Ground Penetrating Radar at multiple sites around Barrow. ReSALT accurately reproduced observed ALT within uncertainty of the GPR and probing data in ~76% of the study area. However, ReSALT was less than observed ALT in ~22% of the study area with well-drained soils and in ~1% of the area where soils contained gravel. ReSALT was greater than observed ALT in some drained thermokarst lake basins representing ~1% of the area. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Mapping Forest Height in Alaska Using GLAS, Landsat Composites, and Airborne LiDAR
Remote Sens. 2014, 6(12), 12409-12426; doi:10.3390/rs61212409
Received: 27 June 2014 / Revised: 22 November 2014 / Accepted: 3 December 2014 / Published: 10 December 2014
Cited by 4 | PDF Full-text (4227 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has
[...] Read more.
Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Establishing a Baseline for Regional Scale Monitoring of Eelgrass (Zostera marina) Habitat on the Lower Alaska Peninsula
Remote Sens. 2014, 6(12), 12447-12477; doi:10.3390/rs61212447
Received: 20 May 2014 / Revised: 27 November 2014 / Accepted: 27 November 2014 / Published: 10 December 2014
PDF Full-text (5256 KB) | HTML Full-text | XML Full-text
Abstract
Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need
[...] Read more.
Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass (Zostera marina) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle A Simple Method for Retrieving Understory NDVI in Sparse Needleleaf Forests in Alaska Using MODIS BRDF Data
Remote Sens. 2014, 6(12), 11936-11955; doi:10.3390/rs61211936
Received: 26 June 2014 / Revised: 15 November 2014 / Accepted: 17 November 2014 / Published: 1 December 2014
Cited by 6 | PDF Full-text (6515 KB) | HTML Full-text | XML Full-text
Abstract
Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas because the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore, many efforts have been made to include understory
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Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas because the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore, many efforts have been made to include understory properties in LAI estimation algorithms. Compared with the conventional data bank method, estimation of forest understory properties from satellite data is superior in studies at a global or continental scale over long periods. However, implementation of the current remote sensing method based on multi-angular observations is complicated. As an alternative, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the fact that the bidirectional variation in NDVIu is smaller than that in canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using pixels within a 5 × 5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position in which the stand NDVI had the smallest angular variation. Validation by a noise-free simulation data set yielded high agreement between estimated and true NDVIu, with R2 and RMSE of 0.99 and 0.03, respectively. Using the MODIS BRDF data, we achieved an estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively) and reasonable seasonal patterns of NDVIu in 2010 to 2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests and ultimately to benefit studies on carbon cycle modeling over high-latitude areas. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 1. Visualization
Remote Sens. 2014, 6(11), 11533-11557; doi:10.3390/rs61111533
Received: 26 June 2014 / Revised: 31 October 2014 / Accepted: 4 November 2014 / Published: 20 November 2014
Cited by 6 | PDF Full-text (31263 KB) | HTML Full-text | XML Full-text
Abstract
Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here
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Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here we present a simple, but effective means of visually assessing landscape disturbances in northern environments using trend analysis of Landsat satellite image stacks. Linear trends of the Tasseled Cap brightness, greenness, and wetness indices, when composited into an RGB image, effectively distinguish diverse landscape changes based on additive color logic. Using a variety of reference datasets within Northwest Territories, Canada, we show that the trend composites are effective for identifying wildfire regeneration, tundra greening, fluvial dynamics, thermokarst processes including lake surface area changes and retrogressive thaw slumps, and the footprint of resource development operations and municipal development. Interpretation of the trend composites is aided by a color wheel legend and contextual information related to the size, shape, and location of change features. A companion paper in this issue (Olthof and Fraser) focuses on quantitative methods for classifying these changes. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification
Remote Sens. 2014, 6(11), 11558-11578; doi:10.3390/rs61111558
Received: 27 June 2014 / Revised: 27 October 2014 / Accepted: 27 October 2014 / Published: 20 November 2014
Cited by 5 | PDF Full-text (3767 KB) | HTML Full-text | XML Full-text
Abstract
