PROCEEDINGS ARTICLE | August 7, 2007
Proc. SPIE. 6754, Geoinformatics 2007: Geospatial Information Technology and Applications
KEYWORDS: Agriculture, Remote sensing, Clouds, Climate change, Shape analysis, Analytical research, Spatial resolution, Nonlinear filtering, Vector spaces, Climatology
The basic idea of current study of crop growth monitoring is to analyze the relation between the shape variety of NDVI curve and the condition variety of crop, calculate the feature factors, and speculate the growing condition of crop. This investigation takes five high-yield provinces as study area, including Hebei, Henan, Shandong, Anhui and Jiangsu, and takes winter wheat as study object. The ten days maximum value composite (MVC) SPOT-VEGETATION dataset, from 1999 to 2005, is used as the main remotely sensed data. Savizky-Golay filter method, which made the NDVI time-series curve disclose the change rule of winter wheat growth better, is use to eliminate the noise. And then the method of Change Vector Analysis (CVA) is 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.
