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应用生态学报 ›› 2002, Vol. ›› Issue (7): 837-840.

• 研究论文 • 上一篇    下一篇

GIS支持下青海湖地区草地蝗虫发生与月均温的相关性

张洪亮1, 倪绍祥1, 邓自旺2, 谌芸2   

  1. 1. 南京师范大学地理科学学院, 南京 210097;
    2. 南京气象学院, 南京 2100440
  • 收稿日期:2001-05-25 修回日期:2001-12-14 出版日期:2002-07-15
  • 通讯作者: 张洪亮,男,1969年8月生,博士后,副教授,主要从事遥感和GIS的应用研究,公开发表学术论文20余篇.E-mail:zhlnjnu@263.net
  • 基金资助:
    国家自然科学基金(49971056);南京气象学院气象灾害和环境变化重点开放实验室资助项目(KLME010202).

Correlation between monthly average temperature and grasshopper outbreak in the region around Qinghai Lake based on GIS

ZHANG Hongliang1, NI Shaoxiang1, DENG Ziwang2, CHEN Yun 2   

  1. 1. College of Geographical Science, Nanjing Normal University, Nanjing 210097;
    2. Nanjing Meteorology University, Nanjing 210044
  • Received:2001-05-25 Revised:2001-12-14 Online:2002-07-15

摘要: 为有效地进行草地蝗虫发生的预测预报,必须摸清其生长和繁殖与地理环境特征的关系.在青海湖地区,气温是影响草地蝗虫发生的主要因素.在Arc/Info和ArcView地理信息系统的支持下,选择了环湖地区邻近的16个气象站点,采用综合方法,在小尺度上模拟该区所需月份的月均温,建立了空间分布式气温信息数据库.然后,把野外调查的蝗虫密度空间数据与相对应的月均温空间数据进行叠加,计算并分析月均温与草地蝗虫发生的关系.结果表明,月均温对草地蝗虫发生的影响是和该区蝗虫优势种的生命史密切相关的,即5、6月(孵化期)和7月(蝗蝻期)的月均温影响当年草地蝗虫的发生;8、9月(交尾、产卵期)的月均温则影响次年草地蝗虫的发生,为该区草地蝗虫发生预报模型的建立提供依据.

关键词: 地理信息系统(GIS), 气温, 模拟, 草地蝗虫, 青海湖地区

Abstract: It is necessary to study the relationship between grasshopper and ecological factors for forecasting grasshopper outbreak effectively. Temperature is one of main factors influencing grasshopper outbreak in the region around Qinghai Lake. With the support of Arc/Info and ArcView, monthly average temperatures were simulated under the scale of 150 m by data from sixteen meteorological stations adjacent to Qinghai Lake for adapting the comprehensive method and establishing spatial temperature database. Then, the relationship between grasshopper outbreak and monthly average temperature were analyzed by combining the spatial data of grasshopper density and the spatial data of monthly average temperature. The result showed that effects of monthly average temperature on Grasshopper outbreak were closely related to the life cycle of the dominant grasshopper species in the region, namely, monthly average temperatures of May, June, and July influenced grasshopper outbreak in the current year, and monthly average temperatures of August and September influenced grasshopper outbreak in the next year. Thereby, it could provide a base of establishing forecasting models of grasshopper outbreak.

Key words: Geographical Information System (GIS), Temperature, Simulation, Grasshopper, Qinghai Lake region

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