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Remote Sensing
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Advances in Terrestrial Remote Sensing of Arctic Environments
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Advances in Terrestrial Remote Sensing of Arctic Environments

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 9098

Special Issue Editors

Prof. Dr. Paul Treitz

E-Mail Website
Guest Editor
Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: biogeophysical remote sensing of Arctic environments; vegetation classification; permafrost degradation and assessment; net ecosystem exchange in high-latitude environments
Dr. Heather Reese

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Gothenburg, Guldhedsgatan 5A, 41320 Göteborg, Sweden
Interests: remote sensing of subarctic alpine vegetation types and changes; optical remote sensing; LiDAR; digital photogrammetry; UAVs

Special Issue Information

Dear Colleagues,

The Arctic is experiencing warming at a rate two to three times that of the global average. As a result, there is strong evidence of environmental change occurring in the High Arctic that can be attributed to this warming. Such changes might occur, for example, in vegetation composition, productivity, nutrient cycling, and ecosystem functioning. The Arctic is also extremely remote and inaccessible. Hence, remote sensing provides a very effective method of examining Arctic environments across scales and for large areas. Advances in remote sensing, such as multi-resolution and multi-source data from UAV to LiDAR to satellite, machine/deep-learning algorithms, and extended time series data are providing new approaches to studying the Arctic environment. This Special Issue is intended to provide a forum for researchers (i) conducting remote sensing to assess Arctic vegetation at local, landscape, and regional scales; (ii) analyzing biogeophysical processes impacting vegetation greening and browning (productivity, permafrost degradation, snow cover and properties, moisture regime, etc.); and (iii) modelling vegetation dynamics under various climate change scenarios. Our goal is to bring together research from around the globe where remote sensing data are applied to examine biogeophysical processes for Arctic vegetation types, ranging from the baseline mapping of vegetation types to modelling the impacts of warming temperatures at high latitudes.

Prof. Dr. Paul Treitz
Dr. Heather Reese
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 submissions that pass pre-check are 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 2700 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.

Keywords

  • Arctic
  • vegetation mapping
  • image classification
  • permafrost degradation
  • snow cover monitoring
  • greenhouse gas exchange
  • environmental change

Published Papers (3 papers)

