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The Applications of Remote Sensing, Machine Learning and Deep Learning in Frozen Ground 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: 15 February 2025 | Viewed by 5125

Special Issue Editors

Prof. Dr. Xiaoqing Peng

E-Mail Website
Guest Editor
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China
Interests: permafrost; interactions in snow–vegetation–frozen ground
Prof. Dr. Dongliang Luo

E-Mail Website
Guest Editor
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Beijing, China
Interests: permafrost; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Raul-David Șerban

E-Mail Website
Guest Editor
Institute for Alpine Environment, Eurac Research, 39100 Bozen-Bolzano, Italy
Interests: permafrost; periglacial geomorphology; landscape change detection; modeling ground temperature
Dr. Lingcao Huang

E-Mail Website
Guest Editor
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong SAR, China
Interests: remote sensing; permafrost; deep learning for computer vision
Prof. Dr. Yonghong Yi

E-Mail Website
Guest Editor
College of Surveying and Geo-Informatics, Tongji University, Shanghai, China
Interests: remote sensing; permafrost; hydrology and carbon cycle

Special Issue Information

Dear Colleagues,

Frozen ground is an important component of the cryosphere. Permafrost regions underlie approximately 24% of the exposed land surface of the Northern Hemisphere, and seasonally frozen ground (SFG) regions occupy 57%. Such a vast area extent of frozen ground plays a significant role in the local to global atmospheric circulation, climate, hydrology, and terrestrial ecosystems by affecting the energy, water, and carbon cycles. Due to significant global warming, frozen ground and its environment have experienced great changes, e.g., the freeze–thaw process, the area extent, ground temperature, landform, vegetation, and others. Thus, it is necessary to study this topic. Excluding classical field observations, remote sensing, machine learning, and deep learning methods are popular in the field of frozen ground research.

This Special Issue is aimed at studies covering different applications of remote sensing, machine learning, and deep learning in the frozen ground, including seasonally frozen ground and permafrost. Topics may cover anything from the related frozen ground in the point-regional-hemisphere scales. Hence, algorithms, applications, and simulations in frozen ground studies, among other issues, are welcome. Articles may address, but are not limited to, the following topics:

  • Remote sensing in the freeze/thaw status;
  • Seasonally frozen ground changes;
  • Permafrost changes;
  • Landform;
  • The application or development of algorithms in the frozen ground study;
  • Environment changes in the frozen ground regions.

Prof. Dr. Xiaoqing Peng
Prof. Dr. Dongliang Luo
Dr. Raul-David Șerban
Dr. Lingcao Huang
Prof. Dr. Yonghong Yi
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

  • frozen ground
  • permafrost
  • remote sensing
  • machining learning
  • deep learning
  • InSAR
  • carbon

