Mining Sequential Patterns from MODIS Time Series for Cultivated Area Mapping
To predict and respond to famine and other forms of food insecurity, different early warning systems are using remote analyses of crop condition and agricultural production by using satellite-based information. To improve these predictions, a reliable estimation of the cultivated area at a national scale must be carried out. In this study, we developed a data mining methodology for extracting cultivated domain patterns based on their temporal behavior as captured in time-series of moderate resolution remote sensing MODIS images.
KeywordsSequential Pattern Early Warning System Numerical Attribute Mining Sequential Pattern Representative Rule
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