干旱区研究

Arid Zone Research >

2025 , Vol. 42 >Issue 6: 1032 - 1042

DOI: https://doi.org/10.13866/j.azr.2025.06.07

Land and Water Resources

Object-based glacier boundary extraction utilizing multi-feature fusion

  • LIN Zhouyan ,
  • WANG Xiaying ,
  • XIA Yuanping
Expand
  • 1. School of Geomatics and Spatial Information Engineering, East China University of Technology, Nanchang 330013, Jiangxi, China
    2. Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake, Ministry of Natural Resources, East China University of Technology, Nanchang 330013, Jiangxi, China
    3. Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang, 330013, Jiangxi, China
    4. Nanchang Key Laboratory of Landscape Process and Territorial Spatial Ecological Restoration, East China University of Technology, Nanchang 330013, Jiangxi, China

Received date: 2024-12-19

  Revised date: 2025-02-13

  Online published: 2025-06-11

Fold

Abstract

Pixel-based classification struggles with the accurate identification of glacier changes in areas with similar spectral characteristics, particularly in debris-covered areas where spectral features closely resemble the surrounding mountains and rocks, thereby resulting in low extraction accuracy. This study investigates the Yinsugaiti and Yalong Glaciers using Google Earth Engine to integrate spectral indices, microwave texture features, and topographic data. An object-based (OB) machine learning algorithm is applied for automated glacier extraction and compared to pixel-based (PB) classification methods. The results show the following. (1) The OB classification approach, integrating multi-feature fusion, significantly improved the glacier extraction accuracy. The OB_RF classifier achieved an overall accuracy of 98.1%, a Kappa coefficient of 0.97, and an F1-score of 98.67%, outperforming the OB_CART and OB_GTB classifiers. When compared to PB_RF, the overall accuracy, Kappa coefficient, and F1-score increased by 1.7%, 0.024, and 5.57%, respectively. (2) Between 2001-2022, the Yinsugaiti and Yalong Glaciers retreated at average annual rates of 0.08% and 0.13%, respectively. (3) Supraglacial debris was primarily distributed below 5000 and 4800 m on the Yinsugaiti and Yalong Glacier, respectively. Over the same period, debris-covered areas on both glaciers expanded upward.

Key words： glacier boundary extraction; object-based; pixel-based; machine learning; multi-feature fusion

Cite this article

LIN Zhouyan , WANG Xiaying , XIA Yuanping . Object-based glacier boundary extraction utilizing multi-feature fusion[J]. Arid Zone Research, 2025 , 42(6) : 1032 -1042 . DOI: 10.13866/j.azr.2025.06.07

