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作者投稿 专家审稿 编辑办公 编委办公 主编办公

冰川冻土 ›› 2022, Vol. 44 ›› Issue (5): 1665-1673.doi: 10.7522/j.issn.1000-0240.2022.0146

• 冰冻圈技术 • 上一篇    


杨佳1,2(), 薛莎莎1, 苏永恒1, 任庆福1   

  1. 1.航天宏图信息技术股份有限公司,北京 100195
    2.上海宏图空间网络科技有限公司,上海 201702
  • 收稿日期:2021-11-11 修回日期:2022-08-23 出版日期:2022-10-25 发布日期:2022-11-05
  • 作者简介:杨佳,工程师,主要从事地物参数遥感反演研究. E-mail: yangjia120625@163.com
  • 基金资助:

Analysis of superiority of the object-oriented-improved ice and snow index method to eliminate glacial lake interference and extract glacier boundaries: taking Geladandong Glacier as an example

Jia YANG1,2(), Shasha XUE1, Yongheng SU1, Qingfu REN1   

  1. 1.PIESAT International Information Technology Limited,Beijing 100195,China
    2.Shanghai Hongtu Space Network Technology Co. ,Ltd. ,Shanghai 201702,China
  • Received:2021-11-11 Revised:2022-08-23 Online:2022-10-25 Published:2022-11-05



关键词: 冰川, 遥感, 面向对象-改进冰雪指数法, 各拉丹冬


Glaciers as important indicators of climate change, the melting of glaciers will not only cause sea level rise, but also cause disasters such as ice avalanches and glacial lake break. Glacier extent monitoring is important to the regional ecological environment and human social life. Glacier extent monitoring based on remote sensing technology is widely used. However, in the conventional remote sensing monitoring method, there is a phenomenon that glaciers and glacial lakes are indistinguishable in the extraction results of the ice and snow index threshold method. The object-oriented classification method is limited by the spectral texture information of ground objects, and the occurrence of different types of land cover with the same spectrum’ phenomena will appear. In order to make up for the above shortcomings, in this paper, improved ice and snow index that can distinguish glacial lakes and glaciers is proposed, and it is integrated into the object-oriented classification method, and an object-oriented-improved ice and snow index method is constructed. Taking the Geladandong Glacier as the test area (the surface of the glacier in this area is clean), the extraction results of this method are compared with the results of the Qinghai-Tibet Plateau glacier data products and the results of conventional remote sensing monitoring methods. Validation of the object-oriented-improved ice and snow index method for effectiveness and robustness. The results demonstrate that: Ice and snow have strong reflection characteristics in the blue band and strong absorption in the near-infrared long-wave band. These two bands are sensitive bands for glacier identification. They not only have a high degree of recognition for glaciers, but also there are also obvious effects in distinguishing glaciers and glacial lakes. The band characteristics of the Landsat-8 OLI sensor were analyzed, and the glacier sensitive bands were Coastal band and SWIR1 band. Three improved ice and snow indices RSI, NDSI* and DSI are proposed for the glacier-sensitive band of Landsat-8 data. Taking the Geladandong Glacier as the test area, the glacier extent was extracted based on three improved ice and snow indices, and the extraction results were compared with the 2017 glacier data on the Qinghai-Tibet Plateau. The results show that the three ice and snow indices can extract the glacier boundary well and the extraction accuracy can reach more than 95%. In the recognition of confusing ground objects, the three indices can distinguish clouds and glaciers, but in the difference between glaciers and glacial lakes, the DSI ice and snow index is significantly better than others. The object-oriented-improved ice and snow index method was used to extract the extent of Geladandong Glacier, and the overall accuracy of the extraction results was as high as 97.26%. This method combines the advantages of the ice and snow index threshold extraction method and the object-oriented classification method to make up their respective shortcomings, and identify the glacier boundary more accurately. The object-oriented-improved ice and snow index method solves the problem of the same spectrum foreign matter in the process of glacier extraction to a certain extent, especially for the distinction between glacial lakes and glaciers. However, there are still some deficiencies. The original data of this study are images at the end of glacier ablation and with a high solar elevation angle. This study did not consider in detail the indistinguishability of snow covered in glacial and non-glacial areas and the difficulty of identifying glaciers in shaded areas. In the follow-up, in view of the above shortcomings, it is proposed to use multi-source remote sensing data (high resolution images, DEM, etc.), integrate multiple glacier characteristic indicators, and further improve the object-oriented-improved ice and snow index method, and strive to fundamentally solve the difficult problem of clean glacier classification.

Key words: glacier, remote sensing, object-oriented-improved ice and snow index method, Geladandong


  • P343.6