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Special Issue "Remote Sensing of Dynamic Permafrost Regions"

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: 1 March 2018

Special Issue Editors

Guest Editor
Dr. Benjamin M. Jones

U.S. Geological Survey, Alaska Science Center, Anchorage, Alaska 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
Guest Editor
Dr. Annett Bartsch

Austrian Polar Research Institute, Vice-Director, c/o Universität Wien, Universitätsstraße 7, 1010 Vienna, Austria
Managing Director, b.geos GmbH, Industriestrasse 1, 2100 Korneuburg, Austria
Website | E-Mail
Interests: microwave remote sensing; landsurface hydrology; frozen ground; snow; land cover
Guest Editor
Prof. Dr. Guido Grosse

Head of Periglacial Research Unit, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Telegrafenberg A43, 14473 Potsdam, Germany
Website | E-Mail
Interests: Arctic terrestrial landscape dynamics; remote sensing of permafrost regions; permafrost thaw; permafrost geomorphology and hydrology

Special Issue Information

Dear Colleagues,

The implications of widespread permafrost degradation are immense and include impacts to infrastructure, ecosystems, hydrology, and carbon cycling. More accurate measures of permafrost distribution, characteristics, and dynamics are needed for understanding past, present, and future permafrost region responses to climate and human disturbance. The development and application of remote sensing in permafrost regions is of importance for inventorying and observing the state and change of this essential component of the cryosphere.

We are pleased to announce a Special Issue in the journal Remote Sensing on “Remote Sensing of Dynamic Permafrost Regions”. We solicit manuscripts that use the multitude of remote sensing platforms and sensors available for describing permafrost region characteristics and dynamics. We welcome submissions that focus on multiple spatial and temporal scales as well as the integration of permafrost region field studies with remotely sensed data. We are particularly interested in submissions that deal with ice-rich permafrost landscapes and quantification of thermokarst and thaw-related landscape dynamics. Contributions that demonstrate the development of new techniques, data products, and/or highlight the challenges of remote sensing in permafrost regions are also encouraged.

Please don’t hesitate to contact us in regards to your potential submission to our special issue focused on “Remote Sensing of Dynamic Permafrost Regions”.

Dr. Benjamin M. Jones
Prof. Dr. Guido Grosse
Dr. Annett Bartsch
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 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 1800 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

  • Permafrost
  • Remote Sensing
  • Thermokarst
  • Permafrost Degradation
  • Ground Ice
  • Frozen Ground
  • Thaw Subsidence

Published Papers (3 papers)

