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Published： 07 October 2020 ，
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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.2020.蝗虫遥感监测预警研究现状与展望.遥感学报，24（10）： 1270-1279
Huang W J，Dong Y Y，Zhao L L，Geng Y，Ruan C，Zhang B Y，Sun Z X，Zhang H S，Ye H C and Wang K. 2020. Review of locust remote sensing monitoring and early warning. Journal of Remote Sensing（Chinese）， 24（10）：1270-1279［DOI：10.11834/jrs.20200239］
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。
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 the “Zero Hunger” goal. 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.
蝗虫遥感监测预警平台
locustremote sensingmonitoringearly warningplatform
Anderson N L. 1964. Some relationships between grasshoppers and vegetation. Annals of the Entomological Society of America, 57(6): 736-742 [DOI: 10.1093/aesa/57.6.736http://dx.doi.org/10.1093/aesa/57.6.736]
Bolkart M, Dahms T C, Conrad C, Latchininsky L V and Löw F. 2016. Mapping and monitoring locust habitats in the Aral sea region based on satellite earth observation data//12th International Congress of Orthopterology. Ilhéus: Orthopterists' Society: 94
Bryceson K P. 1990. Digitally processed satellite data as a tool in detecting potential Australian plague locust outbreak areas. Journal of Environmental Management, 30(3): 191-207 [DOI: 10.1016/0301-4797(90)90001-Dhttp://dx.doi.org/10.1016/0301-4797(90)90001-D]
Cease A J, Elser J J, Ford C F, Hao S G, Kang L and Harrison J F. 2012. Heavy livestock grazing promotes locust outbreaks by lowering plant nitrogen content. Science, 335(6067): 467-469 [DOI: 10.1126/science.1214433http://dx.doi.org/10.1126/science.1214433]
Chen J, Ni S X and Li Y M. 2008. LAI retrieval of reed canopy using the neural network method. Remote Sensing for Land and Resources, (2): 62-67
陈健, 倪绍祥, 李云梅. 2008. 基于神经网络方法的芦苇叶面积指数遥感反演. 国土资源遥感, (2): 62-67
Cherlet M, Mathoux P, Bartholomé E and Defourny P. 2000. SPOT VEGETATION contribution to desert locust habitat monitoring//Proceedings of the VEGETATION 2000 Conference. Italy: Lake Maggiore: 247-257
Cissé S, Ghaout S, Babah Ebbe M A, Kamara S and Piou C. 2016. Field verification of the prediction model on desert locust adult phase status from density and vegetation. Journal of Insect Science, 16(1): 74 [DOI: 10.1093/jisesa/iew046http://dx.doi.org/10.1093/jisesa/iew046]
Cressman K. 2001. Desert Locust Guidelines 3. Information and Forecasting.2nd ed. Rome: FAO
Cressman K. 2008. The use of new technologies in desert locust early warning. Outlooks on Pest Management, 19(2): 55-59 [DOI: 10.1564/19apr03http://dx.doi.org/10.1564/19apr03]
Cressman K. 2013a. Climate change and locusts in the WANA region//Climate Change and Food Security in West Asia and North Africa. Dordrecht: Springer: 131-143 [DOI: 10.1007/978-94-007-6751-5_7http://dx.doi.org/10.1007/978-94-007-6751-5_7]
Cressman K. 2013b. Role of remote sensing in desert locust early warning. Journal of Applied Remote Sensing, 7(1): 075098 [DOI: 10.1117/1.JRS.7.075098http://dx.doi.org/10.1117/1.JRS.7.075098]
Crooks W T S and Archer D J. 2002. SAR observations of dryland moisture - towards monitoring outbreak areas of the Brown Locust in South Africa//IEEE International Geoscience and Remote Sensing Symposium. Toronto, Ontario, Canada: IEEE: 1994-1996 [DOI: 10.1109/IGARSS.2002.1026424http://dx.doi.org/10.1109/IGARSS.2002.1026424]
Crooks W T S and Cheke R A. 2014. Soil moisture assessments for brown locust Locustana pardalina breeding potential using synthetic aperture radar. Journal of Applied Remote Sensing, 8(1): 084898 [DOI: 10.1117/1.JRS.8.084898http://dx.doi.org/10.1117/1.JRS.8.084898]
