干旱区生态环境知识资源中心(Arid): Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia

Arid

DOI	10.1016/j.asr.2019.11.027
	Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia
	Shi, Lamei 1,2,3; Zhang, Jiahua 1,2,3; Zhang, Da 1,2,3; Igbawua, Tertsea 2,4; Liu, Yuqin 5
通讯作者	Zhang, Jiahua
来源期刊	ADVANCES IN SPACE RESEARCH
ISSN	0273-1177
EISSN	1879-1948
出版年	2020
卷号	65 期号:4 页码:1263-1278
英文摘要	Enhancing the dust storm detection is a key part for the environmental protection, human healthy and economic development. The goal of this paper is to propose a new Support Vector Machine (SVM)-based method to automatically detect dust storms using remote sensing data. Existing methods dealing with this problem are usually threshold-based that are of great complexity and uncertainty. In this paper we propose a simple and reliable method combining SVM with MODIS L1 data and explore the optimal band combinations used as the feature vectors of SVM. The developed method was evaluated by MODIS and OMI data qualitatively and quantitatively on three study sites located in the Arabian Desert, Gobi Desert and Taklimakan Desert, and it was also compared to three other traditional methods based on their accuracy, complexity, reliability and sensitivity to thresholds. The detection results demonstrated that the combination of (Band7 - Band3)/(Band7 + Band3) ((B7 - B3)/(B7 + B3)), Band20 - Band31 (B20 - B31), and Band31/Band32 (B31/B32) can detect the dust storms more precisely than other individual bands or their combination. The comparison among those cases indicated that the proposed automatic method exhibited an advantage of minimizing the uncertainty and complexity, which were the limits of defining thresholds based on the threshold-based methods. The conclusions can provide references for studies that focus on statistical-based dust storm detection. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.
英文关键词	Dust detection Support vector machine (SVM) Threshold-based method Moderate resolution imaging spectroradiometer (MODIS) Cluster analysis
类型	Article
语种	英语
国家	Peoples R China ; Nigeria
收录类别	SCI-E
WOS记录号	WOS:000515210700011
WOS关键词	MINERAL DUST ; VERTICAL-DISTRIBUTION ; AEROSOL PROPERTIES ; MIDDLE-EAST ; MODIS ; TRANSPORT ; DESERT ; CLOUDS ; DISCRIMINATION ; IDENTIFICATION
WOS类目	Engineering, Aerospace ; Astronomy & Astrophysics ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS研究方向	Engineering ; Astronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/313902
作者单位	1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China; 2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; 3.Univ Chinese Acad Sci, Beijing 101407, Peoples R China; 4.Univ Agr Makurdi, PMB 2373, Markurdi, Benue State, Nigeria; 5.Chinese Acad Sci, Inst Urban Environm, Key Lab Urban Environm & Hlth, Xiamen 361021, Peoples R China
推荐引用方式 GB/T 7714	Shi, Lamei,Zhang, Jiahua,Zhang, Da,et al. Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia[J],2020,65(4):1263-1278.
APA	Shi, Lamei,Zhang, Jiahua,Zhang, Da,Igbawua, Tertsea,&Liu, Yuqin.(2020).Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia.ADVANCES IN SPACE RESEARCH,65(4),1263-1278.
MLA	Shi, Lamei,et al."Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia".ADVANCES IN SPACE RESEARCH 65.4(2020):1263-1278.