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

19 pages, 5126 KiB  
Article
Simulation and Prediction of Thermokarst Lake Surface Temperature Changes on the Qinghai–Tibet Plateau
by Chengming Zhang, Zeyong Gao, Jing Luo, Wenyan Liu, Mengjia Chen, Fujun Niu, Yibo Wang and Yunhu Shang
Remote Sens. 2024, 16(24), 4645; https://doi.org/10.3390/rs16244645 - 11 Dec 2024
Viewed by 722
Abstract
Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We [...] Read more.
Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We aimed to analyze seasonal variations in the surface water temperature, clarify historical trends in the phenological characteristics of lake ice, and predict future temperature changes in surface water of the thermokarst lakes using the air2water model. The results indicated that in comparison with air temperature, the thermokarst lake’s surface water temperature showed a certain lag and significantly higher values in the warm season. The warming rate of the thermokarst lake’s average surface water temperature based on historical data from 1957 to 2022 was 0.21 °C per decade, with a notably higher rate in August (0.42 °C per decade) than in other months. Furthermore, the ice-covered period steadily decreased by 2.12 d per decade. Based on the Coupled Model Intercomparison Project 6 projections, by 2100, the surface water temperatures of thermokarst lakes during the warm season are projected to increase by 0.38, 0.46, and 2.82 °C (under scenarios SSP126, SSP245, and SSP585), respectively. Compared with typical tectonic lakes on the Qinghai–Tibet Plateau, thermokarst lakes have higher average surface water temperatures during ice-free periods, and they exhibit a higher warming rate (0.21 °C per decade). These results elucidate the response mechanisms of thermokarst lakes’ surface water temperature and the phenological characteristics of lake ice in response to climate change. Full article
(This article belongs to the Special Issue The Applications of Remote Sensing, Machine Learning and Deep Learning in Frozen Ground Regions)
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19 pages, 16533 KiB  
Article
Observed Retrogressive Thaw Slump Evolution in the Qilian Mountains
by Xingyun Liu, Xiaoqing Peng, Yongyan Zhang, Oliver W. Frauenfeld, Gang Wei, Guanqun Chen, Yuan Huang, Cuicui Mu and Jun Du
Remote Sens. 2024, 16(13), 2490; https://doi.org/10.3390/rs16132490 - 7 Jul 2024
Cited by 2 | Viewed by 1569
Abstract
Climate warming can lead to permafrost degradation, potentially resulting in slope failures such as retrogressive thaw slumps (RTSs). The formation of and changes in RTSs could exacerbate the degradation of permafrost and the environment in general. The mechanisms of RTS progression and the [...] Read more.
Climate warming can lead to permafrost degradation, potentially resulting in slope failures such as retrogressive thaw slumps (RTSs). The formation of and changes in RTSs could exacerbate the degradation of permafrost and the environment in general. The mechanisms of RTS progression and the potential consequences on the analogous freeze–thaw cycle are not well understood, owing partly to necessitating field work under harsh conditions and with high costs. Here, we used multi-source remote sensing and field surveys to quantify the changes in an RTS on Eboling Mountain in the Qilian Mountain Range in west-central China. Based on optical remote sensing and SBAS-InSAR measurements, we analyzed the RTS evolution and the underlying drivers, combined with meteorological observations. The RTS expanded from 56 m2 in 2015 to 4294 m2 in 2022, growing at a rate of 1300 m2/a to its maximum in 2018 and then decreasing. Changes in temperature and precipitation play a dominant role in the evolution of the RTS, and the extreme weather in 2016 may also be a primary contributor to the accelerated growth, with an average deformation of −8.3 mm during the thawing period, which decreased slope stability. The RTS evolved more actively during the thawing and early freezing process, with earthquakes having potentially contributed further to RTS evolution. We anticipate that the rate of RTS evolution is likely to increase in the coming years. Full article
(This article belongs to the Special Issue The Applications of Remote Sensing, Machine Learning and Deep Learning in Frozen Ground Regions)
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22 pages, 8260 KiB  
Article
Spatiotemporal Distribution Characteristics and Influencing Factors of Freeze–Thaw Erosion in the Qinghai–Tibet Plateau
by Zhenzhen Yang, Wankui Ni, Fujun Niu, Lan Li and Siyuan Ren
Remote Sens. 2024, 16(9), 1629; https://doi.org/10.3390/rs16091629 - 2 May 2024
Cited by 4 | Viewed by 1319
Abstract
Freeze–thaw (FT) erosion intensity may exhibit a future increasing trend with climate warming, humidification, and permafrost degradation in the Qinghai–Tibet Plateau (QTP). The present study provides a reference for the prevention and control of FT erosion in the QTP, as well as for [...] Read more.
Freeze–thaw (FT) erosion intensity may exhibit a future increasing trend with climate warming, humidification, and permafrost degradation in the Qinghai–Tibet Plateau (QTP). The present study provides a reference for the prevention and control of FT erosion in the QTP, as well as for the protection and restoration of the regional ecological environment. FT erosion is the third major type of soil erosion after water and wind erosion. Although FT erosion is one of the major soil erosion types in cold regions, it has been studied relatively little in the past because of the complexity of several influencing factors and the involvement of shallow surface layers at certain depths. The QTP is an important ecological barrier area in China. However, this area is characterized by harsh climatic and fragile environmental conditions, as well as by frequent FT erosion events, making it necessary to conduct research on FT erosion. In this paper, a total of 11 meteorological, vegetation, topographic, geomorphological, and geological factors were selected and assigned analytic hierarchy process (AHP)-based weights to evaluate the FT erosion intensity in the QTP using a comprehensive evaluation index method. In addition, the single effects of the selected influencing factors on the FT erosion intensity were further evaluated in this study. According to the obtained results, the total FT erosion area covered 1.61 × 106 km2, accounting for 61.33% of the total area of the QTP. The moderate and strong FT erosion intensity classes covered 6.19 × 105 km2, accounting for 38.37% of the total FT erosion area in the QTP. The results revealed substantial variations in the spatial distribution of the FT erosion intensity in the QTP. Indeed, the moderate and strong erosion areas were mainly located in the high mountain areas and the hilly part of the Hoh Xil frozen soil region. Full article
(This article belongs to the Special Issue The Applications of Remote Sensing, Machine Learning and Deep Learning in Frozen Ground Regions)
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