Despland E, Rosenberg J and Simpson S J. 2004. Landscape structure and locust swarming: a satellite’s eye view. Ecography, 27(3): 381-391 [DOI: 10.1111/j.0906-7590.2004.03779.xhttp://dx.doi.org/10.1111/j.0906-7590.2004.03779.x]
Deveson E D. 2013. Satellite normalized difference vegetation index data used in managing Australian plague locusts. Journal of Applied Remote Sensing, 7(1): 075096 [DOI: 10.1117/1.JRS.7.075096http://dx.doi.org/10.1117/1.JRS.7.075096]
Deveson T. 2001. Decision support for locust management using GIS to integrate multiple information sources//Proceedings of the Geospatial Information and Agriculture Conference. Sydney: NSW Agriculture: 361-374
Deveson T and Hunter D. 2002. The operation of a GIS-based decision support system for Australian locust management. Insect Science, 9(4): 1-12 [DOI: 10.1111/j.1744-7917.2002.tb00167.xhttp://dx.doi.org/10.1111/j.1744-7917.2002.tb00167.x]
Dutta D, Bhatawdekar S, Chandrasekharan B, Sharma J R, Adiga S, Wood D and Mccardle A. 2004. Geo-limis - a decision support system for minimizing locust impact in republic of Kazakhstan. Journal of the Indian Society of Remote Sensing, 32(1): 25-47 [DOI: 10.1007/BF03030846http://dx.doi.org/10.1007/BF03030846]
Eltoum M, Dafalla M and Hamid A. 2014. Detection of change in vegetation cover caused by desert locust in Sudan//SPIE Proceeding Asia Pacific Remote Sensing. Beijing, China: SPIE
Escorihuela M J, Merlin O, Stefan V, Moyano G, Eweys O A, Zribi M, Kamara S, Benahi A S, Ebbe M A B, Chihrane J, Ghaout S, Cissé S, Diakité F, Lazar M, Pellarin T, Grippa M, Cressman K and Piou C. 2018. SMOS based high resolution soil moisture estimates for desert locust preventive management. Remote Sensing Applications: Society and Environment, 11: 140-150 [DOI: 10.1016/j.rsase.2018.06.002http://dx.doi.org/10.1016/j.rsase.2018.06.002]
FAO. 2019. Locust watch, Food and Agricultural Organization of the United Nations. Desert Locust Bulletin, 483: 1-8. http://www.fao.org/ag/locusts/en/archives/archive/1823/2415/index.htmlhttp://www.fao.org/ag/locusts/en/archives/archive/1823/2415/index.html
FAO. 2020. Desert Locust. Rome: Author. Retrieved from http: //www. fao.org/locusts/en/www.fao.org/locusts/en/
Fu Q H. 2005. Soil Salt Content Inversion Using Remote Sensing and Its Application in Study on Oriental Migratory Locust. Nanjing: Nanjing Normal University: 59-66
扶卿华. 2005. 土壤盐分含量的遥感反演及其在东亚飞蝗研究中的应用. 南京: 南京师范大学: 59-66 [DOI: 10.7666/d.y801549]
Gómez D, Salvador P, Sanz J, Casanova C, Taratiel D and Casanova J L. 2018. Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture. Journal of Applied Remote Sensing, 12(3): 036011 [DOI: 10.1117/1.JRS.12.036011http://dx.doi.org/10.1117/1.JRS.12.036011]
Gómez D, Salvador P, Sanz J, Casanova C, Taratiel D and Casanova J L. 2019. Desert locust detection using Earth observation satellite data in Mauritania. Journal of Arid Environments, 164: 29-37 [DOI: 10.1016/j.jaridenv.2019.02.005http://dx.doi.org/10.1016/j.jaridenv.2019.02.005]
Guo H D. 2017. Big Earth data: a new frontier in Earth and information sciences. Big Earth Data, 1(1/2): 4-20 [DOI: 10.1080/20964471.2017.1403062http://dx.doi.org/10.1080/20964471.2017.1403062]
Gutman G G. 1999. On the use of long-term global data of land reflectances and vegetation indices derived from the advanced very high resolution radiometer. Journal of Geophysical Research, 104(D6): 6241-6255 [DOI: 10.1029/1998JD200106http://dx.doi.org/10.1029/1998JD200106]
Han X Z. 2003. Study on Remote Sensing Mechanism and Methods for East Asian Migratory Locust Hazard Monitoring. Beijing: Institute of Remote Sensing Application, Chinese Academy of Sciences: 68-82. (韩秀珍. 2003. 东亚飞蝗灾害的遥感监测机理与方法研究. 北京: 中国科学院遥感应用研究所: 68-82.)
