Next Article in Journal
Real-Time LEO Satellite Orbits Based on Batch Least-Squares Orbit Determination with Short-Term Orbit Prediction
Previous Article in Journal
An Improved Iterative Reweighted STAP Algorithm for Airborne Radar
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 15
Issue 1
10.3390/rs15010132
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Warming Has Accelerated the Melting of Glaciers on the Tibetan Plateau, but the Debris-Covered Glaciers Are Rapidly Expanding

by
Mingcheng Hu
1,
Guangsheng Zhou
1,2,3,*,
Xiaomin Lv
2,
Li Zhou
2,
Xiaoliang Wang
1,
Xiaohui He
1 and
Zhihui Tian
1
1
Joint Laboratory of Eco-Meteorology, School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China
2
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
3
Collaborative Innovation Center on Forecast Meteorological Disaster Warning and Assessment, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(1), 132; https://doi.org/10.3390/rs15010132
Submission received: 31 October 2022 / Revised: 22 December 2022 / Accepted: 23 December 2022 / Published: 26 December 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
Glacier changes on the Tibetan Plateau are of great importance for regional climate and hydrology and even global ecological changes. It is urgent to understand the effect of climate warming on both clean and debris-covered glaciers on the Tibetan Plateau. This study used the double RF method and Landsat series images to extract clean glaciers and debris-covered glaciers on the Tibetan Plateau from 1985 to 2020 and analyzed their temporal and spatial changes under the background of climate change. The total area of glaciers on the Tibetan Plateau showed a retreating trend from 1985 to 2020, with an average retreat rate of −0.5 % yr−1. The area of clean glaciers showed a significant retreating trend, with a retreat rate of −0.55 % yr−1. The area of debris-covered glaciers showed an expanding trend, with an expanding rate of 0.62 % yr−1. The clean glaciers retreated faster in the southeast and slower in the northwest, while the debris-covered glaciers expanded in most basins. The debris-covered glaciers were generally located at lower elevation areas than those of the clean glaciers. The slopes of clean glaciers were mainly in the range of 0–50°, while the slopes of debris-covered glaciers were mainly in the range of 0–30°. Climate warming was a main driver of glacier change. The clean glacier area was correlated negatively with average temperature in summer and positively with average precipitation in winter, while the debris-covered glacier area was correlated positively with both. The results of the study may provide a basis for scientific management of glaciers on the Tibetan Plateau in the context of climate warming.

1. Introduction

Glaciers are an important component of the Tibetan Plateau, the “water tower of Asia”, and are of great importance to regional climate and hydrology, as well as to global ecological changes [1,2,3,4]. Climate change has already led to glacier shrinkage on the Tibetan Plateau and surrounding areas, but the rate of glacier shrinkage varies between regions [5,6,7,8].
Glaciers in many sub-regions of the Tibetan Plateau are shrinking rapidly, such as the Himalayas, Nyainqentangula, Altai Mountain, the central and eastern Tianshan Mountains [9], etc. There are also some sub-regions where glaciers are shrinking slowly, such as Qiangtang Plateau, West Kunlun Mountain [10,11,12], Pamirs Plateau, and West Tianshan Mountain. However, the area of glaciers in the Karakorum region is almost unchanged, that is, the phenomenon of the “Karakorum anomaly” (glaciers in the Karakorum moved forward at the end of the 20th century when glaciers were generally in retreat globally) [13,14]. Glacier variability is associated with changes in precipitation in the monsoon-dominated southeast and westerly-dominated northwest [6]. Existing studies have focused on clean glaciers and ignored debris-covered glaciers generally. Climate change, however, may accelerate rock calving and increase the accumulation of debris on glaciers and mountain slopes, forcing geomorphic processes on alpine slopes. As a result, the mountain glaciers of the Pamir, Karakoram, Kunlun, Nyainqentangula, and Himalaya Mountains have extensive debris cover [15,16,17,18,19]. The debris layer has unique thermal conductivity that results in different ablation processes in the underlying glacier [20,21], which affects the mass balance of the glacier and its meltwater runoff. Therefore, understanding the spatial and temporal distribution of clean and debris-covered glaciers on the Tibetan Plateau and their relationship with climate will not only improve the understanding of glaciers and their meltwater runoff but also contribute to the scientific formulation of measures to deal with climate change in the “Water Tower of Asia”.
At present, methods for extracting glaciers based on remote sensing information include the Normalized Difference Snow Index (NDSI) method [22], the band ratio method [23,24], random forest (RF) supervised classification [25,26], etc. These methods can extract clean ice or snow with high accuracy, but it is difficult to extract debris-covered glaciers accurately because the spectral information of these debris-covered glaciers is very similar to that of rocks [27,28]. Therefore, some other parameters or methods are used to identify debris-covered glaciers, such as topographic parameters based on digital elevation models [29,30,31,32], thermal properties in the infrared band [33,34], unique velocity characteristics of glaciers [35], and coherent changes in synthetic aperture radar (SAR) images [36,37,38]. These parameters or methods have improved the accuracy of debris-covered glacier extraction to a certain extent, but they are difficult to apply on a large scale due to complex pre-processing and data limitations [39]. The newly proposed double RF method [40] can significantly improve the extraction accuracy of clean glaciers and debris-covered glaciers on the Tibetan Plateau. The method is based on Google Earth Engine (GEE) [41,42] and combines various classification features to extract different types of glaciers in steps by the random forest method, achieving accurate extraction of glaciers under the influence of shadows, snow, cloudiness, and debris [40].
This study uses the double RF method to extract both clean and debris-covered glaciers on the Tibetan Plateau from 1985 to 2020 in a stepwise manner and then analyzes the spatial and temporal evolution characteristics of different types of glaciers. The results reveal the impact of climate change on the changes of different types of glaciers and provide a basis for scientific responses to climate change.

2. Study Region and Data

2.1. Study Region

The Tibetan Plateau is bounded by the Himalayas in the south, the Kunlun Mountains, the Alps, and the Qilian Mountains in the north, and the Pamir Plateau and the Karakorum Mountains in the west. In the east and northeast, it is connected with the western Qinling Mountains and the Loess Plateau [43] (Figure 1). The average altitude of the Tibetan Plateau is over 4000 m [44], which makes the region form a unique alpine plateau climate.

2.2. Data Sources

The Landsat series of images from 1985 to 2020 was used for this study, with a spatial resolution of 30 m and a temporal resolution of 16 d. Image corrections were made using a fill function provided by the United States Geological Survey (USGS) due to banding problems in Landsat 7 images since June 2003 [46]. Digital elevation was obtained from The Shuttle Radar Topography Mission (SRTM), referred to as SRTMGL1_003, with a spatial resolution of 30 m [47]. Climate data were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Global Climate Fifth Generation Atmospheric Reanalysis (ERA5) for the period 1985–2020.

