基于变化向量分析的冬小麦长势变化监测研究

    Monitoring of growth changes of winter wheat based on change vector analysis

    • 摘要: 现有的农作物长势遥感监测的基本思路是利用NDVI曲线形态变化与作物苗情变化的响应关系,提取特征参数,推测作物的生长发育状况。但由于表征NDVI时间序列曲线的特征参数较多,难以对所有特征参数进行全面变化分析。本研究引进变化向量分析理论,以东部五省冬小麦为研究对象,以1999-2005年SPOT-VGT的旬最大合成NDVI数据为主要数据源,采用Savizky-Golay滤波器重构NDVI时间序列,进而构建基于变化向量分析的长势监测模型,分别对研究区的年际与年内长势变化进行时间和空间上的定量分析。研究表明,变化向量分析方法能有效地从空间域和时间域反映东部五省冬小麦长势变化规律,以单一综合性指标综合了NDVI时间序列曲线的大多数特征参数,为农作物长势遥感监测提供了一种新的研究思路。

       

      Abstract: The basic idea of current study of crop growth monitoring is to analyze the relationship between the shape variety of NDVI curve and the condition variety of crop, calculate the feature factors, and speculate the growth condition of crop. There are many characteristics parameters based on the time series curve of NDVI. Current study cannot carry out all-sided analysis from all of parameters. This study takes five high-yield provinces as study areas, including Hebei, Henan, Shandong, Anhui and Jiangsu Provinces, and takes winter wheat as a study object. The ten-day maximum value composite (MVC) SPOT-VEGETATION dataset, from 1999 to 2005, was used as the main remotely sensed data. The Savizky-Golay filtering method, which made the NDVI time-series curve disclose the change rule of winter wheat growth better, was used to eliminate the noise. And then the method of Change Vector Analysis (CVA) was applied to detect the change dynamics of winter wheat. According to the each average value of Change Vector in six years, changes, intra-annual, inter-annual and interlocal, of winter wheat have been quantified. The result shows that the method of Change Vector Analysis is effective for monitoring the winter wheat growth as a new idea, which can integrate most of the feature factors of NDVI curve.

       

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