Healey R G, Robertson S G, Magor J T, Pender J and Cressman K. 1996. A GIS for desert locust forecasting and monitoring. International Journal of Geographical Information Systems, 10(1): 117-136 [DOI: 10.1080/02693799608902070http://dx.doi.org/10.1080/02693799608902070]
Hunter D and Deveson T. 2002. Forecasting and management of migratory pests in Australia. Insect Science, 9(4): 13-25 [DOI: 10.1111/j.1744-7917.2002.tb00168.xhttp://dx.doi.org/10.1111/j.1744-7917.2002.tb00168.x]
Ji R, Xie B Y, Li D M, Li Z and Zhang X. 2004. Use of MODIS data to monitor the oriental migratory locust plague. Agriculture, Ecosystems and Environment, 104(3): 615-620 [DOI: 10.1016/j.agee.2004.01.041http://dx.doi.org/10.1016/j.agee.2004.01.041]
Ji R, Zhang X, Xie B Y, Li Z, Liu T J and Liu C. 2003. Use of MODIS data to detect the Oriental migratory locust plague: a case study in Nandagang, Hebei Province. Acta Entomologica Sinica, 46(6): 713-719
季荣, 张霞, 谢宝瑜, 李哲, 刘团结, 刘闯. 2003. 用MODIS遥感数据监测东亚飞蝗灾害——以河北省南大港为例. 昆虫学报, 46(6): 713-719 [DOI: 10.3321/j.issn:0454-6296.2003.06.008http://dx.doi.org/10.3321/j.issn:0454-6296.2003.06.008]
Jiang J J, Ni S X and Wei Y C. 2002. Knowledge based grasshopper habitat classification approach supported by GIS in Qinghai Lake region. Journal of Remote Sensing, 6(5): 387-392
蒋建军, 倪绍祥, 韦玉春. 2002. GIS辅助下的环青海湖地区草地蝗虫生境分类研究. 遥感学报, 6(5): 387-392 [DOI: 10.3321/j.issn:1007-4619.2002.05.012http://dx.doi.org/10.3321/j.issn:1007-4619.2002.05.012]
Joshi M J, Raj V P, Solanki C B and Vaishali V B. 2020. Desert Locust (Schistocera gregaria F.) outbreak in Gujarat (India). Agriculture and Food: E-Newsletter, 2(6): 691-693
Kang L, Li H C and Chen Y L. 1989. Studies on the relationships between distribution of Orthopterans and vegetation types in the Xilin River Basin district, Inner Mongolia Autonomous Region. Acta Phytoecologica Et Geobotanica Sinica, 13(4): 341-349
康乐, 李鸿昌, 陈永林. 1989. 内蒙古锡林河流域直翅目昆虫生态分布规律与植被类型关系的研究. 植物生态学与地植物学学报, 13(4): 341-349
Krall S, Peveling R and Ba Diallo D. 1997. New Strategies in Locust Control. Basel: Birkhäuser: 198-200 [DOI: 10.1007/978-3-0348-9202-5http://dx.doi.org/10.1007/978-3-0348-9202-5]
Latchininsky A V. 2013. Locusts and remote sensing: a review. Journal of Applied Remote Sensing, 7(1): 075099 [DOI: 10.1117/1.JRS.7.075099http://dx.doi.org/10.1117/1.JRS.7.075099]
Latchininsky A V, Sivanpillai R, Driese K L and Wilps H. 2007. Can early season Landsat images improve locust habitat monitoring in the Amudarya River Delta of Uzbekistan?. Journal of Orthoptera Research, 16(2): 167-173 [DOI: 10.1665/1082-6467(2007)16http://dx.doi.org/10.1665/1082-6467(2007)16[167:CESLII]2.0.CO;2]
Le Gall M, Overson R and Cease A. 2019. A global review on locusts (Orthoptera: Acrididae) and their interactions with livestock grazing practices. Frontiers in Ecology and Evolution, 7: 263 [DOI: 10.3389/fevo.2019.00263http://dx.doi.org/10.3389/fevo.2019.00263]