3. Method

In this study, both clean and debris-covered glaciers on the Tibetan Plateau from 1985 to 2020 were first extracted using the double RF method, then their spatial and temporal evolution characteristics were analyzed, and finally their responses to climate change were explored. The specific process is shown in Figure 2.

3.1. Image Screening and Cloud Filtering

The conditions of image screening include geographical scope and time. In this study, the images of the Tibetan Plateau from June to October during the summer melting period from 1985 to 2020 were selected to reduce the influence of snowfall on glacier extraction.
The cloud scoring algorithm provided by GEE mainly uses cloud filter to calculate the cloud score of each pixel. The cloud score ranges from 0 (no clouds) to 100 (very thick clouds). The terrain of the Tibetan Plateau is very complex, with many areas covered by clouds all the year round. It is difficult to synthesize a complete map with only pixels with a cloud score of 0. Therefore, the threshold of cloud filtering score in this study was set to 60 points [40].

3.2. Division of Time Period

The influence of cloud cover on the Tibetan Plateau makes it difficult to synthesize a complete image with few clouds and little snow using summer images of one year. Thus, this study divided 1985–2020 into several time periods and ensured that the synthetic images of each time period were relatively complete. Since 1985, we added images for one year at a time, then synthesized the images and calculated the empty pixel rate. The steps of image synthesis are shown in Figure 3. When the empty pixel rate of the composite image was less than 1.5%, the division of a time period was completed. Finally, 11 complete images of less cloud and less snow on the Tibetan Plateau were obtained for the period 1985–2020 (Table 1).
The images of each period were synthesized into a complete image of the Tibetan Plateau with less cloud and snow. This study found that the spectral characteristics of snow in the median composite image are very similar to those of clean glaciers but different from those of debris-covered glaciers. In the minimum composite image, there is almost no snow, but the debris glacier is indistinguishable from the surrounding rocks. Therefore, the minimum value of the pixel set was first used to synthesize images to minimize the influence of snow on the extraction of clean glaciers, and then the median value of the pixel set was to synthesize the images, so as to enhance the difference between the debris-covered glaciers and the surrounding rocks, thus accurately extracting the debris-covered glaciers. In this way, there were two images for glacier identification in each time period.

3.3. Feature Construction

According to the double RF method, the input features of the classifier included spectral features, texture, and topographic features. The spectral features included: raw spectra (B1-B7), normalized difference vegetation index (NDVI) [48], normalized difference water index (NDWI) [49], normalized difference snow index (NDSI) [50], brightness, greenness, and humidity information after tassel cap transformation [51], as well as the newly proposed band difference and multi-temporal minimum band ratio [40]. Texture features included six common texture features based on band ratio images: angle second moment, contrast, correlation, variance, inverse different moment, and entropy [52]. The topographic features included elevation, slope, and aspect, while the time period using Landsat 8 imagery also used the thermal infrared band (B10). The features constructed in this study are shown in Table 2. These features have been shown to be appropriate for glacier classification on the Tibetan Plateau [40].

3.4. Sample Point Selection

Default values were used for all parameters of the classifier. The classification samples were obtained by manual visual interpretation using synthetic images. In this study, clean glaciers, water bodies, and others were used for the first classification, while snow, debris-covered glaciers, water bodies, and others were used for the second classification. In the process of selecting sample points for each category, the principle of randomness and homogeneity was applied to try to have collections throughout the study area. The sample points for all time periods were selected as shown in Figure 4.

3.5. Accuracy Validation

Common accuracy evaluation metrics include overall accuracy, kappa coefficient, producer accuracy, and user accuracy based on the classification confusion matrix [53]. In this study, the total sample was divided into two parts using a cross-validation method, where 70% of the sample points from each category were randomly selected to train the model and 30% of the sample points were retained for validation to calculate the overall accuracy and kappa coefficient.

3.6. Post-Classification Processing

In the classification results of supervised classification, band ratio method or decision tree, there are inevitably some incorrectly classified noise points or small pixel patches, which brings some adverse effects on image quality and accuracy [54,55]. To eliminate this noise (small pixel patches), the initial classification results were filtered. In the first classification, small patches with less than 50 connected pixels were filtered out, and in the second classification, the patches with less than 100 connected pixels were filtered out and larger debris-covered glaciers were retained.

4. Results and Analysis

4.1. Accuracy Verification of Glacier Extraction

The overall classification accuracy and kappa coefficient of each time period are shown in Figure 5. The overall accuracy (OA) of the first classification was better than that of the second. The overall accuracy and kappa coefficient of the first classification were mostly above 0.9, while the overall accuracy and kappa coefficient of the second classification were mostly above 0.65. The accuracy of the validation set showed a gradual increase from 1985 to 2020, probably due to the low number of available remote sensing images in the earlier period.

4.2. Spatial and Temporal Changes of Glaciers

The glacier areas for 11 periods from 1985 to 2020 on the Tibetan Plateau extracted by the double RF method and their change rates are shown in Figure 5. The glaciers showed an overall retreat trend from 1985 to 2020, the total glacier area decreased from 82,814.79 km2 in 1985–1993 to 67,882.12 km2 in 2019–2020, with an average retreat rate of −0.5% yr−1, with the fastest retreat rate of −1.94 % yr−1 in 2007–2009 (Figure 6-a1). The clean glaciers showed a clear trend of retreat, with the area decreasing from 79,086.34 km2 in 1985–1993 to 63,322.9 km2 in 2016–2020, with an average rate of retreat of −0.55% yr−1 (Figure 6-b1). The debris-covered glaciers showed a trend of expansion, increasing in area from 3,728.45 km2 in 1985–1993 to 4,559.22 km2, with an average rate of expanding of 0.62% yr−1 but with large variability in the rate of change over time (Figure 6-c1).
In this study, linear fitting and quadratic polynomial fitting were used to fit the area of total glaciers, clean glaciers, and debris-covered glaciers in each period, respectively. As can be seen from Figure 6(a2,b2,c2), in the fitting curves of all kinds of glaciers, the adjusted R2 of quadratic polynomial fitting was greater than that of linear fitting. Therefore, the fitting effect of quadratic polynomial was better than that of linear fitting in the fitting curves of various glaciers. It could be concluded that the change rate of each glacier area was not constant but gradually accelerated.

