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Remote Sensing of Dynamic Permafrost Regions Ⅱ

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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 (31 May 2023) | Viewed by 10937

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Special Issue Editors

Dr. Olga Makarieva

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Guest Editor

Institute of Earth Science, St. Petersburg State University, St. Petersburg 199034, Russia
Interests: permafrost hydrology; dangerous hydrological phenomena; mathematical modelling; climate and landscape changes impact on hydrological regime; groundwater; mountainous hydrology; forecast; aufeis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dongliang Luo

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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

Special Issue Information

Dear Colleagues,

Permafrost plays a key role in global natural and climate systems; at the same time, it remains the least explored variable in the sense of ground monitoring and research. Degradation of permafrost due to climate change and anthropogenic impact poses a big threat to the safe and sustainable development of society and requires a better comprehension of permafrost and other related cryospheric phenomenon distribution and characteristics, as well as their reaction to past, present, and future transforming factors. Remote sensing techniques and data allow observing permafrost development on different scales worldwide.

We are pleased to announce a Special Issue in the journal Remote Sensing on “Remote Sensing of Dynamic Permafrost Regions”. We invite manuscripts that implement research in permafrost regions related to cryosphere, hydrology and matter fluxes, landscapes, etc. based on the analysis of remote sensing data of different origins, including unmanned aerial vehicles (UAV). Studies using ground monitoring field data to verify remote sensing findings are highly encouraged. We also are interested in the applications of remote sensing data in practical tasks of assessing permafrost-related hazards and the estimation of geocryological risks to infrastructure and other human activities.

Please do not hesitate to contact us in regard to your potential submission to our Special Issue focused on “Remote Sensing of Dynamic Permafrost Regions”.

Dr. Olga Makarieva
Dr. Dongliang Luo
Guest Editors

Manuscript Submission Information

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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

permafrost ecology
remote sensing
thermokarst
periglacial geomorphology
permafrost degradation
ground ice
thaw subsidence

Related Special Issues

Remote Sensing of Dynamic Permafrost Regions Ⅱ in Remote Sensing (6 articles)
Remote Sensing of Dynamic Permafrost Regions Ⅱ in Remote Sensing (6 articles)

Published Papers (6 papers)

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Research

22 pages, 12505 KiB

Open AccessArticle

Satellite-Based Mapping of Gold-Mining-Related Land-Cover Changes in the Magadan Region, Northeast Russia

by Andrey Shikhov, Polina Ilyushina, Olga Makarieva, Anastasiia Zemlianskova and Maria Mozgina

Remote Sens. 2023, 15(14), 3564; https://doi.org/10.3390/rs15143564 - 16 Jul 2023

Cited by 4 | Viewed by 2987

Abstract

Gold mining generates major environmental impacts like landscape degradation, accumulation of waste rock dumps, and water contamination by suspended solids. Russia ranks third in the world in gold production, but the impact of gold mining has not been previously estimated for its vast northeastern part. This study provides a detailed overview of land-cover changes associated with gold mining in the Magadan region (northeast Russia) in the 21st century, where alluvial gold production has increased by a third in the last 20 years. A long-term series of Landsat and Sentinel-2 images obtained in July and August are used to compile two datasets of mining-impacted areas with totally removed vegetation for 2000–2002 and 2022. We calculated the NDVI difference and then discriminated mining-related vegetation losses from other bare areas, using additional data like the classification of landforms based on the digital surface model and the data on mining allotments. The total area of gold-mining sites was estimated as 41,206 ha in 2000–2002 and 72,602 ha in 2022, with an increase of 26,031 ha over the past 4–6 years. Moreover, this is a lower-boundary estimate, without taking into account man-made reservoirs and historical mines recovered by vegetation. The spatial distribution of mining sites has not changed significantly over the past two decades and has a maximum in the western part of the region. We found that the floodplains of the Berelekh and Debin Rivers (large tributaries of the Kolyma River) are most heavily impacted by gold mining with a removed vegetation canopy occupying 16.0% and 11.2% of their area. Along with the land degradation assessment, we found that 19,900 ha of historical gold-mining sites in the Berelekh River basin are recovered by vegetation, which is comparable in size to the areas impacted by mining over the past 20 years. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions Ⅱ)

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20 pages, 25064 KiB

Open AccessArticle

Thermokarst Lake Susceptibility Assessment Induced by Permafrost Degradation in the Qinghai–Tibet Plateau Using Machine Learning Methods

by Rui Wang, Lanlan Guo, Yuting Yang, Hao Zheng, Lianyou Liu, Hong Jia, Baijian Diao and Jifu Liu

