干旱区生态环境知识资源中心(Arid): Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa Using Multisource Remote Sensing Time-Series Data

Arid

DOI	10.1109/JSTARS.2021.3104936
	Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa Using Multisource Remote Sensing Time-Series Data
	Shao, Zhenfeng; Feng, Xiaoxiao; Bai, Linze; Jiao, Haiming; Zhang, Ya; Li, Deren; Fan, Haisheng; Huang, Xiao; Ding, Yuqi; Altan, Orhan; Saleem, Nayyer
通讯作者	Shao, ZF (corresponding author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China.
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN	1939-1404
EISSN	2151-1535
出版年	2021
卷号	14 页码:8638-8652
英文摘要	The outbreak of large-scale desert locust plague in 2020 has attracted wide attention in the world and caused serious damage to food security and livelihood of African and Asian people. Remote sensing techniques can provide indirect feedback on locust plagues, facilitating quick, and real-time monitoring of the occurrence and development of locusts, which is of great significance for ensuring national and regional food security and stability. The hidden Markov model (HMM) is a classic machine learning model that has been widely applied in the fields of time-series data mining. In this study, we aim to predict the severity of locust plague in croplands using the time-series dynamic change features extracted from remote sensing data via HMM. In addition, we assess the damages on the croplands using change detection methods by comparing the crop spectrum before and after the locust plague from two-phase (Feburary 23 and March 7, 2020) hyperspectral images covering substudy area (northern Narok, Kenya). Evaluated by the ground truth data, the OA of predicted results of the plague severity in April, May, June, and July are 0.78, 0.71, 0.74, and 0.72, respectively. The land cover classification OA of the substudy area of the two-phase images are 97.45 and 96.14. Our study demonstrates the validity of the HMM-based method using the remote sensing time-series data to predict locust plague and evaluate its damage. The results of the cropland change detection suggest that the damage of locusts can be quantitatively evaluated using hyperspectral images.
英文关键词	Hidden Markov models Vegetation mapping Hyperspectral imaging Monitoring MODIS Agriculture Earth Change detection Hidden Markov Model (HMM) hyperspectral imagery locust plague prediction moderate-resolution imaging spectroradiometer (MODIS) semisupervised classification
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000694698900011
WOS关键词	EARTH OBSERVATION ; MODEL ; TEMPERATURE ; RESOLUTION ; HABITATS ; MOISTURE
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363563
作者单位	[Shao, Zhenfeng; Feng, Xiaoxiao; Bai, Linze; Jiao, Haiming; Zhang, Ya; Li, Deren] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China; [Fan, Haisheng] Zhuhai Orbita Aerosp Sci & Technol Co Ltd, Zhuhai 519080, Peoples R China; [Huang, Xiao] Univ Arkansas, Dept Geosci, Fayetteville, AR 72701 USA; [Ding, Yuqi] Louisiana State Univ, Dept Comp Sci, Baton Rouge, LA 70803 USA; [Altan, Orhan] Istanbul Tech Univ, Dept Geomat, TR-34467 Istanbul, Turkey; [Saleem, Nayyer] Survey Pakistan, Rawalpindi 46000, Pakistan
推荐引用方式 GB/T 7714	Shao, Zhenfeng,Feng, Xiaoxiao,Bai, Linze,et al. Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa Using Multisource Remote Sensing Time-Series Data[J],2021,14:8638-8652.
APA	Shao, Zhenfeng.,Feng, Xiaoxiao.,Bai, Linze.,Jiao, Haiming.,Zhang, Ya.,...&Saleem, Nayyer.(2021).Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa Using Multisource Remote Sensing Time-Series Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,8638-8652.
MLA	Shao, Zhenfeng,et al."Monitoring and Predicting Desert Locust Plague Severity in Asia-Africa Using Multisource Remote Sensing Time-Series Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):8638-8652.