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Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover

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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 (30 November 2023) | Viewed by 22244

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Special Issue Editors

Prof. Dr. Fujun Niu

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Guest Editor

Chinese Academy of Sciences, Beijing, China
Interests: cold region engineering; cold region environment and geo-hazards

Dr. Shiwei Liu

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Guest Editor

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
Interests: cryo-hydrology; cryosphere service; coupled hydrology and crop growth model

Prof. Dr. Yubao Qiu

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Guest Editor

1. International Research Center of Big Data for Sustainable Development, Beijing 100094, China
2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing of snow and ice; mocrowave remote sensing; global change
Special Issues, Collections and Topics in MDPI journals

Dr. Ian Brown

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Guest Editor

Department of Physical Geography, Stockholm University, Stockholm, Sweden
Interests: the use of synthetic aperture radar to retrieve the properties of snowpacks and glaciers and of boreal and mangrove forests

Dr. Xiaoming Wang

E-Mail Website
Guest Editor

Chinese Academy of Sciencesdisabled, Beijing, China
Interests: cryosphere change; adaptation and sustainable development

Special Issue Information

Dear Colleagues,

There is a global population of about 1.3 billion people living in mountainous regions, in an area amounting to more than 31 million square kilometres. Fresh water is essential to societal and economic development in mountainous regions. In high mountains, fresh water is mostly sourced from the mountain cryosphere, providing meltwater largely from glaciers, snow cover and permafrost. The meltwater also plays a crucial role in supplying water to 2 billion people downstream, as well as vast irrigation for agriculture linked to global supply chain and economy. Moreover, meltwater is vital in maintaining healthy mountain biodiversity and ecosystems (and subsequently broad mountain ecosystem services). This is in addition to the critical role that the cryosphere plays in climate systems.

Climate change is impacting the mountain cryosphere, causing loss of glacier mass, reduction in snow cover extent, and increase in permafrost thaw. More frequent and increasingly severe cryosphere hazards are also observed. The implications of these changes are significant, and thus considerable efforts must be made to understand the changes to the mountain cryosphere, including glaciers, permafrost and snow cover, so that timed responses and adaptations can be accordingly developed to mitigate their consequent risks.

Remote sensing is a key approach to identifying the changes to the mountain cryosphere at multiple spatiotemporal scales, and it is capable of providing dynamic details of geophysical parameters about mountain snow, glacier, precipitation, permafrost, etc. This Special Issue is intended to demonstrate the approach and discover its importance in tracking the changing mountain glaciers, permafrost and snow cover, as well as their implications. Therefore, we encourage you to submit papers on topics including but not limited to the following:

Spatiotemporal distribution and changes of mountain glaciers, permafrost, and seasonal snow cover;
Detection and attribution of mountain cryosphere change to climate change;
Cryosphere services under changing climate;
Ecosystem and services in response to cryosphere change;
Mountain cryo-hydrology under changing cryosphere;
Cryospheric hazards and disaster risks to mountain societies;
Slope instability, thermokarst processes in mountain permafrost;
Engineering and infrastructure in mountain cryospheric environment;
Interplays of cryosphere changes and anthropogenic activities such as irrigation;
Mountain cryosphere change, adaptation and sustainable development.

Prof. Dr. Fujun Niu
Dr. Shiwei Liu
Prof. Dr. Yubao Qiu
Dr. Ian Brown
Dr. Xiaoming Wang
Guest Editors

