基于MODIS数据的作物苗情和灾情监测系统及其开发应用

Development and Application of the Seedling Situation and Disaster Monitoring System Based on the MODIS Data

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作者:

  • 梁瀚月 成都理工大学地球科学学院 成都 610059
  • 房世波 中国气象科学研究院生态环境与农业气象研究所 北京 100081
  • 杨武年 成都理工大学地球科学学院 成都 610059
  • 李璇 中国气象科学研究院生态环境与农业气象研究所 北京 100081

中文摘要:

 随着全球气候变暖,极端高低温、干旱事件趋多趋强,已经威胁到作物的生长和生产。目前,苗情灾情监测多依靠单时相遥感数据,由于难以在不同方法间形成作物灾情和苗情的同一标准,不同方法间难以比较。然而,以长时间序列植被指数为基础数据,通过构建植被条件指数、距平植被指数、与往年比较指数等,以历史作物苗情和灾情为评价标准的方法,为作物苗情和灾情监测提供了新的思路。文章介绍了利用长时间序列的MODIS准实时的多光谱二级数据和植被指数产品数据,构建长时间序列的历史作物苗情和灾情为评价标准,通过系统集成,实现从遥感数据自动下载、MODIS影像预处理,到作物基本参数的信息提取,再到干旱、雪灾监测、苗情和灾情监测,以及最后的专题图的制作等一整套简单化、系统化的处理过程。以西藏为例,介绍了该系统的牧草作物苗情和灾情监测平台,表明该系统可以应用于大面积作物的苗情和灾情监测,以及产量的预测。

中文关键词:

灾情监测,长势监测,作物,遥感技术,业务系统

KeyWords:

disaster monitoring, growth monitoring, crops, remote sensing, operational system

Abstract:

 The extreme high and low temperatures, drought events increased and strengthened associated with global warming.It has been a threat to crop rowth and production. At present, the seedling ituation and disaster monitoring rely solely on the single-phase remote sensing data. Different methods are difficult to be compared due to the difficulty of forming the same standard on the crop disaster and seedling growth between different methods. In this study, a disaster and growth monitoring stem was established based upon the vegetation index time series data, the historical growth of the seedling and crop disaster. The system takes long time series of the seedling historical growth and the crop disaster as the evaluation criteria,uses the long time series of MODIS secondary data,which is quasi real-time and multispectral, as well as the vegetation index product data, achieves the automatic download of remote sensing data, the pre-processing of MODIS images and the extracts the basic parameters of the crop responsing to drought, snow, and disaster, as well as a set of simplified business process including thematic map production etc. The system was employed in Tibet pasture/crop condition as monitoring platforms, It is shown that the system can be applied to large area crop’s and disaster monitoring, as well as yield prediction.

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