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

Author:

Zhang Qinqin12345,Zhang Zihui1234,Wang Xiaofei1234,Xu Zhonglin1234,Wang Yao12346

Affiliation:

1. College of Ecology and Environment, Xinjiang University, Urumqi 830017, China

2. Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830017, China

3. Xinjiang Jinghe Observation and Research Station of Temperate Desert Ecosystem, Ministry of Education, Urumqi 830017, China

4. Technology Innovation Center for Ecological Monitoring and Restoration of Desert-Oasis, Ministry of Natural Resource, Urumqi 830001, China

5. Xinjiang Academy of Surveying and Mapping, Urumqi 830002, China

6. Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China

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.

Funder

Natural Science Foundation of Xinjiang Uygur Autonomous Region

Publisher

MDPI AG

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