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Research

22 pages, 12365 KiB

Open AccessArticle

Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia

by Gang Deng, Zhiguang Tang, Chunyu Dong, Donghang Shao and Xin Wang

Remote Sens. 2024, 16(1), 192; https://doi.org/10.3390/rs16010192 - 2 Jan 2024

Cited by 50 | Viewed by 2082

Abstract

Accurate snow cover data are critical for understanding the Earth’s climate system, and exploring hydrological processes and regional water resource management over High Mountain Asia (HMA). However, satellite-based remote sensing observations of snow cover have inevitable data gaps originating from cloud cover, sensor, orbital limitations and other factors. Here an effective cloud-gap-filled (CGF) method was developed to fully fill the data gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) product. The CGF method combines the respective strengths of the cubic spline interpolation method and the spatio-temporal weighted method for generating the CGF Terra-Aqua MODIS NDSI product over HMA from 2000 to 2021. Based on the validation results of in situ snow-depth observations, the CGF NDSI product achieves a high range overall accuracy (OA) of 93.54–98.08%, a low range underestimation error (MU) of 0.15–3.49% and an acceptable range overestimation error (MO) of 0.84–5.77%. Based on the validation results of high-resolution Landsat images, this product achieves the OA of 88.52–92.40%, the omission error (OE) of 1.42–10.28% and the commission error (CE) of 5.97–17.58%. The CGF MODIS NDSI product can provide scientific support for eco-environment sustainable management in the high mountain region. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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23 pages, 13091 KiB

Open AccessArticle

A New Method for Bare Permafrost Extraction on the Tibetan Plateau by Integrating Machine Learning and Multi-Source Information

by Xiaoyang Li, Yuhe Ji, Guangsheng Zhou, Li Zhou, Xiaopeng Li, Xiaohui He and Zhihui Tian

Remote Sens. 2023, 15(22), 5328; https://doi.org/10.3390/rs15225328 - 12 Nov 2023

Cited by 1 | Viewed by 1458

Abstract

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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21 pages, 6583 KiB

Open AccessArticle

Surging Glaciers in High Mountain Asia between 1986 and 2021

by Xiaojun Yao, Sugang Zhou, Meiping Sun, Hongyu Duan and Yuan Zhang

Remote Sens. 2023, 15(18), 4595; https://doi.org/10.3390/rs15184595 - 18 Sep 2023

Cited by 7 | Viewed by 1878

Abstract

High Mountain Asia (HMA) is one of the concentrated areas of surging glaciers in the world. The dynamic movement of surging glaciers not only reshapes the periglacial landscape but also has the potential to directly or indirectly trigger catastrophic events. Therefore, it is crucial to understand the distribution patterns, periodicities, and occurrence mechanisms of surging glaciers. Based on Landsat TM/ETM+/OLI remote sensing images from 1986 to 2021, a total of 244 surging glaciers were identified in HMA in this study, covering an area of 11,724 km² and accounting for 12.01% of the total area of glaciers in this region. There are 185 surging glaciers identified within the Karakoram Range and Pamirs, which constitute the primary mountainous regions in HMA. From 1986 to 2021, these surging glaciers advanced at least 2802 times and exhibited different temporal and spatial patterns. A total of 36 glaciers in HMA experienced 2 or more surges during this period, with the highest number observed in the Pamirs (19), followed by the Karakorum (13), with the other regions having fewer occurrences. Obvious differences exist in the surge phase and the quiescent phase of glaciers in different regions of HMA. The surge phase of surging glaciers in the Karakoram Range and Pamirs is generally short, mostly in the range of 2~6 years. The quiescent phase lasts for 5~19 years and the overall surge cycle ranges from 9 to 24 years. The complex nature of glacier surges in HMA suggests that multiple mechanisms may be at play, necessitating further research. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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33 pages, 25949 KiB

Open AccessArticle

Preliminary Study on InSAR-Based Uplift or Subsidence Monitoring and Stability Evaluation of Ground Surface in the Permafrost Zone of the Qinghai–Tibet Engineering Corridor, China

by Qingsong Du, Dun Chen, Guoyu Li, Yapeng Cao, Yu Zhou, Mingtang Chai, Fei Wang, Shunshun Qi, Gang Wu, Kai Gao and Chunqing Li

Remote Sens. 2023, 15(15), 3728; https://doi.org/10.3390/rs15153728 - 26 Jul 2023