4.3. Glacier Changes in Different Basins

There was significant spatial heterogeneity in glacier change across the basins of the Tibetan Plateau from 1985 to 2020. In this study, the average change rate was calculated by subtracting the first area data from the last area data of each basin (Figure 7). As a whole, the clean glaciers were shrinking faster in the southeast and slower in the northwest. The fastest shrinking basin of clean glaciers was the Mekong basin, with a retreat rate of −1.47% yr−1, and the slowest shrinking basin was the Tarim basin, with a retreat rate of −0.17% yr−1. The area of debris-covered glaciers in most basins was increasing, but only the areas of debris-covered glaciers in Inner Plateau, Yangtze, and Hexi Corridor basin were decreasing.

4.4. Geographical Characteristics of Glacier Distribution

To elucidate the geographical characteristics of glacier distribution on the Tibetan Plateau, this study overlaid glacier data with DEM data to count the distribution of glaciers at different elevations, slopes, and aspects (Figure 8, Figure 9 and Figure 10).

4.4.1. Glacier Distribution at Different Elevations

Taking the latest period of 2019–2020 as an example, whether clean glaciers or debris-covered glaciers, the distribution of glaciers at different elevations was similar to the normal distribution. The clean glaciers were mainly distributed in the elevation range of 4000–8000 m, and the most distributed when the elevation was 5500 m. The debris-covered glaciers were mainly distributed in the elevation range of 2000–6000 m, and the most distributed when the elevation was 4500 m (Figure 8a), which showed that the distribution elevation of debris-covered glaciers was generally lower than that of clean glaciers. The rate of change of glaciers at different elevations varied. The retreat rate of clean glaciers increased with decreasing elevation, while the expansion rate of debris-covered glaciers increased with increasing elevation, indicating that both clean and debris-covered glaciers had a tendency to expand to higher elevations.

4.4.2. Glacier Distribution at Different Slopes

Glaciers on the Tibetan Plateau showed a decreasing trend with increasing slope (Figure 9). The clean glaciers were mainly concentrated in 0–50°, accounting for more than 95% of the total. The debris-covered glaciers were mainly concentrated at 0–30°, with a significantly lower distribution slope than clean glaciers. It could be seen from this that when the slope was greater than 50° or 30°, it was not conducive to the development of clean glaciers or debris-covered glaciers. The fastest retreat rate of clean glaciers occurred in the 10–40° range, while the debris-covered glaciers expanded the fastest when the slope was 10°.

4.4.3. Glacier Distribution in Different Aspects

The clean glaciers were less distributed in the south or southwest slope and more distributed in the northeast slope, and the glaciers in the northeast slope retreated fastest. The debris-covered glaciers were more distributed in the northeast and southwest slope, while the glaciers expanded faster in the southeast slope and slower in the northwest slope (Figure 10). It may be related to the plateau monsoon climate and water vapor source [56,57].

4.5. The Relationship of Glacier Changes with Climate

Glacier change is the result of glacier accumulation and ablation and is influenced by temperature, precipitation, humidity, and evaporation, with temperature and precipitation being the most important influencing factors. Studies have shown that for every 1 °C increase in temperature, a 25% or 35% increase in precipitation is required to compensate [58,59]. Based on the European Centre for Medium-Range Weather Forecasts (ECMWF) Global Climate 5th Generation Atmospheric Reanalysis (ERA5) data from 1985–2020, this study used GEE cloud platform to calculate the annual average temperature, average temperature in summer, annual average precipitation, and average precipitation in winter on the Tibetan Plateau from 1985 to 2020 (Figure 11).
In this study, Pearson correlation coefficients between total glacier area and four climatic factors were calculated: annual average temperature, average temperature in summer, annual average precipitation and average precipitation in winter (Table 3). From Table 3, it can be seen that the average temperature in summer and average precipitation in winter were more closely related to the glacier area of the Tibetan Plateau, and they were more suitable for exploring the relationship between glacier area and climate.
To find out the relationship between different types of glaciers and climate, this study calculated 11 groups of average temperature in summer and average precipitation in winter on the Tibetan Plateau according to time period divided during glacier identification and then carried out multiple regression analysis on these data and different types of glaciers. The results are shown in Table 4. The clean glacier area on the Tibetan Plateau was negatively correlated with average temperature in summer and positively correlated with average precipitation in winter (Table 4), indicating that climate warming was the dominant factor in glacier melt and that increased precipitation slowed the melting of glaciers. For clean glaciers, it took a 1 °C increase in temperature and a 236 mm increase in precipitation to offset the effect. The standardized regression coefficient showed that the effect of temperature was about 12 times greater than that of precipitation. The area of debris-covered glaciers was positively correlated with both average temperature in summer and average precipitation in winter. In other words, the increase in temperature and precipitation promoted the expansion of debris-covered glaciers, and the effect of temperature was approximately eight times greater than that of precipitation.

5. Discussion

5.1. Comparison of Glacier Extraction Results with Existing Studies

5.1.1. Glacier Extraction Results

Comparing the glacier extraction results of 11 periods from 1985 to 2020 with the research results of the corresponding periods, it shows (Table 5) that the differences between the extraction results of 1985–1993, 2005–2006, and 2017–2018 and the existing research results were within 2% [60,61,62]. The clean glacier area of the Tibetan Plateau in 2001 by hand drawing was about 42,210 ± 1621 km2 based on Landsat 7 images from 1999 to 2002 [63], and it is obviously larger than the result of this study. The reasons were that 71% of the remote sensing images used by the former were from winter, and the snow widely distributed in winter may lead to the overestimation of glacier area; on the other hand, this study only synthesized a complete cloudless or partly cloudy image based on 5 years’ data from 1997 to 2001. The extracted glacier area was the average area from 1997 to 2001, and it is different from the glacier area in 2001. At the same time, the glacier area was underestimated to some extent due to the influence of empty pixels.

5.1.2. Change Rate of Glaciers

Table 6 shows the comparison between this study and existing studies on the change rate of clean glaciers. Although the research scope and time were not exactly the same, the results were consistent in most sub-regions and periods, and the average annual change rate was less than 0.2%.
However, it has been shown that the clean glacier retreat rates of −0.36% yr−1 and −0.78% yr−1 in the Salween and Mekong basins during 1988–2013 [63], respectively, which are significantly lower than the −0.62% yr−1 and −1.52% yr−1 in this study. This may be caused by different glacial extraction periods (1988–2013 in the former, 1985–2014 in this study) and the effects of snow cover during the glacial extraction period. Other studies showed that the average annual change rate of clean glacier area in Amu Darya Basin from 1990 to 2018 was −0.59 ± 0.1% yr−1 [64]. Because the study areas were not completely consistent, the results of this study were quite different from them. Because the time and scope of the research are not completely consistent, there is definitely a gap between different research results, but this tiny gap verifies our research results to some extent.

5.2. Possible Causes of Glacier Changes

Precipitation and temperature are the main climatic factors that affect the development of glaciers. Temperature determines the melting of glaciers, precipitation determines the accumulation of glaciers, and their combination determines the nature, development, and evolution of glaciers [67]. In addition, the physical properties and attributes of the glacier itself, as well as the terrain conditions such as altitude, slope and aspect, also affect the development and change of the glacier to varying degrees. The results of this study show that under the background of global warming, the clean glaciers on the Tibetan Plateau were shrinking, while the debris-covered glaciers were expanding. Clean glaciers are very sensitive to the temperature. As the temperature rises, clean glaciers melt. In addition, some clean glaciers may also be transformed into debris-covered glaciers due to the rock movement caused by severe climate change, which may also be one of the main reasons for the retreat of clean glaciers and the expansion of debris-covered glaciers. In this study, from 1985 to 2020, the area of debris-covered glaciers on the Tibet Plateau increased by 830.77 km2, of which about 35% was converted from clean glaciers and 65% from natural expansion of debris-covered glaciers. However, sudden and large-scale rock mass movements are common in mountain areas, such as rockfalls, clastic ice, and avalanches [68]. For example, from September 1999 to June 2000, a landslide occurred in the upper part of Batra Glacier, which may have been caused by slope failure. The accumulated rock fragments cover the glacier surface, and their shapes and sizes change dynamically as the glacier ice moves downstream. This phenomenon plays a key role in the increase and fluctuation in the area covered by debris [69]. So, this is one of the reasons for the instability of the change rate of debris-covered glaciers.

5.3. Limitations

5.3.1. Factors Affecting Glacier Identification

(1) Image resolution
Both supervised classification and unsupervised classification need to manually take samples or use other people’s sample datasets. The samples of this study were obtained by manual visual interpretation, but the spatial resolution of 30 m definitely affects the accuracy of the samples. Therefore, to improve the accuracy of classification, we can use high-resolution remote sensing images for visual interpretation. At the same time, the 30 m resolution DEM may not reflect the key surface features, which also affects the accuracy of classification.
(2) Snow
Although the double RF method can greatly reduce the impact of snow cover, it cannot distinguish permanent snow cover. The spectral features of permanent snow cover are very similar to those of glaciers, and only texture features can be used to assist extraction.
(3) Empty pixels
Due to the high altitude of the Tibetan Plateau and the frequent occurrence of clouds, it is difficult to synthesize a complete cloudless or partly cloudy image based on the remote sensing image data of that year. Therefore, in this study, the threshold of cloud filtering score was set to 60 points, and the composite image was subjected to cloud filtering. In most cases, there were still many pixels left in the pixel set after cloud filtering, but in extreme cases, there would be no pixel set left, and the resulting empty pixels were considered as non-glacial pixels (Figure 3). In fact, the ground objects covered by such empty pixels may be glaciers, which leads to the underestimation of glacier area. Although the empty pixel rate is less than 1% in most periods of this study, the percentage of glacier cover in the study area is usually about 3%. Therefore, the empty pixels still affect the accuracy of glacier extraction. In the future, it is necessary to strengthen the reconstruction of remote sensing information of the Tibetan Plateau with long time and high spatial–temporal resolution to improve the accuracy of glacier extraction.

5.3.2. Relationship between Glacier Change and Climate

In this study, the relationship between glacier change and climate was calculated by using the glacier area in each period. However, there may be noises in glacier area data caused by external conditions (snow, shadows, debris, clouds, etc.), and there were little glacier area data, so these noises would seriously affect the calculation of the glacier change rate. For example, the increase of precipitation in recent years (Figure 11) brought more snow to high altitude places, which may lead to the overestimation of glacier area. Therefore, when studying glacier changes in the future, we should not only ensure the accuracy of glacier identification but also dig up more data as much as possible.
Furthermore, when analyzing the relationship between glacier change and climate, we only considered two factors: temperature and precipitation. However, the actual mechanism of glacier change is complicated, and it is not only related to a variety of influencing factors of climate change (such as temperature, precipitation, humidity, radiation, evapotranspiration, water vapor, etc.) but also related to physical and chemical properties of glaciers themselves. Glacier characteristics, ice formation, glacier erosion, glacier deposition, and glacier thickness are all closely related to glacier change. Therefore, the purpose of this study is to discuss the relationship between glacier change and climate factors instead of establishing a model to predict glacier change. When studying glacier changes in the future, it should be more in-depth, systematic, and complete, and it is necessary to fully consider the comprehensive effects of various factors.

6. Conclusions

Extracting glaciers from remote sensing images is easily affected by mountain and cloud shadows, cloud cover, and seasonal snow cover. In this study, by counting the empty pixel rate of the composite image, 1985–2020 was divided into 11 periods, and then the clean glaciers and debris-covered glaciers of the 11 periods on the Tibetan Plateau were gradually extracted using the double RF method. The results show that the clean glaciers showed an obvious retreat trend, with an average retreat rate of −0.55% yr−1, while the area of debris-covered glaciers showed an expanding trend, with an expanding rate of 0.62 % yr−1 but with large variability in the rate of change over time.
According to the analysis of the evolution characteristics of glaciers on the Tibetan Plateau from different basins, elevations, slopes, and aspects, it is found that the clean glaciers were shrinking faster in the southeast and slower in the northwest, while the area of debris-covered glaciers in most basins was increasing. The distribution elevation of debris-covered glaciers was generally lower than that of clean glaciers. Both clean and debris-covered glaciers were constantly expanding to high altitudes. The slopes of clean glaciers were mainly 0–50°, while those of debris-covered glaciers were mainly 0–30°. There were fewer clean glaciers in the south or southwest slope, and more clean glaciers in the northeast slope, while debris-covered glaciers were more distributed in the northeast and southwest slope.
Climate warming was a main driver of glacier change. The clean glacier area was negatively correlated with the average temperature in summer and positively correlated with the average precipitation in winter, while the debris-covered glacier area was correlated positively with both.
With the global warming, more and more clean glaciers in Asian alpine areas have been transformed into debris-covered glaciers, which may greatly affect the water resources in the basin and its future trend. In the future, it is necessary to consider all the factors affecting glaciers, establish a general glacier model, reveal the relationship between glaciers and climate, and provide a theoretical basis for glacier change prediction and disaster prevention on the Tibetan Plateau as well as for the rational utilization of glacier melt water resources.

Author Contributions

Conceptualization, M.H., G.Z., and X.W.; methodology, M.H.; validation, M.H., G.Z., and X.L.; formal analysis, G.Z., and L.Z.; data curation, X.L.; writing—original draft preparation, M.H.; writing—review and editing, G.Z.; funding acquisition, G.Z.; supervision, X.H., Z.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Second Tibetan Plateau Comprehensive Research Project (2019QZKK0106), the National Natural Science Foundation of China (42130514), and the Fundamental Research Funds of the Chinese Academy of Meteorological Sciences (2020Z004, 2022Y015).

Data Availability Statement

Not applicable.

Acknowledgments

We thank the Google Earth Engine Science team for the freely available cloud-computing platform and USGS for Landsat imagery and SRTM DEM. We are thankful for the glacier dataset provided by Science Data Bank.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ruddiman, W.F.; Kutzbach, J.E. Plateau uplift and climatic change. Sci. Am. 1991, 264, 66–75. [Google Scholar] [CrossRef]
  2. Molnar, P.; England, P.; Martinod, J. Mantle dynamics, uplift of the Tibetan Plateau, and the Indian Monsoon. Rev. Geophys. 1993, 31, 357–396. [Google Scholar] [CrossRef]
  3. Asahi, K. Equilibrium-line altitudes of the present and Last Glacial Maximum in the eastern Nepal Himalayas and their implications for SW monsoon climate. Quat. Int. 2010, 212, 26–34. [Google Scholar] [CrossRef]
  4. Zemp, M.; Huss, M.; Thibert, E.; Eckert, N.; McNabb, R.; Huber, J.; Barandun, M.; Machguth, H.; Nussbaumer, S.U.; Gärtner-Roer, I. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 2019, 568, 382–386. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Kääb, A.; Berthier, E.; Nuth, C.; Gardelle, J.; Arnaud, Y. Contrasting patterns of early twenty-first-century glacier mass change in the Himalayas. Nature 2012, 488, 495–498. [Google Scholar] [CrossRef]
  6. Yao, T.; Thompson, L.; Yang, W.; Yu, W.; Gao, Y.; Guo, X.; Yang, X.; Duan, K.; Zhao, H.; Xu, B. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Chang. 2012, 2, 663–667. [Google Scholar] [CrossRef]
  7. Gardner, A.S.; Moholdt, G.; Cogley, J.G.; Wouters, B.; Arendt, A.A.; Wahr, J.; Berthier, E.; Hock, R.; Pfeffer, W.T.; Kaser, G. A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science 2013, 340, 852–857. [Google Scholar] [CrossRef] [Green Version]
  8. Hugonnet, R.; McNabb, R.; Berthier, E.; Menounos, B.; Nuth, C.; Girod, L.; Farinotti, D.; Huss, M.; Dussaillant, I.; Brun, F. Accelerated global glacier mass loss in the early twenty-first century. Nature 2021, 592, 726–731. [Google Scholar] [CrossRef]
  9. Farinotti, D.; Longuevergne, L.; Moholdt, G.; Duethmann, D.; Mölg, T.; Bolch, T.; Vorogushyn, S.; Güntner, A. Substantial glacier mass loss in the Tien Shan over the past 50 years. Nat. Geosci. 2015, 8, 716–722. [Google Scholar] [CrossRef]
  10. Neckel, N.; Kropáček, J.; Bolch, T.; Hochschild, V. Glacier mass changes on the Tibetan Plateau 2003–2009 derived from ICESat laser altimetry measurements. Environ. Res. Lett. 2014, 9, 014009. [Google Scholar] [CrossRef]
  11. Kääb, A.; Treichler, D.; Nuth, C.; Berthier, E. Brief Communication: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya. Cryosphere 2015, 9, 557–564. [Google Scholar] [CrossRef] [Green Version]
  12. Xie, F.; Liu, S.; Gao, Y.; Zhu, Y.; Bolch, T.; Kääb, A.; Duan, S.; Miao, W.; Kang, J.; Zhang, Y. Interdecadal glacier inventories in the Karakoram since the 1990s. Earth Syst. Sci. Data Discuss. 2022, 1–27. [Google Scholar] [CrossRef]
  13. Gardelle, J.; Berthier, E.; Arnaud, Y. Slight mass gain of Karakoram glaciers in the early twenty-first century. Nat. Geosci. 2012, 5, 322–325. [Google Scholar] [CrossRef]
  14. Hewitt, K. The Karakoram Anomaly? Glacier Expansion and the ‘Elevation Effect’, Karakoram Himalaya. Mt. Res. Dev. 2005, 25, 332–340. [Google Scholar] [CrossRef] [Green Version]
  15. Scherler, D.; Bookhagen, B.; Strecker, M.R. Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nat. Geosci. 2011, 4, 156–159. [Google Scholar] [CrossRef]
  16. Khan, A.; Naz, B.S.; Bowling, L.C. Separating snow, clean and debris covered ice in the Upper Indus Basin, Hindukush-Karakoram-Himalayas, using Landsat images between 1998 and 2002. J. Hydrol. 2015, 521, 46–64. [Google Scholar] [CrossRef] [Green Version]
  17. Zhang, J.; Jia, L.; Menenti, M.; Hu, G. Glacier facies mapping using a machine-learning algorithm: The Parlung Zangbo Basin case study. Remote Sens. 2019, 11, 452. [Google Scholar] [CrossRef] [Green Version]
  18. Scherler, D.; Wulf, H.; Gorelick, N. Global assessment of supraglacial debris—Cover extents. Geophys. Res. Lett. 2018, 45, 11798–711805. [Google Scholar] [CrossRef] [Green Version]
  19. Zhang, Z.; Liu, S.; Wei, J.; Xu, J.; Guo, W.; Bao, W.; Jiang, Z. Mass Change of Glaciers in Muztag Ata–Kongur Tagh, Eastern Pamir, China from 1971/76 to 2013/14 as Derived from Remote Sensing Data. PLoS ONE 2016, 11, e0147327. [Google Scholar] [CrossRef]
  20. Nicholson, L.; Benn, D.I. Calculating ice melt beneath a debris layer using meteorological data. J. Glaciol. 2006, 52, 463–470. [Google Scholar] [CrossRef]
  21. Istrem, G. Ice melting under a thin layer of moraine and the existence of ice cores in moraine ridge. Geogr. Ann. 1959, 41, 228–230. [Google Scholar]
  22. Kulkarni, A.; Srinivasulu, J.; Manjul, S.; Mathur, P. Field based spectral reflectance studies to develop NDSI method for snow cover monitoring. J. Indian Soc. Remote Sens. 2002, 30, 73–80. [Google Scholar] [CrossRef]
  23. Zhang, M.; Wang, X.; Shi, C.; Yan, D. Automated Glacier Extraction Index by Optimization of Red/SWIR and NIR/SWIR Ratio Index for Glacier Mapping Using Landsat Imagery. Water 2019, 11, 1223. [Google Scholar] [CrossRef] [Green Version]
  24. Bolch, T.; Menounos, B.; Wheate, R. Landsat-based inventory of glaciers in western Canada, 1985–2005. Remote Sens. Environ. 2010, 114, 127–137. [Google Scholar] [CrossRef]
  25. Khan, A.A.; Jamil, A.; Hussain, D.; Taj, M.; Jabeen, G.; Malik, M.K. Machine-learning algorithms for mapping debris-covered glaciers: The Hunza Basin case study. IEEE Access 2020, 8, 12725–12734. [Google Scholar] [CrossRef]
  26. Lu, Y.; Zhang, Z.; Huang, D. Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir. Water 2020, 12, 3231. [Google Scholar] [CrossRef]
  27. Robson, B.A.; Nuth, C.; Dahl, S.O.; Hölbling, D.; Strozzi, T.; Nielsen, P.R. Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment. Remote Sens. Environ. 2015, 170, 372–387. [Google Scholar] [CrossRef] [Green Version]
  28. Bennett, M.M.; Glasser, N.F. Glacial Geology: Ice Sheets and Landforms; John Wiley & Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
  29. Paul, F.; Huggel, C.; Kääb, A. Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers. Remote Sens. Environ. 2004, 89, 510–518. [Google Scholar] [CrossRef]
  30. Frey, H.; Paul, F. On the suitability of the SRTM DEM and ASTER GDEM for the compilation of topographic parameters in glacier inventories. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 480–490. [Google Scholar] [CrossRef]
  31. Patel, A.; Prajapati, R.; Dharpure, J.K.; Mani, S.; Chauhan, D. Mapping and monitoring of glacier areal changes using multispectral and elevation data: A case study over Chhota-Shigri glacier. Earth Sci. Inform. 2019, 12, 489–499. [Google Scholar] [CrossRef]
  32. Bolch, T.; Buchroithner, M.F.; Kunert, A.; Kamp, U. Automated delineation of debris-covered glaciers based on ASTER data. In Proceedings of the Geoinformation in Europe, 27th EARSeL Symposium, Bolzano, Italy, 4–7 June 2007; pp. 4–6. [Google Scholar]
  33. Singh, V.; Goyal, M.K. An improved coupled framework for Glacier classification: An integration of optical and thermal infrared remote-sensing bands. Int. J. Remote Sens. 2018, 39, 6864–6892. [Google Scholar] [CrossRef]
  34. Taschner, S.; Ranzi, R. Comparing the opportunities of Landsat-TM and Aster data for monitoring a debris covered glacier in the Italian Alps within the GLIMS project. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada, 24–28 June 2002; pp. 1044–1046. [Google Scholar]
  35. Smith, T.; Bookhagen, B.; Cannon, F. Improving Semi-Automated Glacier Mapping with a Multi-Method Approach: Applications in Central Asia. Cryosphere 2015, 9, 1747–1759. [Google Scholar] [CrossRef] [Green Version]
  36. Janke, J.R.; Bellisario, A.C.; Ferrando, F.A. Classification of debris-covered glaciers and rock glaciers in the Andes of central Chile. Geomorphology 2015, 241, 98–121. [Google Scholar] [CrossRef]
  37. Lippl, S.; Vijay, S.; Braun, M. Automatic delineation of debris-covered glaciers using InSAR coherence derived from X-, C-and L-band radar data: A case study of Yazgyl Glacier. J. Glaciol. 2018, 64, 811–821. [Google Scholar] [CrossRef] [Green Version]
  38. Holobâcă, I.-H.; Tielidze, L.G.; Ivan, K.; Elizbarashvili, M.; Alexe, M.; Germain, D.; Petrescu, S.H.; Pop, O.T.; Gaprindashvili, G. Multi-sensor remote sensing to map glacier debris cover in the Greater Caucasus, Georgia. J. Glaciol. 2021, 67, 685–696. [Google Scholar] [CrossRef]
  39. Racoviteanu, A.; Williams, M.W. Decision tree and texture analysis for mapping debris-covered glaciers in the Kangchenjunga area, Eastern Himalaya. Remote Sens. 2012, 4, 3078–3109. [Google Scholar] [CrossRef] [Green Version]
  40. Hu, M.; Zhou, G.; Lv, X.; Zhou, L.; He, X.; Tian, Z. A New Automatic Extraction Method for Glaciers on the Tibetan Plateau under Clouds, Shadows and Snow Cover. Remote Sens. 2022, 14, 3084. [Google Scholar] [CrossRef]
  41. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
  42. Hird, J.N.; Kariyeva, J.; McDermid, G.J. Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas. Remote Sens. 2021, 13, 4745. [Google Scholar] [CrossRef]
  43. Zhang, Y.; Li, B.; Zheng, D. A discussion on the boundary and area of the Tibetan Plateau in China. Geogr. Res. 2002, 21, 1–8. [Google Scholar] [CrossRef]
  44. Bohner, J. General climatic controls and topoclimatic variations in Central and High Asia. Boreas 2006, 35, 279–295. [Google Scholar] [CrossRef]
  45. Zhang, G. Dataset of River Basins Map over the TP(2016); National Tibetan Plateau/Third Pole Environment Data Center: Beijing, China, 2019. [Google Scholar] [CrossRef]
  46. Scaramuzza, P.; Micijevic, E.; Chander, G. SLC gap-filled products phase one methodology. Landsat Tech. Notes 2004, 5, 1–5. [Google Scholar]
  47. Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L. The shuttle radar topography mission. Rev. Geophys. 2007, 45, RG2004. [Google Scholar] [CrossRef] [Green Version]
  48. Defries, R.S.; Townshend, J.R.G. NDVI-derived land cover classifications at a global scale. Int. J. Remote Sens. 1994, 15, 3567–3586. [Google Scholar] [CrossRef]
  49. Hao, Z.; AghaKouchak, A. Multivariate standardized drought index: A parametric multi-index model. Adv. Water Resour. 2013, 57, 12–18. [Google Scholar] [CrossRef] [Green Version]
  50. Yan, D.; Huang, C.; Ma, N.; Zhang, Y. Improved landsat-based water and snow indices for extracting lake and snow cover/glacier in the tibetan plateau. Water 2020, 12, 1339. [Google Scholar] [CrossRef]
  51. Yarbrough, L.D.; Easson, G.; Kuszmaul, J.S. Proposed workflow for improved Kauth–Thomas transform derivations. Remote Sens. Environ. 2012, 124, 810–818. [Google Scholar] [CrossRef]
  52. Alazawi, S.A.; Shati, N.M.; Abbas, A.H. Texture features extraction based on GLCM for face retrieval system. Period. Eng. Nat. Sci. (PEN) 2019, 7, 1459–1467. [Google Scholar] [CrossRef] [Green Version]
  53. Zourarakis, D.P. Remote Sensing Handbook–Volume I: Remotely Sensed Data Characterization, Classification, and Accuracies. Photogramm. Eng. Remote Sens. 2018, 84, 481. [Google Scholar] [CrossRef]
  54. Huang, X.; Lu, Q.; Zhang, L.; Plaza, A. New postprocessing methods for remote sensing image classification: A systematic study. IEEE Trans. Geosci. Remote Sens. 2014, 52, 7140–7159. [Google Scholar] [CrossRef]
  55. Luo, K. Comparison of three realization methods of remote sensing image classification post-processing. For. Investig. Plan. 2008, 3, 24–26. [Google Scholar]
  56. Li, S.; Li, D.; Zhao, P.; Zhang, G. The climatic characteristics of vapor transportation in rainy season of the origin area of three rivers in Qinhai-Xizang Plateau. Acta Meteor. Sin. 2009, 67, 591–598. [Google Scholar]
  57. Siyang, D.; Xian, X.; Manhou, X. Influence of climate change on water environment in the Qinghai-Tibet Plateau. Arid Land Geogr. 2013, 36, 841–853. [Google Scholar]
  58. Raper, S.; Brown, O.; Braithwaite, R. A geometric glacier model for sea-level change calculations. J. Glaciol. 2000, 46, 357–368. [Google Scholar] [CrossRef]
  59. Oerlemans, J. Extracting a climate signal from 169 glacier records. Science 2005, 308, 675–677. [Google Scholar] [CrossRef] [Green Version]
  60. RGI Consortium. Randolph Glacier Inventory—A Dataset of Global Glacier Outlines, Version 6; NSIDC: National Snow and Ice Data Center: Boulder, CO, USA, 2017. [Google Scholar] [CrossRef]
  61. Liu, S.; Yao, X.; Guo, W.; Xu, J.; Shangguan, D.; Wei, J.; Bao, W.; Wu, L. The contemporary glaciers in China based on the Second Chinese Glacier Inventory. Acta Geogr. Sin. 2015, 70, 3–16. [Google Scholar]
  62. Ran, W.; Wang, X.; Guo, w.; Zhao, H.; Zhao, X.; Liu, S.; Wei, J.; Zhang, Y. A Dataset of Glacier Inventory in Western China during 2017–2018 (V1); Science Data Bank: Beijing, China, 2021. [Google Scholar] [CrossRef]
  63. Ye, Q.; Zong, J.; Tian, L.; Cogley, J.G.; Song, C.; Guo, W. Glacier changes on the Tibetan Plateau derived from Landsat imagery: Mid-1970s–2000–13. J. Glaciol. 2017, 63, 273–287. [Google Scholar] [CrossRef] [Green Version]
  64. Huang, L.; Li, Z.; Zhou, J.M.; Zhang, P. An automatic method for clean glacier and nonseasonal snow area change estimation in High Mountain Asia from 1990 to 2018. Remote Sens. Environ. 2021, 258, 112376. [Google Scholar] [CrossRef]
  65. Lv, H. Response of Glacier Variation to Climate Change in the Himalayan Mountains, during the Last 40 Year; Lanzhou University: Lanzhou, China, 2013. [Google Scholar]
  66. Ninglian, W.; Tandong, Y.; Baiqing, X.; An’an, C.; Weicai, W. Spatiotemporal pattern, trend, and influence of glacier change in Tibetan Plateau and surroundings under global warming. Bull. Chin. Acad. Sci. (Chin. Version) 2019, 34, 1220–1232. [Google Scholar]
  67. Sorg, A.; Bolch, T.; Stoffel, M.; Solomina, O.; Beniston, M. Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nat. Clim. Chang. 2012, 2, 725–731. [Google Scholar] [CrossRef]
  68. Hambrey, M.J.; Quincey, D.J.; Glasser, N.F.; Reynolds, J.M.; Richardson, S.J.; Clemmens, S. Sedimentological, geomorphological and dynamic context of debris-mantled glaciers, Mount Everest (Sagarmatha) region, Nepal. Quat. Sci. Rev. 2008, 27, 2361–2389. [Google Scholar] [CrossRef]
  69. Xie, F.; Liu, S.; Wu, K.; Zhu, Y.; Gao, Y.; Qi, M.; Duan, S.; Saifullah, M.; Tahir, A.A. Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990~2019. Front. Earth Sci. 2020, 8, 308. [Google Scholar] [CrossRef]
Remotesensing 15 00132 g001 550
Figure 1. Study area and basin boundaries [45].
Figure 1. Study area and basin boundaries [45].
Remotesensing 15 00132 g001
Remotesensing 15 00132 g002 550
Figure 2. Flowchart of the study.
Figure 2. Flowchart of the study.
Remotesensing 15 00132 g002
Remotesensing 15 00132 g003 550
Figure 3. The process of cloud filtering and image synthesis.
Figure 3. The process of cloud filtering and image synthesis.
Remotesensing 15 00132 g003
Remotesensing 15 00132 g004 550
Figure 4. Sample points for each period. The clean glacier, water body, and others are the first classification samples and the debris-covered glacier, snow, water body (2), and others (2) are the second.
Figure 4. Sample points for each period. The clean glacier, water body, and others are the first classification samples and the debris-covered glacier, snow, water body (2), and others (2) are the second.
Remotesensing 15 00132 g004
Remotesensing 15 00132 g005 550
Figure 5. Verification set results of glacier extraction from 1985 to 2020.
Figure 5. Verification set results of glacier extraction from 1985 to 2020.
Remotesensing 15 00132 g005
Remotesensing 15 00132 g006 550
Figure 6. Glacier changes on the Tibetan Plateau from 1985 to 2020. (a1), (b1), (c1) are the areas of total glaciers, clean glaciers, and debris-covered glaciers in each period and their change rates, (a2), (b2), (c2), are the area fitting curves of total glaciers, clean glaciers, and debris-covered glaciers, respectively.
Figure 6. Glacier changes on the Tibetan Plateau from 1985 to 2020. (a1), (b1), (c1) are the areas of total glaciers, clean glaciers, and debris-covered glaciers in each period and their change rates, (a2), (b2), (c2), are the area fitting curves of total glaciers, clean glaciers, and debris-covered glaciers, respectively.
Remotesensing 15 00132 g006
Remotesensing 15 00132 g007 550
Figure 7. Rate of glacier change in the basins of the Tibetan Plateau. Numbers outside and in parentheses are the rates of change for clean and debris-covered glaciers, respectively.
Figure 7. Rate of glacier change in the basins of the Tibetan Plateau. Numbers outside and in parentheses are the rates of change for clean and debris-covered glaciers, respectively.