Li K L and Ni S X. 2006. Breeding area classification for oriental migratory locust assisted by remote sensing: a case study of the Huanghua Region in Hebei Province. Geographical Research, 25(4): 579-586
李开丽, 倪绍祥. 2006. 东亚飞蝗生境的遥感分类——以河北省黄骅地区为例. 地理研究, 25(4): 579-586 [DOI: 10.3321/j.issn:1000-0585.2006.04.003http://dx.doi.org/10.3321/j.issn:1000-0585.2006.04.003]
Löw F, Waldner F, Latchininsky A, Biradar C, Bolkart M and Colditz R R. 2016. Timely monitoring of Asian migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index. Journal of Environmental Management, 183: 562-575 [DOI: 10.1016/j.jenvman.2016.09.001http://dx.doi.org/10.1016/j.jenvman.2016.09.001]
Lu S H and Ye S J. 2020. Using an image segmentation and support vector machine method for identifying two locust species and instars. Journal of Integrative Agriculture, 19(5): 1301-1313 [DOI: 10.1016/S2095-3119(19)62865-0http://dx.doi.org/10.1016/S2095-3119(19)62865-0]
Ma J W, Han X Z, Hasibagan, Wang C L, Zhang Y J, Tang J Y, Xie Z Y and Deveson T. 2005. Monitoring East Asian migratory locust plagues using remote sensing data and field investigations. International Journal of Remote Sensing, 26(3): 629-634 [DOI: 10.1080/01431160310001595019http://dx.doi.org/10.1080/01431160310001595019]
Ma J W, Han X Z, Hasibagan, Wang Z G, Yan S X and Dai Q. 2004. Remote sensing new model for monitoring the East Asian Migratory Locust infections based on its breeding circle. Journal of Remote Sensing, 8(4): 370-377
马建文, 韩秀珍, 哈斯巴干, 王志刚, 燕守勋, 戴芹. 2004. 基于东亚飞蝗生育周期的遥感蝗灾监测新模式. 遥感学报, 8(4): 370-377 [DOI: 10.11834/jrs.20040412http://dx.doi.org/10.11834/jrs.20040412]
Ma J W, Han X Z, Hasibagan, Zhang Y J, Tang J Y and Xie Z Y. 2003. The experimental Remote Sensing monitoring of the Oriental Migratory Locust plague. Remote Sensing for Land and Resources, (1): 51-55
马建文, 韩秀珍, 哈斯巴干, 张跃进, 汤金仪, 谢志庾. 2003. 东亚飞蝗灾害的遥感监测实验. 国土资源遥感, (1): 51-55 [DOI: 10.3969/j.issn.1001-070X.2003.01.014]
Ma S C. 1958. The population dynamics of the oriental migratory locust (Locusta migratoria manilensis Meyen) in China. Acta Entomologica Sinica, 8(1): 1-40
马世骏. 1958. 东亚飞蝗(Locusta migratoria manilensis Meyen)在中国的发生动态. 昆虫学报, 8(1): 1-40
McCulloch L and Hunter D M. 1983. Identification and monitoring of Australian plague locust habitats from landsat. Remote Sensing of Environment, 13(2): 95-102 [DOI: 10.1016/0034-4257(83)90015-9http://dx.doi.org/10.1016/0034-4257(83)90015-9]
Meynard C N, Lecoq M, Chapuis M P and Piou C. 2020. On the relative role of climate change and management in the current desert locust outbreak in East Africa. Global Change Biology, 26(7): 3753-3755 [DOI: 10.1111/gcb.15137http://dx.doi.org/10.1111/gcb.15137]