References

[1] Cogley J G, Hock R, Rasmussen L A, et al. Glossary of glacier mass balance and related terms[J]. Arctic Antarctic and Alpine Research, 2012, 44(2): 256-258.
[2] Hewitt K. Rock avalanches that travel onto glaciers and related developments, Karakoram Himalaya, Inner Asia[J]. Geomorphology, 2009, 103(1): 66-79.
[3] 张勇, 刘时银, 王欣. 青藏高原及周边冰川区表碛影响研究进展[J]. 冰川冻土, 2022, 44(3): 900-913.
  [Zhang Yong, Liu Shiyin, Wang Xin. Debris-cover effect in the Tibetan Plateau and surroundings: A review[J]. Journal of Glaciology and Geocryology, 2022, 44(3): 900-913.]
[4] Cauvy-Fraunié S, Andino P, Espinosa R, et al. Ecological responses to experimental glacier-runoff reduction in alpine rivers[J]. Nature Communications, 2016, 7(1): 12025.
[5] Millan R, Mouginot J, Rabatel A, et al. Ice velocity and thickness of the world’s glaciers[J]. Nature Geoscience, 2022, 15(2): 124-129.
[6] Zemp M, Huss M, Thibert E, et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016[J]. Nature, 2019, 568(7752): 382-386.
[7] Hall D K, Riggs G A, Salomonson V V. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data[J]. Remote Sensing of Environment, 1995, 54(2): 127-140.
[8] 陈逸青, 段克勤, 石培宏, 等. 1990—2020年帕米尔高原无表碛覆盖冰川变化态势研究——基于Google Earth Engine平台[J]. 冰川冻土, 2024, 46(3): 742-753.
  [Chen Yiqing, Duan Keqin, Shi Peihong, et al. Change trend of debris-free glaciers in the Pamir based on Google Earth Engine from 1990 to 2020[J]. Journal of Glaciology and Geocryology, 2024, 46(3): 742-753.]
[9] 黄晓然, 包安明, 郭浩, 等. 近20 a中国天山东段典型冰川变化及其气候响应[J]. 干旱区研究, 2017, 34(4): 870-880.
  [Huang Xiaoran, Bao Anming, Guo Hao, et al. Change of typical glaciers and its response to meteorological factors in the Eastern Tianshan Mountains in China in recent 20 years[J]. Arid Zone Research, 2017, 34(4): 870-880.]
[10] Zhang M, Wang X H, Shi C L, et al. Automated glacier extraction index by optimization of Red/SWIR and NIR/SWIR ratio index for glacier mapping using Landsat imagery[J]. Water, 2019, 11(6): 1223.
[11] 都伟冰, 李均力, 包安明, 等. 高山冰川多时相多角度遥感信息提取方法[J]. 测绘学报, 2015, 44(1): 59-66.
  [Du Weibing, Li Junli, Bao Anming, et al. Information extraction method of alpine glaciers with multitemporal and multiangle remote sensing[J]. Acta Geodactica et Cartographica Sinica, 2015, 44(1): 59-66.]
[12] Huang L, Li Z, Tian B S, et al. Classification and snow line detection for glacial areas using the polarimetric SAR image[J]. Remote Sensing of Environment, 2011, 115(7): 1721-1732.
[13] Walter V. Object-based classification of remote sensing data for change detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 58(3-4): 225-238.
[14] 怀保娟, 李忠勤, 孙美平, 等. 多种遥感分类方法提取冰川边界探讨——以喀纳斯河源地区为例[J]. 干旱区研究, 2013, 30(2): 372-377.
  [Huai Baojuan, Li Zhongqin, Sun Meiping, et al. Discussion on RS methods for glacier outline detection——A case study in headwaters of the Kanas River[J]. Arid Zone Research, 2013, 30(2): 372-377.]
[15] Shukla A, Arora M K, Gupta R P. Synergistic approach for mapping debris-covered glaciers using optical-thermal remote sensing data with inputs from geomorphometric parameters[J]. Remote Sensing of Environment, 2010, 114(7): 1378-1387.
[16] 雷赛月, 方立, 李辰德, 等. 改进光学卫星图像中表碛覆盖型冰川区域提取算法[J]. 计算机工程, 2025, 2(51): 269-277.
  [Lei Saiyue, Fang Li, Li Chende, et al. Improving the algorithm for extracting debris-covered glaciers in optical satellite images[J]. Computer Engineering, 2025, 2(51): 269-277.]
[17] 薛娇, 姚晓军, 张聪, 等. 表碛覆盖型冰川的提取方法及变化[J]. 冰川冻土, 2022, 44(5): 1653-1664.
  [Xue Jiao, Yao Xiaojun, Zhang Cong, et al. Extraction method and change of debris-covered glaciers[J]. Journal of Glaciology and Geocryology, 2022, 44(5): 1653-1664.]
[18] Holobac? I-H, Tielidze L G, Ivan K, et al. Multi-sensor remote sensing to map glacier debris cover in the Greater Caucasus, Georgia[J]. Journal of Glaciology, 2021, 67(264): 685-696.
[19] 周建民, 李震, 邢强. 基于雷达干涉失相干特性提取冰川边界方法研究[J]. 冰川冻土, 2010, 32(1): 133-138.
  [Zhou Jianmin, Li Zhen, Xing Qiang. Deriving glacier border information based on analysis of decorrelation in SAR interferometry[J]. Journal of Glaciology and Geocryology, 2010, 32(1): 133-138.]
[20] 李若楠, 李均力, 李爽爽, 等. 基于Sentinel-2的依连哈比尔尕冰川变化监测[J]. 干旱区研究, 2024, 41(6): 940-950.
  [Li Ruonan, Li Junli, Li Shuangshuang, et al. Monitoring the glacier changes in Yilian Habirga Mountain using Sentinel-2 data[J]. Arid Zone Research, 2024, 41(6): 940-950.]