Remote Sens. 2023, 15(13), 3331; https://doi.org/10.3390/rs15133331 - 29 Jun 2023

Viewed by 1136

Abstract

The rapidly warming climate on the Qinghai–Tibet Plateau (QTP) leads to permafrost degradation, and the thawing of ice-rich permafrost induces land subsidence to facilitate the development of thermokarst lakes. Thermokarst lakes exacerbate the instability of permafrost, which significantly alters regional geomorphology and hydrology, affecting biogeochemical cycles. However, the spatial distribution and future changes in thermokarst lakes have rarely been assessed at large scales. In this study, we combined various conditioning factors and an inventory of thermokarst lakes to assess the spatial distribution of susceptibility maps using machine-learning algorithms. The results showed that the extremely randomized trees (EXT) performed the best in the susceptibility modeling process, followed by random forest (RF) and logistic regression (LR). According to the assessment based on EXT, the high- and very high-susceptibility area of the present (2000–2016) susceptibility map was 196,222 km², covering 19.67% of the permafrost region of the QTP. In the future (the 2070s), the area of the susceptibility map was predicted to shrink significantly under various representative concentration pathway scenarios (RCPs). The susceptibility map area would be reduced to 37.06% of the present area in RCP 8.5. This paper also performed correlation and importance analysis on the conditioning factors and thermokarst lakes, which indicated that thermokarst lakes tended to form in areas with flat topography and high soil moisture. The uncertainty of the susceptibility map was further assessed by the coefficient of variation (CV). Our results demonstrate a way to study the spatial distribution of thermokarst lakes at the QTP scale and provide a scientific basis for understanding thermokarst processes in response to climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions Ⅱ)

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19 pages, 5452 KiB

Open AccessArticle

Ground Deformation and Permafrost Degradation in the Source Region of the Yellow River, in the Northeast of the Qinghai-Tibet Plateau

by Chengye Li, Lin Zhao, Lingxiao Wang, Shibo Liu, Huayun Zhou, Zhibin Li, Guangyue Liu, Erji Du, Defu Zou and Yingxu Hou

Remote Sens. 2023, 15(12), 3153; https://doi.org/10.3390/rs15123153 - 16 Jun 2023

Cited by 2 | Viewed by 1076

Abstract

The source region of the Yellow River (SRYR) is situated on the permafrost boundary in the northeast of the Qinghai-Tibet Plateau (QTP), which is an area highly sensitive to climate change. As a result of increasing global temperatures, the permafrost in this region has undergone significant degradation. In this study, we utilized Sentinel-1 to obtain ground surface deformation data in the SRYR from June 2017 to January 2022. We then analyzed the differences in terrain deformation under various environmental conditions. Our findings indicated an overall subsidence trend in the SRYR, with a long-term deformation velocity of −4.2 mm/a and seasonal deformation of 8.85 mm. Furthermore, the results showed that terrain deformation varied considerably from region to region, and that the Huanghe’ yan sub-basin with the highest permafrost coverage among all sub-basins significantly higher subsidence rates than other regions. Topography strongly influenced ground surface deformation, with flat slopes exhibiting much higher subsidence rates and seasonal deformation. Moreover, the ground temperature and ground ice richness played a certain role in the deformation pattern. This study also analyzed regional deformation details from eight boreholes and one profile line covering different surface conditions, revealing the potential for refining the permafrost boundary. Overall, the results of this study provide valuable insights into the evolution of permafrost in the SRYR region. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions Ⅱ)

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16 pages, 2656 KiB

Open AccessArticle

Impacts of National Highway G214 on Vegetation in the Source Area of Yellow and Yangtze Rivers on the Southern Qinghai Plateau, West China

by Xiaoying Jin, Jianjun Tang, Dongliang Luo, Qingfeng Wang, Ruixia He, Raul-D. Serban, Yan Li, Mihaela Serban, Xinze Li, Hongwei Wang, Xiaoying Li, Wenhui Wang, Qingbai Wu and Huijun Jin

Remote Sens. 2023, 15(6), 1547; https://doi.org/10.3390/rs15061547 - 12 Mar 2023

Cited by 1 | Viewed by 1768

Abstract

Engineering corridors on the Qinghai–Tibet Plateau have substantially modified the regional ecosystem functions and environment, resulting in changes in the alpine ecosystem. In addition, the building and operation of these engineering corridors have led to rapid permafrost degradation, which in turn has impacted local vegetation along these corridors. This study investigated vegetation changes and their driving factors by the methods of coefficient of variation, correlation analysis, and GeoDetector in a 30 km wide buffer zone at each side along the National Highway G214 (G214) at the northern and southern flanks of the Bayan Har Mountains in part of the source area of the Yellow and Yangtze rivers on the southern Qinghai Plateau, West China. The following results were obtained: (1) The Normalized Difference Vegetation Index in Growing Season (NDVI_gs) rose slightly in 2010–2019, with an average annual change rate of 0.006/a. Patterns of NDVI_gs along the G214 exhibited “low at the northern flank and high at the southern flank of the Bayan Har Mountains”. (2) Spatially, average NDVI_gs increased from the first buffer zone at the distance of 0–10 km from the highway centerline to the second buffer zone at 20–30 km perpendicularly away from the G214. Furthermore, the first buffer zone had the lowest coefficient of variation, possibly due to a low vegetation recovery as a result of the greatest influence of the G214 on NDVI_gs at 0–10 km. (3) Furthermore, annual precipitation (AP) was the dominant factor for significantly (p < 0.01) and positively influencing the variations in NDVI_gs (R = 0.75, p < 0.01). Additionally, NDVI_gs was more strongly influenced by the two combined factors than any single one, with the highest q-value (0.74) for the interactive influences of AP and annual average air temperature (AAAT) and followed by that of the AP and mean annual ground temperature (MAGT) at the depth of zero annual amplitude (15 m). Evidently, the construction and operation of the G214 have directly and indirectly affected vegetation through changing environmental variables, with significant impacts on NDVI_gs extended at least 20 km outwards from the highway. This study helps better understand the environmental impacts along the engineering corridors in elevational permafrost regions at mid and low latitudes and their management. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions Ⅱ)

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16 pages, 6731 KiB

Open AccessArticle

Leveraging Soil Moisture Assimilation in Permafrost Affected Regions

by Ankita Pradhan, Akhilesh S. Nair, J. Indu, Olga Makarieva and Nataliia Nesterova

Remote Sens. 2023, 15(6), 1532; https://doi.org/10.3390/rs15061532 - 10 Mar 2023

Viewed by 1400

Abstract

The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions Ⅱ)

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20 pages, 53339 KiB

Open AccessArticle

Monitoring Ground Surface Deformation of Ice-Wedge Polygon Areas in Saskylakh, NW Yakutia, Using Interferometric Synthetic Aperture Radar (InSAR) and Google Earth Engine (GEE)

by Wenhui Wang, Huijun Jin, Ze Zhang, Mikhail N. Zhelezniak, Valentin V. Spektor, Raul-David Șerban, Anyuan Li, Vladimir Tumskoy, Xiaoying Jin, Suiqiao Yang, Shengrong Zhang, Xiaoying Li, Mihaela Șerban, Qingbai Wu and Yanan Wen

Remote Sens. 2023, 15(5), 1335; https://doi.org/10.3390/rs15051335 - 27 Feb 2023

Cited by 3 | Viewed by 1962

Abstract

As one of the best indicators of the periglacial environment, ice-wedge polygons (IWPs) are important for arctic landscapes, hydrology, engineering, and ecosystems. Thus, a better understanding of the spatiotemporal dynamics and evolution of IWPs is key to evaluating the hydrothermal state and carbon budgets of the arctic permafrost environment. In this paper, the dynamics of ground surface deformation (GSD) in IWP zones (2018–2019) and their influencing factors over the last 20 years in Saskylakh, northwestern Yakutia, Russia were investigated using the Interferometric Synthetic Aperture Radar (InSAR) and Google Earth Engine (GEE). The results show an annual ground surface deformation rate (AGSDR) in Saskylakh at −49.73 to 45.97 mm/a during the period from 1 June 2018 to 3 May 2019. All the selected GSD regions indicate that the relationship between GSD and land surface temperature (LST) is positive (upheaving) for regions with larger AGSDR, and negative (subsidence) for regions with lower AGSDR. The most drastic deformation was observed at the Aeroport regions with GSDs rates of −37.06 mm/a at tower and 35.45 mm/a at runway. The GSDs are negatively correlated with the LST of most low-centered polygons (LCPs) and high-centered polygons (HCPs). Specifically, the higher the vegetation cover, the higher the LST and the thicker the active layer. An evident permafrost degradation has been observed in Saskylakh as reflected in higher ground temperatures, lusher vegetation, greater active layer thickness, and fluctuant numbers and areal extents of thermokarst lakes and ponds. Full article

(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions Ⅱ)

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