Murali Sankar P and Shreedevasena S. 2020. Desert locusts (Schistocerca gregaria)–A global threatening transboundary pest for food security. Research Today, 2(5): 389-391
Ni S X. 2002. Remote Sensing Monitoring and Forecasting of Grasshopper in the Region around Qinghai Lake. Shanghai: Shanghai Scientific and Technical Publishers: 101-120
倪绍祥. 2002. 环青海湖地区草地蝗虫遥感监测与预测. 上海: 上海科学技术出版社: 101-120
Pekel J F, Ceccato P, Vancutsem C, Cressman K, Van Bogaert E and Defourny P. 2011. Development and application of multi-temporal colorimetric transformation to monitor vegetation in the desert locust habitat. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2): 318-326 [DOI: 10.1109/JSTARS.2010.2052591http://dx.doi.org/10.1109/JSTARS.2010.2052591]
Piou C, Lebourgeois V, Benahi A S, Bonnal V, Jaavar M E H, Lecoq M and Vassal J M. 2013. Coupling historical prospection data and a remotely-sensed vegetation index for the preventative control of desert locusts. Basic and Applied Ecology, 14(7): 593-604 [DOI: 10.1016/j.baae.2013.08.007http://dx.doi.org/10.1016/j.baae.2013.08.007]
Piou C, Gay P E, Benahi A S, Ebbe M A O B, Chihrane J, Ghaout S, Cisse S, Diakite F, Lazar M, Cressman K, Merlin O and Escorihuela M J. 2019. Soil moisture from remote sensing to forecast desert locust presence. Journal of Applied Ecology, 56(4): 966-975 [DOI: 10.1111/1365-2664.13323http://dx.doi.org/10.1111/1365-2664.13323]
Qi X L, Wang X H, Xu H F and Kang L. 2007. Influence of soil moisture on egg cold hardiness in the migratory locust Locusta migratoria (Orthoptera: Acridiidae). Physiological Entomology, 32(3): 219-224 [DOI: 10.1111/j.1365-3032.2007.00564.xhttp://dx.doi.org/10.1111/j.1365-3032.2007.00564.x]
Ren B Y, Yang P Y, Zhu J Q and Wei Q W. 2017. Achievements and prospects of sustainable management of locust plague in China. China Plant Protection, 37(9): 55-57, 69
任彬元, 杨普云, 朱景全, 魏启文. 2017. 我国蝗虫灾害可持续治理成效与展望. 中国植保导刊, 37(9): 55-57, 69
Renier C, Waldner F, Jacques D C, Ebbe M A B, Cressman K and Defourny P. 2015. A dynamic vegetation senescence indicator for near-real-time desert locust habitat monitoring with MODIS. Remote Sensing, 7(6): 7545-7570 [DOI: 10.3390/rs70607545http://dx.doi.org/10.3390/rs70607545]
Roychoudhury A. 2020. ‘Desert Locust’: a menace to Indian agriculture and economy. Eureka Journals, 4(1): 14-20
Scanlan J C, Grant W E, Hunter D M and Milner R J. 2001. Habitat and environmental factors influencing the control of migratory locusts (Locusta migratoria) with an entomopathogenic fungus (Metarhizium anisopliae). Ecological Modelling, 136(2/3): 223-236 [DOI: 10.1016/S0304-3800(00)00424-5http://dx.doi.org/10.1016/S0304-3800(00)00424-5]
