干旱区生态环境知识资源中心(Arid): 蝗虫遥感监测预警研究现状与展望

Arid

	蝗虫遥感监测预警研究现状与展望
其他题名	Review of locust remote sensing monitoring and early warning
	黄文江; 董莹莹; 赵龙龙; 耿芸; 阮超; 张弼尧; 孙忠祥; 张寒苏; 叶回春; 王昆
来源期刊	遥感学报
ISSN	1007-4619
出版年	2020
卷号	24 期号:10 页码:1270-1279
中文摘要	气候变化背景下全球蝗灾日益肆虐,为支撑虫害及时精准防控,迫切需要开展大面积蝗虫动态监测预警研究。本文从蝗虫生境遥感监测、蝗虫发生动态遥感预警,以及蝗灾遥感损失评估3个方面介绍了当前研究现状,并指出当前存在的问题主要包括3个方面:蝗虫监测预警的时空分辨率较粗,无法精准定位虫害热点发生区和重点危害区;遥感虫害响应机制与虫害生物学扩散模型耦合度较低,导致模型时空普适性较差;缺乏高时空精度的虫害监测预警空间信息服务产品。因此,当前急需发展面向全球、洲际、全国、区域的多尺度、长时序、高精度虫害精准监测预警平台。通过建立时空精细尺度的虫害监测预警指标体系,研制遥感机制与虫害生物学机理深度耦合的高精度预测预报模型,发布多尺度高时频的虫害监测预警空间信息产品和服务,以实现海量数据的自动入库和智能存储、多层次模型的快速调用和高性能计算、虫害测报产品的在线生产和可视化服务。建立从数据到模型到产品服务的全链路,从而切实提升全球应对重大迁飞性虫害的智能化水平,为保障粮食安全、维护区域稳定和可持续发展提供科技支撑。
英文摘要	Vegetation systems worldwide are facing a growing challenge of locust threats, including Desert Locust (Schistocerca gregaria) invasion in African and Asian countries, Australian Plague Locust (Chortoicetes terminifera), and Oriental Migratory Locust (Locusta migratoria manilensis) in China. The traditional single-point hand-check monitoring method could obtain information on the occurrence and development of locust at the point level, which could not meet the needs of monitoring and timely prevention and control of locust at the area level. It is urgent to conduct large-scale locust remote sensing monitoring and early warning to support timely prevention and control of locust, to ensure the safety of agricultural production, and furthermore to promote the realization of theZero Hungergoal. We reviewed the current research of locust from three aspects, i.e. pest habitat monitoring, pest occurrence early warning, and loss assessment. We found that, the locust monitoring and early warning normally has a coarse spatial and temporal resolution, which makes it impossible to accurately locate the hazard hotspots; and the loose coupling of remote sensing pest response mechanism and pest biological diffusion model leads to a poor temporal and spatial universality and prediction accuracy; also we lack of timely, quantitative and visualized remote sensing monitoring and early warning locust service products to promote effective pest prevention. Therefore, there is an urgent need to develop a multi-scale, long-term, high-precision locust monitoring and early warning platform in global, intercontinental, national, and regional levels, to establish spatial and temporal continuous pest monitoring and early warning indexes, to develop pest monitoring and early warning models by deeply coupling of remote sensing mechanism and pest biological mechanism, and to release multi-scale, high-time-frequency pest products and services. On the one hand, we need to bring together and produce cutting edge research to provide information for locust monitoring and early warning, by integrating multi-source data, such as Earth Observation-EO, meteorological, entomological and plant pathological, etc. On the other hand, multi-models, including vegetation radiation transfer model, vegetation parameter inversion model, pest diffusion model, loss assessment model, are needed to be coupled with each other to provide temporal and spatial continuously pest monitoring, forecasting and loss assessment results. Besides, an intelligent platform, including storage module, calculation module, product module, is needed to be constructed, to integrating big data intelligent analysis, conducting high-performance model computing, realizing online locust product production and service push. The future trend of pest remote sensing system is realizing automatic storage and intelligent storage of massive data, fast calling of multi-level models and high-performance computing, and online producing of pest products and visualization. It will fully open up the entire link from data to models to product services, to effectively improve the global level of intelligence to deal with migratory pests, and to provide scientific and technological support for ensuring food security and maintaining regional stability. Furthermore, with locust now a world migratory pest, China and other countries, together with each other to discuss joint monitoring, collaborative scientific research and development of new coordinated integrated pest management mechanisms to provide economic, effective and ecologically-friendly management solutions.
中文关键词	蝗虫 ; 遥感 ; 监测 ; 预警 ; 平台
英文关键词	locust remote sensing monitoring early warning platform
类型	Review
语种	中文
收录类别	CSCD
WOS类目	Remote Sensing
CSCD记录号	CSCD:6827051
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/353959
作者单位	黄文江, 中国科学院空天信息创新研究院, 中国科学院数字地球重点实验室, 北京 100094, 中国. 董莹莹, 中国科学院空天信息创新研究院, 中国科学院数字地球重点实验室, 北京 100094, 中国. 张弼尧, 中国科学院空天信息创新研究院, 中国科学院数字地球重点实验室, 北京 100094, 中国. 孙忠祥, 中国科学院空天信息创新研究院, 中国科学院数字地球重点实验室, 北京 100094, 中国. 张寒苏, 中国科学院空天信息创新研究院, 中国科学院数字地球重点实验室, 北京 100094, 中国. 叶回春, 中国科学院空天信息创新研究院, 中国科学院数字地球重点实验室, 北京 100094, 中国. 王昆, 中国科学院空天信息创新研究院, 中国科学院数字地球重点实验室, 北京 100094, 中国. 赵龙龙, 中国科学院深圳先进技术研究院先进计算与数字工程研究所, 深圳, 广东 518055, 中国. 耿芸, 中国科学院空天信息创新研究院;;中国科学院大学资源与环境学院, 中国科学院数字地球重点实验室;;, ;;, 北京;;北京 100094;;100190, 中国. 阮超, 中国科学院空天信息创新研究院;;中国科学院大学资源与环境学院, 中国科学院数字地球重点实验室;;, ;;, 北京;;北京 100094;;100190, 中国.
推荐引用方式 GB/T 7714	黄文江,董莹莹,赵龙龙,等. 蝗虫遥感监测预警研究现状与展望[J],2020,24(10):1270-1279.
APA	黄文江.,董莹莹.,赵龙龙.,耿芸.,阮超.,...&王昆.(2020).蝗虫遥感监测预警研究现状与展望.遥感学报,24(10),1270-1279.
MLA	黄文江,et al."蝗虫遥感监测预警研究现状与展望".遥感学报 24.10(2020):1270-1279.