[21] Yousuf B, Shukla A, Arora M K, et al. On drivers of subpixel classification accuracy—An example from glacier facies[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13(1): 601-608.
[22] Aftab A K, Akhtar J, Dostdar H, et al. Machine-learning algorithms for mapping debris-covered glaciers: The Hunza Basin case study[J]. IEEE Access, 2020, 8(1): 12725-12734.
[23] Xie Z Y, Asari V K, Haritashya U K. Evaluating deep-learning models for debris-covered glacier mapping[J]. Applied Computing and Geosciences, 2021, 12(1): 100071.
[24] Yang S J, Wang F T, Xie Y D, et al. Delineation evaluation and variation of debris-covered glaciers based on the multi-source remote sensing images, Take glaciers in the eastern Tomur Peak region for example[J]. Remote Sensing, 2023, 15(10): 2575.
[25] Racoviteanu A, Williams M W. Decision tree and texture analysis for mapping debris-covered glaciers in the Kangchenjunga area, Eastern Himalaya[J]. Remote Sensing, 2012, 4(10): 3078-3109.
[26] Zhao Q, Yu L, Li X C, et al. Progress and trends in the application of Google Earth and Google Earth Engine[J]. Remote Sensing, 2021, 13(18): 3778.
[27] Amani M, Mahdavi S, Afshar M, et al. Canadian wetland inventory using Google Earth Engine: The first map and preliminary results[J]. Remote Sensing, 2019, 11(7): 842.
[28] 喻彩丽, 陆健强, 窦旭峰, 等. 基于GEE的多源遥感影像青梅种植信息提取[J]. 江苏农业学报, 2024, 40(8): 1455-1463.
  [Yu Caili, Lu Jianqiang, Dou Xufeng, et al. Extraction of greengage planting information from multi-source remote sensing images based on GEE[J]. Jiangsu Journal of Agricultural Sciences, 2024, 40(8): 1455-1463.]
[29] 马东岭, 刘卫星, 张春红. 基于谷歌地球引擎和分形理论的黄河三角洲土地利用空间结构研究[J]. 土壤, 2024, 56(3): 655-665.
  [Ma Dongling, Liu Weixing, Zhang Chunhong. Study on land use spatial structure based on Google Earth Engine and fractal theory: A case study of ecological function reserve in Yellow River Delta[J]. Soils, 2024, 56(3): 655-665.]
[30] Bevington A R, Menounos B. Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery[J]. Remote Sensing of Environment, 2022, 270(1): 112862.
[31] Hu M C, Zhou G S, Lv X M, et al. A new automatic extraction method for glaciers on the Tibetan Plateau under clouds, shadows and snow cover[J]. Remote Sensing, 2022, 14(13): 3084.
[32] Huang L, Li Z, Zhou J M, et al. An automatic method for clean glacier and nonseasonal snow area change estimation in High Mountain Asia from 1990 to 2018[J]. Remote Sensing of Environment, 2021, 258(1): 112376.
[33] Ali A, Dunlop P, Coleman S, et al. Glacier area changes in Novaya Zemlya from 1986-89 to 2019-21 using object-based image analysis in Google Earth Engine[J]. Journal of Glaciology, 2023, 69(277): 1305-1316.
[34] 刘时银, 姚晓军, 郭万钦, 等. 基于第二次冰川编目的中国冰川现状[J]. 地理学报, 2015, 70(1): 3-16.
  [Liu Shiyin, Yao Xiaojun, Guo Wanqin, et al. The contemporary glaciers in China based on the Second Chinese Glacier Inventory[J]. Acta Geographica Sinica, 2015, 70(1): 3-16.]
[35] Achanta R, Susstrunk S. Superpixels and polygons using simple non-iterative clustering[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Computer Society, 2017: 4651-4660.
[36] 裴欢, 孙天娇, 王晓妍. 基于 Landsat 8 OLI 影像纹理特征的面向对象土地利用/覆盖分类[J]. 农业工程学报, 2018, 34(2): 248-255.
  [Pei Huan, Sun Tianjiao, Wang Xiaoyan. Object-oriented land use/cover classification based on texture features of Landsat 8 OLI image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(2): 248-255.]
[37] Paul F, Huggel C, K??b A. Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers[J]. Remote Sensing of Environment, 2004, 89(4): 510-518.
[38] ?strem G. Ice melting under a thin layer of moraine, and the existence of ice cores in moraine ridges[J]. Geografiska Annaler, 1959, 41(4): 228-230.
[39] Zhuang L C, Ke C Q, Cai Y, et al. Measuring glacier changes in the Tianshan Mountains over the past 20 years using Google Earth Engine and machine learning[J]. Journal of Geographical Sciences, 2023, 33(9): 1939-1964.
[40] 许艾文. 近40年中国喀喇昆仑山冰川变化的遥感监测[D]. 兰州: 兰州大学, 2017.
  [Xu Aiwen. Monitoring Glacier Change Based on Remote Sensing in China Karakoram for the Last Four Decades[D]. Lanzhou: Lanzhou University, 2017.]
[41] 赵晋彪. 藏东南地区冰川变化与运动特征研究[D]. 安徽: 安徽理工大学, 2024.
  [Zhao Jinbiao. Study on Characteristics of Glacier Change and Movement in Southeast Xizang[D]. Anhui: Anhui University of Science and Technology, 2024.]
Options
Outlines

/

〈 〉