Shi Y, Huang W J, Dong Y Y, Peng D L, Zheng Q and Yang P Y. 2018. The influence of landscape's dynamics on the Oriental Migratory Locust habitat change based on the time-series satellite data. Journal of Environmental Management, 218: 280-290 [DOI: 10.1016/j.jenvman.2018.04.028http://dx.doi.org/10.1016/j.jenvman.2018.04.028]
Shroder J F and Sivanpillai R. 2016. Biological and Environmental Hazards, Risks, and Disasters. Massachusetts: Academic Press: 67-86
Sivanpillai R and Latchininsky A V. 2007. Mapping locust habitats in the Amudarya River Delta, Uzbekistan with multi-temporal MODIS imagery. Environmental Management, 39(6): 876-886 [DOI: 10.1007/s00267-006-0193-yhttp://dx.doi.org/10.1007/s00267-006-0193-y]
Song P L, Zheng X M, Li Y Y, Zhang K Y, Huang J F, Li H M, Zhang H J, Liu L, Wei C W, Mansaray L R, Wang D Z and Wang X M. 2020. Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data. Science of the Total Environment, 719: 137519 [DOI: 10.1016/j.scitotenv.2020.137519http://dx.doi.org/10.1016/j.scitotenv.2020.137519]
Stefan V G, Escorihuela M J, Merlin O, Chihrane J, Ghaout S and Piou C. 2018. Using Sentinel-3 land surface temperature to derive high resolution soil moisture estimates for desert locust management. EGU General Assembly 2018, 20: EGU2018-13752
Stokstad E. 2020. In Somalia, an unprecedented effort to kill massive locust swarms with biocontrol [J/OL].Science, [2020-02-12].https://www.sciencemag.org/news/2020/02/somalia-unprecedented-effort-kill-massive-locust-swarms-biocontrolhttps://www.sciencemag.org/news/2020/02/somalia-unprecedented-effort-kill-massive-locust-swarms-biocontrol.
Tu X B, Hu G, Fu X W, Zhang Y H, Ma J, Wang Y P, Gould P J L, Du G L, Su H T, Zhang Z H and Chapman J W. 2020. Mass windborne migrations extend the range of the migratory locust in East China. Agricultural and Forest Entomology, 22(1): 41-49 [DOI: 10.1111/afe.12359http://dx.doi.org/10.1111/afe.12359]
Waldner F, Ebbe M A B, Cressman K and Defourny P. 2015. Operational monitoring of the desert locust habitat with earth observation: an assessment. ISPRS International Journal of Geo-Information, 4(4): 2379-2400 [DOI: 10.3390/ijgi4042379http://dx.doi.org/10.3390/ijgi4042379]
Wang J N, Chen X L, Hou X W, Zhou L B, Zhu C D and Ji L Q. 2017. Construction, implementation and testing of an image identification system using computer vision methods for fruit flies with economic importance (Diptera: Tephritidae). Pest Management Science, 73(7): 1511-1528 [DOI: 10.1002/ps.4487http://dx.doi.org/10.1002/ps.4487]
Weiss J. 2016. Do Locusts Seek Greener Pastures? An Evaluation of MODIS Vegetation Indices to Predict Presence, Abundance and Impact of the Australian Plague Locust in Southeastern Australia. Australia: The University of Melbourne: 29-53
Wu T, Ni S X and Li Y M. 2006. Research on the forecasting model about area of the outbreak from oriental migratory locust using of LAI. Acta Ecologica Sinica, 26(3): 862-869