Remotesensing 15 00132 g007
Remotesensing 15 00132 g008 550
Figure 8. Changes in glacier area at different elevations on the Tibetan Plateau. (a) Percentage of area and (b) average annual rate of change at different elevations.
Figure 8. Changes in glacier area at different elevations on the Tibetan Plateau. (a) Percentage of area and (b) average annual rate of change at different elevations.
Remotesensing 15 00132 g008
Remotesensing 15 00132 g009 550
Figure 9. Glacier changes on the Tibetan Plateau at different slopes. (a) Percentage of area and (b) average annual rate of change at different elevations.
Figure 9. Glacier changes on the Tibetan Plateau at different slopes. (a) Percentage of area and (b) average annual rate of change at different elevations.
Remotesensing 15 00132 g009
Remotesensing 15 00132 g010 550
Figure 10. Glacier changes on the Tibetan Plateau in different aspects. (a) Percentage of area (b) indicates the average annual retreat rate of clean glaciers in different slopes, and (c) indicates the average annual expansion rate of debris-covered glaciers in different slopes.
Figure 10. Glacier changes on the Tibetan Plateau in different aspects. (a) Percentage of area (b) indicates the average annual retreat rate of clean glaciers in different slopes, and (c) indicates the average annual expansion rate of debris-covered glaciers in different slopes.
Remotesensing 15 00132 g010
Remotesensing 15 00132 g011 550
Figure 11. Average annual temperature and precipitation on Tibetan Plateau from 1985 to 2020. (a) shows the annual average temperature and precipitation, and (b) shows the temperature in summer and precipitation in winter.
Figure 11. Average annual temperature and precipitation on Tibetan Plateau from 1985 to 2020. (a) shows the annual average temperature and precipitation, and (b) shows the temperature in summer and precipitation in winter.
Remotesensing 15 00132 g011
Table
Table 1. Results for time periods.
Table 1. Results for time periods.
Period of TimeImageNumber of ImagesEmpty Pixel Rate
1985–1993Landsat 543461.20%
1994–1996Landsat 525110.80%
1997–2001Landsat 536920.40%
2002–2004Landsat 724151.10%
2005–2006Landsat 721170.40%
2007–2009Landsat 728070.30%
2010–2012Landsat 731340.30%
2013–2014Landsat 828970.30%
2015–2016Landsat 830840.40%
2017–2018Landsat 830060.80%
2019–2020Landsat 830190.30%
Table
Table 2. Feature construction.
Table 2. Feature construction.
Features
Spectral featureB1~B7, B10, NDVI, NDWI, NDSI, band difference, greenness, brightness, humidity, multi-temporal minimum band ratio
Texture featureSecond moment, contrast, correlation, variance, inverse different moment, and entropy
Topographic featuresElevation, slope, aspect
Table
Table 3. Pearson correlation coefficients.
Table 3. Pearson correlation coefficients.
Pearson Correlation Coefficients
Annual average temperature−0.418571359
Average temperature in summer−0.772825141 **
Annual average precipitation0.01301384
Average precipitation in winter0.288317186
** At the level of 0.05 (bilateral), the correlation is significant.
Table
Table 4. Multiple linear regression results.
Table 4. Multiple linear regression results.
Clean GlacierDebris-Covered Glacier
Non-Standardized CoefficientsStandardized CoefficientsNon-Standardized CoefficientsStandardized Coefficients
Constant (C)122,679.427 −10,993.306
Average temperature in summer (T)−6394.734−0.4521905.3230.54
Average precipitation in winter (P)27.7210.03711.5190.061
Table
Table 5. Comparison of glacier extraction results between this study and existing studies.
Table 5. Comparison of glacier extraction results between this study and existing studies.
Period of TimeResults of This Study (km2)Existing Research Results (km2)GapData Source
1985–199382,814.881,820.441.2%[60]
1997–2001 *#38,577.47442,210 ± 16214.9%−11%[63]
2005–2006 *44,026.8443,588.161.0%[61]
2017–2018 *39,505.1439,049.071.16%[62]
* Glaciers in China, # Clean Glaciers.
Table
Table 6. Comparison of the change rate of clean glaciers between this study and existing studies. A: average annual rate of change for this study (% yr−1). B: average annual rate of change of studies available (% yr−1).
Table 6. Comparison of the change rate of clean glaciers between this study and existing studies. A: average annual rate of change for this study (% yr−1). B: average annual rate of change of studies available (% yr−1).
BasinCorresponding Time to Existing StudiesABSource
Salween1985–2014−0.62−0.36[63]
Mekong1985–2014−1.52−0.78[63]
Inner Plateau1985–2020−0.31−0.08 ± 0.25[64]
Indus1985–2014−0.47−0.31[63]
Hexi Corridor1985–2014−0.52−0.48[63]
Yellow1985–2013−0.24−0.24[63]
Ganges1985–2020−0.62−0.51[65]
Yangtze1985–2014−0.22−0.36[63]
Brahmaputra1985–2020−0.50−0.45[66]
Amu Darya1985–2018−0.99−0.59 ± 0.1[64]
Tarim1985–2020−0.17−0.2[66]
Qaidam1985–2014−0.69−0.54[63]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hu, M.; Zhou, G.; Lv, X.; Zhou, L.; Wang, X.; He, X.; Tian, Z. Warming Has Accelerated the Melting of Glaciers on the Tibetan Plateau, but the Debris-Covered Glaciers Are Rapidly Expanding. Remote Sens. 2023, 15, 132. https://doi.org/10.3390/rs15010132

AMA Style

Hu M, Zhou G, Lv X, Zhou L, Wang X, He X, Tian Z. Warming Has Accelerated the Melting of Glaciers on the Tibetan Plateau, but the Debris-Covered Glaciers Are Rapidly Expanding. Remote Sensing. 2023; 15(1):132. https://doi.org/10.3390/rs15010132

Chicago/Turabian Style

Hu, Mingcheng, Guangsheng Zhou, Xiaomin Lv, Li Zhou, Xiaoliang Wang, Xiaohui He, and Zhihui Tian. 2023. "Warming Has Accelerated the Melting of Glaciers on the Tibetan Plateau, but the Debris-Covered Glaciers Are Rapidly Expanding" Remote Sensing 15, no. 1: 132. https://doi.org/10.3390/rs15010132

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop