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Review of locust remote sensing monitoring and early warning

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：huangwj@aircas.ac.cn
  • Introduction：黄文江，1972年生, 男, 研究员,研究方向为植被病虫害遥感机理与应用。E-mail: huangwj@aircas.ac.cn
HUANG Wenjiang1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

DONG Yingying1,  
  • Affiliation：

    Institute of Advanced Computing and Digital Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

ZHAO Longlong2,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

GENG Yun13,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

RUAN Chao13,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHANG Biyao1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

SUN Zhongxiang1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHANG Hansu1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

YE Huichun1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

WANG Kun1

Resümee

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.

Schlüsselwort

locust;remote sensing;monitoring;early warning;platform

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