吴彤, 倪绍祥, 李云梅. 2006. 基于LAI的东亚飞蝗发生面积的预测模型. 生态学报, 26(3): 862-869 [DOI: 10.3321/j.issn:1000-0933.2006.03.031http://dx.doi.org/10.3321/j.issn:1000-0933.2006.03.031]
Zha Y, Gao J, Ni S X and Shen N. 2005. Temporal filtering of successive MODIS data in monitoring a locust outbreak. International Journal of Remote Sensing, 26(24): 5665-5674 [DOI: 10.1080/01431160500196349http://dx.doi.org/10.1080/01431160500196349]
Zha Y, Ni S X, Gao J and Liu Z B. 2008. A new spectral index for estimating the oriental migratory locust density. Photogrammetric Engineering and Remote Sensing, 74(5): 619-624 [DOI: 10.14358/PERS.74.5.619http://dx.doi.org/10.14358/PERS.74.5.619]
Zhang B, Chen Z, Peng D, Benediktsson J A, and Plaza A. 2019. Remotely sensed big data: evolution in model development for information extraction. Proceedings of the IEEE, 107(12), 2294-2301.
Zhang C L. 2006. Retrieval of Land Surface Temperature Using Remotely Sensed Data and Its Application in Monitoring Oriental Migratory Locust. Nanjing: Nanjing Agricultural University: 40-60
张灿龙. 2006. 地表温度遥感反演及其在东亚飞蝗监测中的应用. 南京: 南京农业大学: 40-60 [DOI: 10.7666/d.y980817]
Zhang H L and Ni S X. 2003. A new algorithm for grasshopper outbreak monitoring from Landsat-TM imagery. Journal of Remote Sensing, 7(6): 504-508
张洪亮, 倪绍祥. 2003. 草地蝗虫发生遥感监测的一种新算法. 遥感学报, 7(6): 504-508 [DOI: 10.11834/jrs.20030612http://dx.doi.org/10.11834/jrs.20030612]
Zhang X F, Rao J F and Pan Y F. 2015. Progressive approach for risk prediction of rangeland locust hazard in Xinjiang based on remotely sensed data. Transactions of the Chinese Society of Agricultural Engineering, 31(11): 202-208
张显峰, 饶俊峰, 潘一凡. 2015. 基于遥感的新疆蝗虫灾害渐进式修正预测方法. 农业工程学报, 31(11): 202-208 [DOI: 10.11975/j.issn.1002-6819.2015.11.029http://dx.doi.org/10.11975/j.issn.1002-6819.2015.11.029]
Zhao F J. 2014. The Application of Hyper Spectra in Locusts Monitor on Grassland. Beijing: Chinese Academy of Agricultural Sciences: 49-78
赵凤杰. 2014. 高光谱在草地蝗虫监测中的应用研究. 北京: 中国农业科学院: 49-78
Zhao L C, Li Q Z, Zhang Y, Wang H Y and Du X. 2020. Normalized NDVI valley area index (NNVAI)-based framework for quantitative and timely monitoring of winter wheat frost damage on the Huang-Huai-Hai Plain, China. Agriculture, Ecosystems and Environment, 292: 106793 [DOI: 10.1016/j.agee.2019.106793http://dx.doi.org/10.1016/j.agee.2019.106793]
Zheng X M. 2019. Monitoring Oriental Migratory Locust Damage based on Multi-Platform Remote Sensing Techniques. Hangzhou: Zhejiang University: 46-55
郑晓梅. 2019. 基于多平台遥感的东亚飞蝗灾害监测研究. 杭州: 浙江大学: 46-55
Zhu E L. 1999. Occurrence and Management of the Oriental Migratory Locust in China. Beijing: China Agriculture Press: 3-38
朱恩林. 1999. 中国东亚飞蝗发生与治理. 北京: 中国农业出版社: 3-38
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