Cited by 11 | Viewed by 1936

Abstract

Against the background of global warming, permafrost areas are facing increasing thawing, and the threat to the surface of the Qinghai–Tibet Engineering Corridor (QTEC) is serious. It is imperative to understand the current surface deformation and analyze the changes spatiotemporal characteristics for future warnings. At present, observation of a long time series and overall coverage of vertical ground deformation in QTEC are lacking. This paper takes the permafrost deformation of the QTEC as its research object. It uses the pretreated LiCSAR product and combines it with the LiCSBAS package to obtain monitoring results of the long time series deformation of the engineering corridor’s surface. The SAR image acquisition date is taken as the constraint, the results covering the whole processing area are selected, and then the vertical deformation information covering the entire engineering corridor area by ignoring the north–south displacement is calculated. The results show that the surface of the study area, as a whole, slightly subsided between May 2017 and March 2022, and the vertical deformation rate was mostly distributed at −27.068 mm/yr − 18.586 mm/yr, with an average of −1.06 mm/yr. Vertical deformation dominated at 52.84 percent of the study area, of which settlement accounted for 27.57 percent and uplift accounted for 25.27 percent. According to the statistics of the normal distribution of deformation velocity per pixel, a total of 77% of the engineering corridor was stable, with a vertical deformation rate between −6.964 mm/yr and −4.844 mm/yr, and 17.7% of the region was sub-stable, with a settling rate of −12.868 mm/yr − –6.964 mm/yr. The unstable regions included areas with settlement rates greater than 12.868 mm/yr and uplift rates greater than 10.748 mm/yr, representing 4.4 percent and 0.9 percent of the total area, respectively, for a total of 5.3 percent. The results of this paper can be used as the theoretical basis and as basic data for decision making and scientific research in various departments, and they are of great significance for surface stability assessment and early warnings along engineering corridors and traffic projects. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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22 pages, 29803 KiB

Open AccessArticle

Mountain Glacier Flow Velocity Retrieval from Ascending and Descending Sentinel-1 Data Using the Offset Tracking and MSBAS Technique: A Case Study of the Siachen Glacier in Karakoram from 2017 to 2021

by Qian Liang and Ninglian Wang

Remote Sens. 2023, 15(10), 2594; https://doi.org/10.3390/rs15102594 - 16 May 2023

Cited by 4 | Viewed by 2239

Abstract

Synthetic Aperture Radar images have recently been utilized in glacier surface flow velocity research due to their continuously improving imaging technology, which increases the resolution and scope of research. In this study, we employed the offset tracking and multidimensional small baseline subset (MSBAS) technique to extract the surface flow velocity of the Siachen Glacier from 253 Sentinel-1 images. From 2017 to 2021, the Siachen Glacier had an average flow velocity of 38.25 m a⁻¹, with the highest flow velocity of 353.35 m a⁻¹ located in the upper part of a tributary due to the steep slope and narrow valley. The inter-annual flow velocity fluctuations show visible seasonal patterns, with the highest flow velocity observed between May and July and the lowest between December and January. Mass balance calculated by the geodetic method based on AST14DEM indicates that the Siachen Glacier experienced a positive mass change (0.07 ± 0.23 m w.e. a⁻¹) between 2008 and 2021. However, there was significant spatial heterogeneity revealed in the distribution, with surface elevation changes showing a decrease in the glacier tongue while thickness increased in two other western tributaries of the Siachen Glacier. The non-surface parallel flow component is correlated with the strain rate and mass balance process, and correlation analysis indicates a positive agreement between these two variables. Therefore, using glacier flow velocities obtained from the SAR approach, we can evaluate the health of the glacier and obtain crucial factors for the glacier’s dynamic model. Two western tributaries of the Siachen Glacier experienced mass gain in the past two decades, necessitating close monitoring of flow velocity changes in the future to detect potential glacier surges. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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12 pages, 7039 KiB

Open AccessCommunication

Icequakes and Large Shear Wave Velocity Drop in the Kuoqionggangri Glacier of Tibetan Plateau Observed with Fiber Optic Seismometer Array

by Yanan Li, Wenzhu Huang, Guohui Li, Wei Yang, Xiaolong Zhang, Jiule Li, Wentao Zhang and Baiqing Xu

