气候变化领域集成服务门户(CCPortal): Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

CCPortal

DOI	10.3390/rs13132595
	Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers
	Lu, Yijie; Zhang, Zhen; Shangguan, Donghui; Yang, Junhua
通讯作者	Zhang, Z (通讯作者)，Anhui Univ Sci & Technol, Sch Geomat, Huainan 232001, Peoples R China. ; Zhang, Z (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Cryospher Sci, Lanzhou 730000, Peoples R China.
发表日期	2021
EISSN	2072-4292
卷号	13 期号:13
英文摘要	Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.
关键词	CONVOLUTIONAL NEURAL-NETWORK RANDOM FOREST TIBETAN PLATEAU EASTERN PAMIR CLASSIFICATION BASIN IMAGES MODEL INVENTORY ACCURACY
英文关键词	random forest; convolutional neural network; debris-covered glacier; Eastern Pamir; Nyainqentanglha; glacier mapping
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000671296600001
来源期刊	REMOTE SENSING
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/254372
作者单位	[Lu, Yijie; Zhang, Zhen] Anhui Univ Sci & Technol, Sch Geomat, Huainan 232001, Peoples R China; [Lu, Yijie; Zhang, Zhen; Shangguan, Donghui; Yang, Junhua] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, State Key Lab Cryospher Sci, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Lu, Yijie,Zhang, Zhen,Shangguan, Donghui,et al. Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers[J]. 中国科学院西北生态环境资源研究院,2021,13(13).
APA	Lu, Yijie,Zhang, Zhen,Shangguan, Donghui,&Yang, Junhua.(2021).Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers.REMOTE SENSING,13(13).
MLA	Lu, Yijie,et al."Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers".REMOTE SENSING 13.13(2021).