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

Qinqin Zhang, Zihui Zhang, Xiaofei Wang, Zhonglin Xu () and Yao Wang ()
Additional contact information
Qinqin Zhang: College of Ecology and Environment, Xinjiang University, Urumqi 830017, China
Zihui Zhang: College of Ecology and Environment, Xinjiang University, Urumqi 830017, China
Xiaofei Wang: College of Ecology and Environment, Xinjiang University, Urumqi 830017, China
Zhonglin Xu: College of Ecology and Environment, Xinjiang University, Urumqi 830017, China
Yao Wang: College of Ecology and Environment, Xinjiang University, Urumqi 830017, China

Land, 2024, vol. 13, issue 9, 1-17

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 km 2 , with an average annual retreat rate of 6.84 km 2 , 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.

Keywords: Google Earth Engine; glacier area; Ili River Basin; random forest algorithm; feature optimization (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/9/1417/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/9/1417/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:9:p:1417-:d:1470176

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().