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories
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Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone
Remote Sens. 2014, 6(10), 10070-10088; doi:10.3390/rs61010070
Received: 30 June 2014 / Revised: 16 September 2014 / Accepted: 29 September 2014 / Published: 21 October 2014
Cited by 6 | PDF Full-text (3305 KB) | HTML Full-text | XML Full-text
Abstract
Satellite-based estimates of vegetation structure capture broad-scale vegetation characteristics as well as differences in vegetation structure at plot-scales. Active remote sensing from laser altimetry and radar systems is regularly used to measure vegetation height and infer vegetation structural attributes, however, the current uncertainty
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Satellite-based estimates of vegetation structure capture broad-scale vegetation characteristics as well as differences in vegetation structure at plot-scales. Active remote sensing from laser altimetry and radar systems is regularly used to measure vegetation height and infer vegetation structural attributes, however, the current uncertainty of their spaceborne measurements is likely to mask actual plot-scale differences in vertical structures in sparse forests. In the taiga (boreal forest)—tundra ecotone (TTE) the accumulated effect of subtle plot-scale differences in vegetation height across broad-scales may be significant. This paper examines the uncertainty of plot-scale forest canopy height measurements in northern Siberia Larix stands by combining complementary canopy surface elevations derived from satellite photogrammetry and ground elevations derived from the Geosciences Laser Altimeter System (GLAS) from the ICESat-1 satellite. With a linear model, spaceborne-derived canopy height measurements at the plot-scale predicted TTE stand height ~5 m–~10 m tall (R2 = 0.55, bootstrapped 95% confidence interval of R2 = 0.36–0.74) with an uncertainty ranging from ±0.86 m–1.37 m. A larger sample may mitigate the broad uncertainty of the model fit, however, the methodology provides a means for capturing plot-scale canopy height and its uncertainty from spaceborne data at GLAS footprints in sparse TTE forests and may serve as a basis for scaling up plot-level TTE vegetation height measurements to forest patches. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Spatio-Temporal Analysis of Gyres in Oriented Lakes on the Arctic Coastal Plain of Northern Alaska Based on Remotely Sensed Images
Remote Sens. 2014, 6(10), 9170-9193; doi:10.3390/rs6109170
Received: 14 July 2014 / Revised: 13 September 2014 / Accepted: 15 September 2014 / Published: 26 September 2014
Cited by 2 | PDF Full-text (5566 KB) | HTML Full-text | XML Full-text
Abstract
The formation of oriented thermokarst lakes on the Arctic Coastal Plain of northern Alaska has been the subject of debate for more than half a century. The striking elongation of the lakes perpendicular to the prevailing wind direction has led to the development
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The formation of oriented thermokarst lakes on the Arctic Coastal Plain of northern Alaska has been the subject of debate for more than half a century. The striking elongation of the lakes perpendicular to the prevailing wind direction has led to the development of a preferred wind-generated gyre hypothesis, while other hypotheses include a combination of sun angle, topographic aspect, and/or antecedent conditions. A spatio-temporal analysis of oriented thermokarst lake gyres with recent (Landsat 8) and historical (Landsat 4, 5, 7 and ASTER) satellite imagery of the Arctic Coastal Plain of northern Alaska indicates that wind-generated gyres are both frequent and regionally extensive. Gyres are most common in lakes located near the Arctic coast after several days of sustained winds from a single direction, typically the northeast, and decrease in number landward with decreasing wind energy. This analysis indicates that the conditions necessary for the Carson and Hussey (1962) wind-generated gyre for oriented thermokarst lake formation are common temporally and regionally and correspond spatially with the geographic distribution of oriented lakes on the Arctic Coastal Plain. Given an increase in the ice-free season for lakes as well as strengthening of the wind regime, the frequency and distribution of lake gyres may increase. This increase has implications for changes in northern high latitude aquatic ecosystems, particularly if wind-generated gyres promote permafrost degradation and thermokarst lake expansion. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Assessing Seasonal Backscatter Variations with Respect to Uncertainties in Soil Moisture Retrieval in Siberian Tundra Regions
Remote Sens. 2014, 6(9), 8718-8738; doi:10.3390/rs6098718
Received: 30 June 2014 / Revised: 26 August 2014 / Accepted: 2 September 2014 / Published: 17 September 2014
Cited by 7 | PDF Full-text (5463 KB) | HTML Full-text | XML Full-text
Abstract
