Time Series Analysis Of Multi-Temporal AVHRR-NDVI Data Applied To A Land Cover Classification

Remote Sensing has an important role on landuse/landcover analysis. Information extraction of the earth surface using different remotely sensed data has become a main aspect of Remote Sensing Geo-analysis. NOAA-AVHRR provides multi-temporal data for research on vegetation Phenology. This paper is based on multi-temporal NDVI data and presents time series analysis, which is used for extracting seasonal information of vegetation in one year.1) Standardized Principal Analysis can extract seasonal information embedded in a time series data. It is more valuable in time series analysis than unstandardized principal analysis.2) