Monitoring of Glacier Area Changes in the Ili River Basin during 1992–2020 Based on Google Earth Engine

The Ili River Basin, a crucial transboundary river in the arid region of Central Asia, plays a significant role in the region’s ecology and water resources. However, current methods for monitoring glacier area changes in this region face challenges in automation and accuracy due to the complex terrain and climatic conditions. This study aims to evaluate the effectiveness of the Google Earth Engine (GEE) platform for monitoring glacier area changes in the Ili River Basin from 1992 to 2020, with a focus on improving data accuracy and processing efficiency. Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. Our results demonstrated that the GEE platform, combined with the RF algorithm, provided high accuracy in glacier monitoring, achieving an overall accuracy of 89% and a kappa coefficient of 0.85. During the study period, the glacier area in the Ili River Basin decreased by 184.76 km2, with an average annual retreat rate of 6.84 km2, most notably between 3800 and 4400 m in elevation. The analysis revealed that temperature changes had a more pronounced impact on glacier dynamics than precipitation. This approach significantly enhances image utilization efficiency and data processing speed, offering a reliable tool for monitoring glacier dynamics. Future research should focus on integrating additional environmental variables and extending the temporal scope to further refine glacier dynamics modeling and predictions.

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Publication Details

Journal
Land
Published
2024-09-03
DOI
https://doi.org/10.3390/land13091417
Citations
3
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
1.13

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article

Monitoring of Glacier Area Changes in the Ili River Basin during 1992–2020 Based on Google Earth Engine

Zhonglin Xu, Yao Wang, Qinqin Zhang, Zihui Zhang et al.
3 citations
Land
Cryospheric studies and observations
1.13
article

Monitoring of Glacier Area Changes in the Ili River Basin during 1992–2020 Based on Google Earth Engine

Zhonglin Xu, Yao Wang, Qinqin Zhang, Zihui Zhang, Xiaofei Wang
article en
3 citations

Abstract

The Ili River Basin, a crucial transboundary river in the arid region of Central Asia, plays a significant role in the region’s ecology and water resources. However, current methods for monitoring glacier area changes in this region face challenges in automation and accuracy due to the complex terrain and climatic conditions. This study aims to evaluate the effectiveness of the Google Earth Engine (GEE) platform for monitoring glacier area changes in the Ili River Basin from 1992 to 2020, with a focus on improving data accuracy and processing efficiency. Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. Our results demonstrated that the GEE platform, combined with the RF algorithm, provided high accuracy in glacier monitoring, achieving an overall accuracy of 89% and a kappa coefficient of 0.85. During the study period, the glacier area in the Ili River Basin decreased by 184.76 km2, with an average annual retreat rate of 6.84 km2, most notably between 3800 and 4400 m in elevation. The analysis revealed that temperature changes had a more pronounced impact on glacier dynamics than precipitation. This approach significantly enhances image utilization efficiency and data processing speed, offering a reliable tool for monitoring glacier dynamics. Future research should focus on integrating additional environmental variables and extending the temporal scope to further refine glacier dynamics modeling and predictions.

LandVol. 13(9)
China Meteorological Administration (CN), Xinjiang University (CN)
Natural Science Foundation of Xinjiang
Clean water and sanitation
Openalex Percentile: Top 25%
Cryospheric studies and observations
1.13
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