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Research

Open AccessArticle Analysis of Permafrost Region Coherence Variation in the Qinghai–Tibet Plateau with a High-Resolution TerraSAR-X Image
Remote Sens. 2018, 10(2), 298; doi:10.3390/rs10020298 (registering DOI)
Received: 1 November 2017 / Revised: 2 February 2018 / Accepted: 13 February 2018 / Published: 15 February 2018
PDF Full-text (1558 KB)
Abstract
The Qinghai–Tibet Plateau (QTP) is heavily affected by climate change and has been undergoing serious permafrost degradation due to global warming. Synthetic aperture radar interferometry (InSAR) has been a significant tool for mapping surface features or measuring physical parameters, such as soil moisture,
[...] Read more.
The Qinghai–Tibet Plateau (QTP) is heavily affected by climate change and has been undergoing serious permafrost degradation due to global warming. Synthetic aperture radar interferometry (InSAR) has been a significant tool for mapping surface features or measuring physical parameters, such as soil moisture, active layer thickness, that can be used for permafrost modelling. This study analyzed variations of coherence in the QTP area for the first time with high-resolution SAR images acquired from June 2014 to August 2016. The coherence variation of typical ground targets was obtained and analyzed. Because of the effects of active-layer (AL) freezing and thawing, coherence maps generated in the Beiluhe permafrost area exhibits seasonal variation. Furthermore, a temporal decorrelation model determined by a linear temporal-decorrelation component plus a seasonal periodic-decorrelation component and a constant component have been proposed. Most of the typical ground targets fit this temporal model. The results clearly indicate that railways and highways can hold high coherence properties over the long term in X-band images. By contrast, mountain slopes and barren areas cannot hold high coherence after one cycle of freezing and thawing. The possible factors (vegetation, soil moisture, soil freezing and thawing, and human activity) affecting InSAR coherence are discussed. This study shows that high-resolution time series of TerraSAR-X coherence can be useful for understanding QTP environments and for other applications. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
Open AccessArticle Permafrost Distribution along the Qinghai-Tibet Engineering Corridor, China Using High-Resolution Statistical Mapping and Modeling Integrated with Remote Sensing and GIS
Remote Sens. 2018, 10(2), 215; doi:10.3390/rs10020215
Received: 18 October 2017 / Revised: 5 December 2017 / Accepted: 30 January 2018 / Published: 1 February 2018
PDF Full-text (6621 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Permafrost distribution in the Qinghai-Tibet Engineering Corridor (QTEC) is of growing interest due to the increase in infrastructure development in this remote area. Empirical models of mountain permafrost distribution have been established based on field sampled data, as a tool for regional-scale assessments
[...] Read more.
Permafrost distribution in the Qinghai-Tibet Engineering Corridor (QTEC) is of growing interest due to the increase in infrastructure development in this remote area. Empirical models of mountain permafrost distribution have been established based on field sampled data, as a tool for regional-scale assessments of its distribution. This kind of model approach has never been applied for a large portion of this engineering corridor. In the present study, this methodology is applied to map permafrost distribution throughout the QTEC. After spatial modelling of the mean annual air temperature distribution from MODIS-LST and DEM, using high-resolution satellite image to interpret land surface type, a permafrost probability index was obtained. The evaluation results indicate that the model has an acceptable performance. Conditions highly favorable to permafrost presence (≥70%) are predicted for 60.3% of the study area, declaring a discontinuous permafrost distribution in the QTEC. This map is useful for the infrastructure development along the QTEC. In the future, local ground-truth observations will be required to confirm permafrost presence in favorable areas and to monitor permafrost evolution under the influence of climate change. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
Figures

Open AccessArticle Vegetation Changes along the Qinghai-Tibet Plateau Engineering Corridor Since 2000 Induced by Climate Change and Human Activities
Remote Sens. 2018, 10(1), 95; doi:10.3390/rs10010095
Received: 21 September 2017 / Revised: 28 December 2017 / Accepted: 10 January 2018 / Published: 12 January 2018
PDF Full-text (14807 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Qinghai-Tibet (QT) Plateau Engineering Corridor is located in the hinterland of the QT Plateau, which is highly sensitive to global climate change. Climate change causes permafrost degradation, which subsequently affects vegetation growth. This study focused on the vegetation dynamics and their relationships
[...] Read more.
The Qinghai-Tibet (QT) Plateau Engineering Corridor is located in the hinterland of the QT Plateau, which is highly sensitive to global climate change. Climate change causes permafrost degradation, which subsequently affects vegetation growth. This study focused on the vegetation dynamics and their relationships with climate change and human activities in the region surrounding the QT Plateau Engineering Corridor. The vegetation changes were inferred by applying trend analysis, the Mann-Kendall trend test and abrupt change analysis. Six key regions, each containing 40 nested quadrats that ranged in size from 500 × 500 m to 20 × 20 km, were selected to determine the spatial scales of the impacts from different factors. Cumulative growing season integrated enhanced vegetation index (CGSIEVI) values were calculated for each of the nested quadrats of different sizes to indicate the overall vegetation state over the entire year at different spatial scales. The impacts from human activities, a sudden increase in precipitation and permafrost degradation were quantified at different spatial scales using the CGSIEVI values and meteorological data based on the double mass curve method. Three conclusions were derived. First, the vegetation displayed a significant increasing trend over 23.6% of the study area. The areas displaying increases were mainly distributed in the Hoh Xil. Of the area where the vegetation displayed a significant decreasing trend, 72.4% was made up of alpine meadows. Second, more vegetation, especially the alpine meadows, has begun to degenerate or experience more rapid degradation since 2007 due to permafrost degradation and overgrazing. Finally, an active layer depth of 3 m to 3.2 m represents a limiting depth for alpine meadows. Full article
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
Figures

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