Remote Sens. 2023, 15(5), 1282; https://doi.org/10.3390/rs15051282 - 25 Feb 2023

Cited by 1 | Viewed by 2031

Abstract

We developed a kind of fiber optic seismometer array for a high mountain glacier and first tested it on the Kuoqionggangri Glacier in the Tibetan Plateau. The array clearly recorded substantial passive seismic source signals of various icequakes, including shallow, deep and hybrid events. These fracturing activities indicate that crevasses and/or fractures developed in the glacier. We further obtained the glacial thickness of about 40 m by analyzing the active seismic source after hitting the glacier surface with a hammer based on the seismic scattering method. Most importantly, we observed a low shear wave velocity layer with a large velocity drop of ~28% and thickness of about ~7 m in the lower glacier. It is inferred that the low-velocity layer may represent a temperate ice layer. Our experiment provides a kind of feasible seismic observation to study icequakes and the englacial structure of Tibetan glaciers, offering new insights for evaluating glacier change in the Tibetan Plateau. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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13 pages, 31341 KiB

Open AccessArticle

Characterizing the Changes in Permafrost Thickness across Tibetan Plateau

by Yufeng Zhao, Yingying Yao, Huijun Jin, Bin Cao, Yue Hu, Youhua Ran and Yihang Zhang

Remote Sens. 2023, 15(1), 206; https://doi.org/10.3390/rs15010206 - 30 Dec 2022

Cited by 6 | Viewed by 2804

Abstract

Permafrost impacts the subsurface hydrology and determines the transport of buried biochemical substances. Current evaluations of permafrost mostly focus on the overlying active layer. However, the basic but missing information of permafrost thickness constrains the quantification of trends and effects of permafrost degradation on subsurface hydrological processes. Our study quantified the long-term variations in permafrost thickness on the Tibetan Plateau (TP) between 1851 and 2100 based on layered soil temperatures calculated from eight earth system models (ESMs) of Coupled Model Intercomparison Project (the sixth phase) and validated by field observations and previous permafrost pattern from remote sensing. The calculated permafrost distribution based on ESMs was validated by the pattern derived from the MODIS datasets and field survey. Our results show that permafrost thicker than 10 m covers approximately 0.97 million km

^{2}

of the total area of the TP, which represents an areal extent of over 36.49% of the whole TP. The mean permafrost thickness of the TP was 43.20 m between 1851 and 2014, and it would decrease at an average rate of 9.42, 14.99, 18.78, and 20.75 cm per year under scenarios SSP126, SSP245, SSP370, and SSP585 from 2015 to 2100, respectively. The permafrost thickness will decrease by over 50 cm per year in Qiangtang Basin under SSP585. Our study provides new insights for spatiotemporal changes in permafrost thickness and a basic dataset combined results of remote sensing, field measurements for further exploring relevant hydrological, geomorphic processes and biogeochemical cycles in the plateau cryospheric environment. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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16 pages, 6258 KiB

Open AccessArticle

Effect of Cloud Mask on the Consistency of Snow Cover Products from MODIS and VIIRS

by Anwei Liu, Tao Che, Xiaodong Huang, Liyun Dai, Jing Wang and Jie Deng

Remote Sens. 2022, 14(23), 6134; https://doi.org/10.3390/rs14236134 - 3 Dec 2022

Cited by 3 | Viewed by 1853

Abstract

Snow cover has significant impacts on the global water cycle, ecosystem, and climate change. At present, satellite remote sensing is regarded as the most efficient approach to detect long-term and multiscale observations of snow cover extent. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze and evaluate the consistency of the snow cover products retrieved from these two sensors. In this study, we performed comparisons and a consistency evaluation between the MODIS and VIIRS snow cover products in three major snow distribution regions in China: Northeast China (NE), Northwest China (NW) and the Qinghai–Tibet Plateau (QT). The results demonstrated that (1) the normalized difference snow index (NDSI)-derived snow cover products showed suitable consistency between VIIRS and MODIS under clear sky conditions, with a mean difference value of less than 5%; (2) the VIIRS snow cover product presented much more snow and fewer clouds than that of MODIS in the snow season due to the differences in cloud-masking algorithms; (3) cloud mask strongly affects the potential of snow cover observation, and presents seasonal pattern in the test regions; and (4) VIIRS is able to distinguish clouds from snow with greater accuracy. The comparisons indicated that the greater the difference in cloud cover, the poorer the agreement in snow cover. This evaluation implies that perfecting the cloud-masking algorithm of VIIRS to update the MODIS would be the best solution to achieve better consistency for long-term and high-quality snow cover products. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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22 pages, 27308 KiB

