Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir

Lu, Yijie; Zhang, Zhen; Huang, Danni

doi:10.3390/w12113231

Cited by 21 publications

(20 citation statements)

References 73 publications

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“…a −1 in debris-covered glaciers was connected with glacial lakes, which may suggest a different evolution of debris-covered glaciers in the monsoon-influenced region. Accordingly, we tend to conclude that the increase in the debris-covered glacier area was due to geomorphic activities (see also [95]), although the evolution of debris-covered glaciers requires further investigations.…”

Section: Comparison With Previous Studiesmentioning

confidence: 90%

Interannual and Seasonal Variability of Glacier Surface Velocity in the Parlung Zangbo Basin, Tibetan Plateau

Zhang

Menenti

et al. 2020

Remote Sensing

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Monitoring glacier flow is vital to understand the response of mountain glaciers to environmental forcing in the context of global climate change. Seasonal and interannual variability of surface velocity in the temperate glaciers of the Parlung Zangbo Basin (PZB) has attracted significant attention. Detailed patterns in glacier surface velocity and its seasonal variability in the PZB are still uncertain, however. We utilized Landsat-8 (L8) OLI data to investigate in detail the variability of glacier velocity in the PZB by applying the normalized image cross-correlation method. On the basis of satellite images acquired from 2013 to 2020, we present a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The analysis of meteorological data at two weather stations on the outskirts of the glacier area provided some indications of increased precipitation during winter-spring. Such increase likely had an impact on ice mass accumulation in the up-stream portion of the glacier. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. Besides, there was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue. The seasonal and interannual velocity variability was captured by the transverse velocity profiles in the four selected glaciers. The observed spatial pattern and seasonal variability in glacier surface velocity suggests that the winter-spring snow might be a driver of glacier flow in the central and upper portions of glaciers. Furthermore, the variations in glacier surface velocity are likely related to topographic setting and basal slip caused by the percolation of rainfall. The findings on glacier velocity suggest that the transfer of winter-spring accumulated ice triggered by mass conservation seems to be the main driver of changes in glacier velocity. The reasons that influence the seasonal surface velocity change need further investigation.

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Section: Comparison With Previous Studiesmentioning

confidence: 90%

Interannual and Seasonal Variability of Glacier Surface Velocity in the Parlung Zangbo Basin, Tibetan Plateau

Zhang

Menenti

et al. 2020

Remote Sensing

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“…Most glacier inventory datasets include glacier boundaries, which are important for studying the spatial distribution of global or regional glaciers. Further, glacier classification is a crucial step in glacier inventory [11]. The parameters from the glacier inventory help inform fields related to the national economy and people's livelihoods, and they include information regarding water resource utilization, climate change research, and ecological environment construction [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…A technique to effectively measure and monitor mountain glaciers is crucial at this stage [16][17][18]. However, in high mountain areas, which experience frequent cloud activity and have a large amount of debris coverage, automatic and semi-automatic glacier mapping based on remote sensing images is challenging [1,11,20], and short-term studies such as these do not necessarily show stable changes over time. Debris-covered glaciers are a special type of glacier [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [25] used multi-temporal data based on the RF method to realize the automatic classification of the borders of non-debris-covered glaciers and debris-covered glaciers. Lu et al [11] added the characteristics of glacier movement based on the aforementioned study [25] and optimized the RF algorithm to extract debris-covered glaciers. Hussain et al [50] compared three machine learning algorithmssupport vector machines, artificial neural networks, and random forests-to identify debriscovered glaciers; the results showed that the RF classification accuracy was the highest.…”

Section: Introductionmentioning

confidence: 99%

“…Although the RF solves the classification problem of the linear fractionable data set, its solutions are infinite, which leads to a lack of generalization ability in the model [49]. Further, the increase in the size of the pixel-based RF classifier can improve the generalization ability and stability of the model, while also increasing the required storage capacity, resulting in a significant decrease in classification prediction speed [11,49], especially for high-resolution remote sensing image classification.…”

Section: Introductionmentioning

confidence: 99%

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Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

Zhang

Shangguan

et al. 2021

Remote Sensing

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Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.

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The response of glaciers and glacial lakes to climate change in the Southeastern Tibetan Plateau over the past three decades

Dou,

Fan,

Wang

et al. 2023

Land Degrad Dev

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With global warming, changes in glaciers and glacial lakes on the Tibetan Plateau call for a serious inspection. The Southeastern Tibetan Plateau (SETP) is a typical monsoonal marine glacier area that is extremely vulnerable to climate change. It is affected by both humid and warm Indian Ocean currents. Outlines of glaciers and glacial lakes in SETP have been extracted from Landsat satellite images with an automatic extraction method based on image compositing with Google Earth Engine and Random Forest algorithm. Alongside the outlines, meteorological data have been collected from 1990 to 2021. The results show that since 1990 the glacier area of SETP has retreated by about 2165.33 km2 (~25.28%), the glacial lake area has increased by about 36.41 km2 (~21.45%), and their number has increased by 477 (~27.32%). The reliability and scientific validity of this study is proven by comparison with existing glacier inventories. By analyzing the meteorological data, the inverse correlation between the glacier area and temperature has been found. Despite decreases in cumulative precipitation, the glacial lake level rise and consequent area expansion have been observed. A fitting equation for the response pattern of glacier changes to temperature and precipitation change is presented and will provide a basis for future studies.

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Glacier Mapping Based on Random Forest Algorithm: A Case Study over the Eastern Pamir

Cited by 21 publications

References 73 publications

Interannual and Seasonal Variability of Glacier Surface Velocity in the Parlung Zangbo Basin, Tibetan Plateau

Interannual and Seasonal Variability of Glacier Surface Velocity in the Parlung Zangbo Basin, Tibetan Plateau

Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

The response of glaciers and glacial lakes to climate change in the Southeastern Tibetan Plateau over the past three decades

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