Knowledge of surface hydrology is essential for many applications, including studies that aim to understand permafrost response to changing climate and the associated feedback mechanisms. Advanced remote sensing techniques make it possible to retrieve a range of land-surface variables, including radar retrieved soil
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Knowledge of surface hydrology is essential for many applications, including studies that aim to understand permafrost response to changing climate and the associated feedback mechanisms. Advanced remote sensing techniques make it possible to retrieve a range of land-surface variables, including radar retrieved soil moisture (SSM). It has been pointed out before that soil moisture retrieval from satellite data can be challenging at high latitudes, which correspond to remote areas where ground data are scarce and the applicability of satellite data of this type is essential. This study investigates backscatter variability other than associated with changing soil moisture in order to examine the possible impact on soil moisture retrieval. It focuses on issues specific to SSM retrieval in the Arctic, notably variations related to tundra lakes. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (WS, 120 m) data are used to understand and quantify impacts on Metop (AAdvanced Scatterometer (ASCAT, 25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to ASAR WS availability, high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. Backscatter variations are analyzed with respect to the ASCAT footprint area. It can be shown that the low model agreement is related to water fraction in most cases. No difference could be detected between periods with floating ice (in snow off situation) and ice free periods at the chosen sites. The mean footprint backscatter is however impacted by partial short term surface roughness change. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91) Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Land Cover Characterization and Classification of Arctic Tundra Environments by Means of Polarized Synthetic Aperture X- and C-Band Radar (PolSAR) and Landsat 8 Multispectral Imagery — Richards Island, Canada
Remote Sens. 2014, 6(9), 8565-8593; doi:10.3390/rs6098565
Received: 10 June 2014 / Revised: 20 August 2014 / Accepted: 22 August 2014 / Published: 11 September 2014
Cited by 10 | PDF Full-text (27180 KB) | HTML Full-text | XML Full-text
Abstract
In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The
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In this work the potential of polarimetric Synthetic Aperture Radar (PolSAR) data of dual-polarized TerraSAR-X (HH/VV) and quad-polarized Radarsat-2 was examined in combination with multispectral Landsat 8 data for unsupervised and supervised classification of tundra land cover types of Richards Island, Canada. The classification accuracies as well as the backscatter and reflectance characteristics were analyzed using reference data collected during three field work campaigns and include in situ data and high resolution airborne photography. The optical data offered an acceptable initial accuracy for the land cover classification. The overall accuracy was increased by the combination of PolSAR and optical data and was up to 71% for unsupervised (Landsat 8 and TerraSAR-X) and up to 87% for supervised classification (Landsat 8 and Radarsat-2) for five tundra land cover types. The decomposition features of the dual and quad-polarized data showed a high sensitivity for the non-vegetated substrate (dominant surface scattering) and wetland vegetation (dominant double bounce and volume scattering). These classes had high potential to be automatically detected with unsupervised classification techniques. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Spatial and Temporal Variability in the Onset of the Growing Season on Svalbard, Arctic Norway — Measured by MODIS-NDVI Satellite Data
Remote Sens. 2014, 6(9), 8088-8106; doi:10.3390/rs6098088
Received: 1 April 2014 / Revised: 21 August 2014 / Accepted: 22 August 2014 / Published: 27 August 2014
Cited by 2 | PDF Full-text (9682 KB) | HTML Full-text | XML Full-text
Abstract
The Arctic is among the regions with the most rapid changes in climate and has the expected highest increase in temperature. Changes in the timing of phenological phases, such as onset of the growing season observed from remote sensing, are among the most
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The Arctic is among the regions with the most rapid changes in climate and has the expected highest increase in temperature. Changes in the timing of phenological phases, such as onset of the growing season observed from remote sensing, are among the most sensitive bio-indicators of climate change. The study area here is the High Arctic archipelago of Svalbard, located between 76°30ʹ and 80°50ʹN. The goal of this study was to use MODIS Terra data (the MOD09Q1 and MOD09A1 surface reflectance products, both with 8-day temporal composites) to map the onset of the growing season on Svalbard for the 2000–2013 period interpreted from field observations. Due to a short and intense period with greening-up and frequent cloud cover, all the cloud free data is needed, which requires reliable cloud masks. We used a combination of three cloud removing methods (State QA values, own algorithms, and manual removal). This worked well, but is time-consuming as it requires manual interpretation of cloud cover. The onset of the growing season was then mapped by a NDVI threshold method, which showed high correlation (r2 = 0.60, n = 25, p < 0.001) with field observations of flowering of Salix polaris (polar willow). However, large bias was found between NDVI-based mapped onset and field observations in bryophyte-dominated areas, which indicates that the results in these parts must be interpreted with care. On average for the 14-year period, the onset of the growing season occurs after July 1st in 68.4% of the vegetated areas of Svalbard. The mapping revealed large variability between years. The years 2000 and 2008 were extreme in terms of late onset of the growing season, and 2002 and 2013 had early onset. Overall, no clear trend in onset of the growing season for the 2000–2013 period was found. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
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Open AccessArticle Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures
Remote Sens. 2014, 6(5), 4582-4599; doi:10.3390/rs6054582
Received: 26 March 2014 / Revised: 12 May 2014 / Accepted: 13 May 2014 / Published: 21 May 2014
Cited by 3 | PDF Full-text (584 KB) | HTML Full-text | XML Full-text
Abstract
The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using
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The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS) has been proposed as a tool for the detection of small pioneer trees for such vast areas using laser height and intensity data. The main objective of the present study was to assess a possible improvement in the performance of classifying tree and nontree laser echoes from high-density ALS data. The data were collected along a 1000 km long transect stretching from southern to northern Norway. Different geostatistical and statistical measures derived from laser height and intensity values were used to extent and potentially improve more simple models ignoring the spatial context. Generalised linear models (GLM) and support vector machines (SVM) were employed as classification methods. Total accuracies and Cohen’s kappa coefficients were calculated and compared to those of simpler models from a previous study. For both classification methods, all models revealed total accuracies similar to the results of the simpler models. Concerning classification performance, however, the comparison of the kappa coefficients indicated a significant improvement for some models both using GLM and SVM, with classification accuracies >94%. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)
Open AccessArticle Pan-Arctic Climate and Land Cover Trends Derived from Multi-Variate and Multi-Scale Analyses (1981–2012)
Remote Sens. 2014, 6(3), 2296-2316; doi:10.3390/rs6032296
Received: 22 November 2013 / Revised: 19 February 2014 / Accepted: 5 March 2014 / Published: 12 March 2014
Cited by 10 | PDF Full-text (1842 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Arctic ecosystems have been afflicted by vast changes in recent decades. Changes in temperature, as well as precipitation, are having an impact on snow cover, vegetation productivity and coverage, vegetation seasonality, surface albedo, and permafrost dynamics. The coupled climate-vegetation change in the arctic
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Arctic ecosystems have been afflicted by vast changes in recent decades. Changes in temperature, as well as precipitation, are having an impact on snow cover, vegetation productivity and coverage, vegetation seasonality, surface albedo, and permafrost dynamics. The coupled climate-vegetation change in the arctic is thought to be a positive feedback in the Earth system, which can potentially further accelerate global warming. This study focuses on the co-occurrence of temperature, precipitation, snow cover, and vegetation greenness trends between 1981 and 2012 in the pan-arctic region based on coarse resolution climate and remote sensing data, as well as ground stations. Precipitation significantly increased during summer and fall. Temperature had the strongest increase during the winter months (twice than during the summer months). The snow water equivalent had the highest trends during the transition seasons of the year. Vegetation greenness trends are characterized by a constant increase during the vegetation-growing period. High spatial resolution remote sensing data were utilized to map structural vegetation changes between 1973 and 2012 for a selected test region in Northern Siberia. An intensification of woody vegetation cover at the taiga-tundra transition area was found. The observed co-occurrence of climatic and ecosystem changes is an example of the multi-scale feedbacks in the arctic ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Changing Northern High Latitude Ecosystems)

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