Open AccessArticle

Characterizing Spatiotemporal Patterns of Snowfall in the Kaidu River Basin from 2000–2020 Using MODIS Observations

by Jiangeng Wang, Linglong Zhu, Yonghong Zhang, Wei Huang, Kaida Song and Feng Tian

Remote Sens. 2022, 14(22), 5885; https://doi.org/10.3390/rs14225885 - 20 Nov 2022

Cited by 1 | Viewed by 1665

Abstract

Characterizing spatiotemporal patterns of snowfall is essential for understanding cryosphere responses to warming climate stress. The changes in snowfall and topographic controls in mountain regions still need to be clarified. This study proposes a general parsimonious methodology to obtain the frequency of snowfall in mountainous areas. The methodology employed is easily transferable to any other mountain region. Utilizing daily MODIS observations from June 2000 to May 2020 and the snowfall event detection algorithm, we monitored the frequency of snowfall in a long time series in the Kaidu river basin. The results are as follows: (1) The method for detecting the frequency of snowfall has high accuracy. The annual detected results agreed with surface observations, with an R² of 0.65 and RMSE of 3.39. (2) The frequency of snowfall events increased monotonically with elevation. The influence of slope angle on snowfall gradually decreased with increasing elevation. (3) The frequency of snowfall events in the Kaidu river basin was dominated by an increasing trend. The trends showed a pronounced topographic dependence. This study reveals the distribution characteristics and changing snowfall trends in mountain regions. The results provide a reference for snowfall research in mountainous areas. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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18 pages, 6666 KiB

Open AccessArticle

Deformation and Volumetric Change in a Typical Retrogressive Thaw Slump in Permafrost Regions of the Central Tibetan Plateau, China

by Chenglong Jiao, Fujun Niu, Peifeng He, Lu Ren, Jing Luo and Yi Shan

Remote Sens. 2022, 14(21), 5592; https://doi.org/10.3390/rs14215592 - 6 Nov 2022

Cited by 6 | Viewed by 2587

Abstract

Ice-rich permafrost in the Qinghai–Tibet Plateau (QTP), China, is becoming susceptible to thermokarst landforms, and the most dramatic among these terrain-altering landforms is retrogressive thaw slump (RTS). Concurrently, RTS development can in turn affect the eco-environment, and especially soil erosion and carbon emission, during their evolution. However, there are still a lack of quantitative methods and comprehensive studies on the deformation and volumetric change in RTS. The purpose of this study is to quantitatively assess the RTS evolution through a novel and feasible simulation framework of the GPU-based discrete element method (DEM) coupled with the finite difference method (FDM). Additionally, the simulation results were calibrated using the time series observation results from September 2021 to August 2022, using the combined methods of terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV). The results reveal that, over this time, thaw slump mobilized a total volume of 1335 m³ and approximately 1050 m³ moved to a displaced area. Additionally, the estimated soil erosion was about 211 m³. Meanwhile, the corresponding maximum ground subsidence and headwall retrogression were 1.9 m and 3.2 m, respectively. We also found that the amount of mass wasting in RTS development is highly related to the ground ice content. When the volumetric ice content exceeds 10%, there will be obvious mass wasting in the thaw slump development area. Furthermore, this work proposed that the coupled DEM-FDM method and field survey method of TLS-UAV can provide an effective pathway to simulate thaw-induced slope failure problems and complement the research limitations of small-scale RTSs using remote sensing methods. The results are meaningful for assessing the eco-environmental impacts on the QTP. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Mountain Glaciers, Permafrost, and Snow Cover)

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