气候变化领域集成服务门户(CCPortal): Integration of optical, SAR and DEM data for automated detection of debris-covered glaciers over the western Nyainqentanglha using a random forest classifier

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DOI	10.1016/j.coldregions.2021.103421
	Integration of optical, SAR and DEM data for automated detection of debris-covered glaciers over the western Nyainqentanglha using a random forest classifier
	Lu, Yijie; Zhang, Zhen; Kong, Yuru; Hu, Kehong
通讯作者	Zhang, Z (通讯作者)，Anhui Univ Sci & Technol, Sch Spatial Informat & Geomat Engn, Huainan 232001, Peoples R China.
发表日期	2022
ISSN	0165-232X
EISSN	1872-7441
卷号	193
英文摘要	The capability of optical images to detect clean-ice glaciers has been well-demonstrated. Debris-covered glaciers, are different from clean-ice glaciers and are important for research of glacier mass balance. However, the main challenges to detect debris-covered glaciers in alpine regions faced by many researchers are the spectral similarity between debris-covered glaciers and the rocks and soils on both sides, as well as shadows cast from mountains, clouds and seasonal snow in satellite images. This study aimed to develop an automatic algorithm (using a Random Forest classifier model) implemented in the western Nyainqentanglha to map debris-covered glaciers based on multi-source datasets such as Sentinel-1 Synthetic Aperture Radar data, Sentinel-2 Multispectral Instrument data, Landsat-8 Thermal Infrared Sensor and Digital Elevation Models. All data was split into training (70%) and testing datasets (30%). The main strength of this study is that our method overcomes most of the above-mentioned challenges and the great accuracy (Kappa coefficient: 0.96, overall accuracy: 97.21%) of the Random Forest classifier model represents a comprehensive success in identifying debris-covered glaciers, illustrating that if this method can be executed efficiently, it will bring benefits for glacier inventory management. Additionally, an analysis of the spatial characteristic of the mountain glaciers showed that the glacier area, elevation and slope of the Nyainqentanglha Mountains were closely related. Moreover, most glacier movement occurred in glaciers with an area of less than 1 km2 and greater than 10 km2, which had mean velocities of 0.141 m/day and 0.168 m/day, respectively, and glacier movement explained the uncertainty of glacier facies mapping.
关键词	SATELLITE DATA INVENTORY BASIN DELINEATION ALPS AREA
英文关键词	Coherence coefficient; Random Forest; Debris-covered glaciers; Automatic glacier mapping; Western Nyainqentanglha
语种	英语
WOS研究方向	Engineering ; Geology
WOS类目	Engineering, Environmental ; Engineering, Civil ; Geosciences, Multidisciplinary
WOS记录号	WOS:000728382300004
来源期刊	COLD REGIONS SCIENCE AND TECHNOLOGY
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254557
作者单位	[Lu, Yijie; Zhang, Zhen; Hu, Kehong] Anhui Univ Sci & Technol, Sch Spatial Informat & Geomat Engn, Huainan 232001, Peoples R China; [Lu, Yijie; Zhang, Zhen] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Cryospher Sci, Lanzhou 730000, Peoples R China; [Kong, Yuru] Jiangsu Normal Univ, Sch Geog Geomat & Planning, Xuzhou 221116, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Lu, Yijie,Zhang, Zhen,Kong, Yuru,et al. Integration of optical, SAR and DEM data for automated detection of debris-covered glaciers over the western Nyainqentanglha using a random forest classifier[J]. 中国科学院西北生态环境资源研究院,2022,193.
APA	Lu, Yijie,Zhang, Zhen,Kong, Yuru,&Hu, Kehong.(2022).Integration of optical, SAR and DEM data for automated detection of debris-covered glaciers over the western Nyainqentanglha using a random forest classifier.COLD REGIONS SCIENCE AND TECHNOLOGY,193.
MLA	Lu, Yijie,et al."Integration of optical, SAR and DEM data for automated detection of debris-covered glaciers over the western Nyainqentanglha using a random forest classifier".COLD REGIONS SCIENCE AND TECHNOLOGY 193(2022).