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Research

19 pages, 4608 KiB  
Article
Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg
by Daniel Alexander Rudd, Mojtaba Karami and Rasmus Fensholt
Remote Sens. 2021, 13(18), 3559; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183559 - 07 Sep 2021
Cited by 3 | Viewed by 2575
Abstract
Mapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth observations to monitor land [...] Read more.
Mapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth observations to monitor land cover in areas that are otherwise difficult to access. With the continuous development of new satellites, it is important to optimize the existing maps for further monitoring of Arctic ecosystems. This study presents a scalable classification framework, producing novel 10 m resolution land cover maps for Kobbefjord, Disko, and Zackenberg in Greenland. Based on Sentinel-2, a digital elevation model, and Google Earth Engine (GEE), this framework classifies the areas into nine classes. A vegetation land cover classification for 2019 is achieved through a multi-temporal analysis based on 41 layers comprising phenology, spectral indices, and topographical features. Reference data (1164 field observations) were used to train a random forest classifier, achieving a cross-validation accuracy of 91.8%. The red-edge bands of Sentinel-2 data proved to be particularly well suited for mapping the fen vegetation class. The study presents land cover mapping in the three study areas with an unprecedented spatial resolution and can be extended via GEE for further ecological monitoring in Greenland. Full article
(This article belongs to the Special Issue Advances in Terrestrial Remote Sensing of Arctic Environments)
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21 pages, 4786 KiB  
Article
Seasonal Surface Subsidence and Frost Heave Detected by C-Band DInSAR in a High Arctic Environment, Cape Bounty, Melville Island, Nunavut, Canada
by Greg Robson, Paul Treitz, Scott F. Lamoureux, Kevin Murnaghan and Brian Brisco
Remote Sens. 2021, 13(13), 2505; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132505 - 26 Jun 2021
Cited by 4 | Viewed by 2264
Abstract
Differential interferometry of synthetic aperture radar (DInSAR) can be used to generate high-precision surface displacement maps in continuous permafrost environments, capturing isotropic surface subsidence and uplift associated with the seasonal freeze and thaw cycle. We generated seasonal displacement maps using DInSAR with ultrafine-beam [...] Read more.
Differential interferometry of synthetic aperture radar (DInSAR) can be used to generate high-precision surface displacement maps in continuous permafrost environments, capturing isotropic surface subsidence and uplift associated with the seasonal freeze and thaw cycle. We generated seasonal displacement maps using DInSAR with ultrafine-beam Radarsat-2 data for the summers of 2013, 2015, and 2019 at Cape Bounty, Melville Island, and examined them in combination with a land-cover classification, meteorological data, topographic data, optical satellite imagery, and in situ measures of soil moisture, soil temperature, and depth to the frost table. Over the three years studied, displacement magnitudes (estimated uncertainty ± 1 cm) of up to 10 cm per 48-day DInSAR stack were detected. However, generally, the displacement was far smaller (up to 4 cm). Surface displacement was found to be most extensive and of the greatest magnitude in low-lying, wet, and steeply sloping areas. The few areas where large vertical displacements (>2.5 cm) were detected in multiple years were clustered in wet, low lying areas, on steep slopes or ridges, or close to the coast. DInSAR also captured the expansion of two medium-sized retrogressive thaw slumps (RTS), exhibiting widespread negative surface change in the slump floor. Full article
(This article belongs to the Special Issue Advances in Terrestrial Remote Sensing of Arctic Environments)
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22 pages, 4494 KiB  
Article
Rapid Ecosystem Change at the Southern Limit of the Canadian Arctic, Torngat Mountains National Park
by Emma L. Davis, Andrew J. Trant, Robert G. Way, Luise Hermanutz and Darroch Whitaker
Remote Sens. 2021, 13(11), 2085; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112085 - 26 May 2021
Cited by 13 | Viewed by 3363
Abstract
Northern protected areas guard against habitat and species loss but are themselves highly vulnerable to environmental change due to their fixed spatial boundaries. In the low Arctic, Torngat Mountains National Park (TMNP) of Canada, widespread greening has recently occurred alongside warming temperatures and [...] Read more.
Northern protected areas guard against habitat and species loss but are themselves highly vulnerable to environmental change due to their fixed spatial boundaries. In the low Arctic, Torngat Mountains National Park (TMNP) of Canada, widespread greening has recently occurred alongside warming temperatures and regional declines in caribou. Little is known, however, about how biophysical controls mediate plant responses to climate warming, and available observational data are limited in temporal and spatial scope. In this study, we investigated the drivers of land cover change for the 9700 km2 extent of the park using satellite remote sensing and geostatistical modelling. Random forest classification was used to hindcast and simulate land cover change for four different land cover types from 1985 to 2019 with topographic and surface reflectance imagery (Landsat archive). The resulting land cover maps, in addition to topographic and biotic variables, were then used to predict where future shrub expansion is likely to occur using a binomial regression framework. Land cover hindcasts showed a 235% increase in shrub and a 105% increase in wet vegetation cover from 1985/89 to 2015/19. Shrub cover was highly persistent and displaced wet vegetation in southern, low-elevation areas, whereas wet vegetation expanded to formerly dry, mid-elevations. The predictive model identified both biotic (initial cover class, number of surrounding shrub neighbors), and topographic variables (elevation, latitude, and distance to the coast) as strong predictors of future shrub expansion. A further 51% increase in shrub cover is expected by 2039/43 relative to 2014 reference data. Establishing long-term monitoring plots within TMNP in areas where rapid vegetation change is predicted to occur will help to validate remote sensing observations and will improve our understanding of the consequences of change for biotic and abiotic components of the tundra ecosystem, including important cultural keystone species. Full article
(This article belongs to the Special Issue Advances in Terrestrial Remote Sensing of Arctic Environments)
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