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1.兰州交通大学测绘与地理信息学院,甘肃 兰州,730070
2.中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程全国重点实验室,甘肃 兰州,730000
3.中国科学院大学,北京,100049
4.地理国情监测技术应用国家地方联合工程研究中心,甘肃 兰州,730070
5.甘肃省地理国情监测工程实验室,甘肃 兰州,730070
Received:28 May 2024,
Published:05 December 2025
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周卫永,许民,康世昌,等.天山科其喀尔冰川表碛识别及其表面流速特征[J].武汉大学学报(信息科学版),2025,50(12):2537-2547.
ZHOU Weiyong,XU Min,KANG Shichang,et al.Identification of Debris-Covered Koxkar Glacier in Mt. Tianshan and Its Velocity Characteristics[J].Geomatics and Information Science of Wuhan University,2025,50(12):2537-2547.
周卫永,许民,康世昌,等.天山科其喀尔冰川表碛识别及其表面流速特征[J].武汉大学学报(信息科学版),2025,50(12):2537-2547. DOI:10.13203/j.whugis20220656
ZHOU Weiyong,XU Min,KANG Shichang,et al.Identification of Debris-Covered Koxkar Glacier in Mt. Tianshan and Its Velocity Characteristics[J].Geomatics and Information Science of Wuhan University,2025,50(12):2537-2547. DOI:10.13203/j.whugis20220656
表碛覆盖型冰川有其独特的物质平衡特征,冰川流速是评估物质平衡的重要指标。针对遥感方法难以区分冰川表碛和岩石的问题,利用结合纹理和地形因素的特征优选随机森林算法识别了天山科其喀尔冰川的表碛范围,比较于其他机器学习方法,识别精度有显著提升。基于表碛识别结果和2017—2021年哨兵2号影像数据,采用特征匹配及互相关计算方法对表碛覆盖冰川流速进行了估算。表碛识别结果显示,表碛覆盖面积约24.6 km
2
,占整个科其喀尔冰川面积的31.7%。流速估算结果表明,科其喀尔冰川年均最大流速位于东支海拔4 380 m左右位置,在2020年达到约145.9 m/a。随海拔降低,冰川末端流速递减至0~10 m/a。对研究区1989—2021年的流速变化进行长时序分析发现,在冰川中上部,冰川表面流速随气候变暖而持续加快;在冰川下部海拔3 090~3 500 m的区间内,由于冰碛积累和冰舌的不断减薄,冰川表面流速持续降低,冰川物质平衡处于亏损状态。
Objectives
2
The spectral characteristics of supraglacial debris are extremely similar to those of rocks
which is difficult to retrieve by semi-automatic or automatic interpretation of remote sensing. Obtaining the extent of supraglacial debris and glacial surface flow velocity can help to understand the characteristics of glacier mass balance about debris-covered glaciers.
Methods
2
Random forest method based on feature optimization is used to identify the supraglacial debris of Koxkar Glacier. To increase the distinguish ability between supraglacial debris and rocks
Sentinel-2 images
remote sensing indexes
terrain features and texture features are also added for image classification. Glacial flow velocity is estimated using coregistration of optical sensed images and correlation (COSI-Corr) method
which is considered to be one of the most effective methods for small-scale and high-precision estimation of glacial flow velocity.
Results
2
Compared with other machine learning classification methods
random forest method based on feature optimization can effectively avoid the misclassification between supraglacial debris and rock. The result of identification shows that the area of debris is about 24.6 km
2
accounting for 31.7% of the total area of Koxkar Glacier. The results of glacial flow velocity show that the maximum average annual flow velocity occurs at about 4 380 m above sea level in the eastern branch and can reach 145.9 m/a in 2020. The flow velocity reduces to 0-10 m/a with the decreasing elevation. The flow velocity during the ablation period is much higher than the annual mean velocity. The differences of velocity on the glacier tongue are induced by differences of altitude
ice lake outburst
collapse of subglacial meltwater channel and other factors. The changes of flow velocity in the study area from 1989 to 2021 show that the flow velocity in the upper and middle parts of the glacier continues to accelerate with the increase of surface temperature. In the range from 3 090 m to 3 500 m above sea level located in the lower part of the glacier
the flow velocity decreases continuously due to the accumulation of debris and the continuous thinning of glacier tongue.
Conclusions
2
Compared with other machine learning classification methods
the accuracy of the random forest method based on feature optimization can be significantly improved in identification of supraglacial debris. In the past 30 years
the overall flow velocity of debris-covered Koxkar Glacier is slowly increasing with obviously spatiotemporal differences
and the glacier mass balance of the glacier below 3 500 m above sea level is in a continuous deficit state.
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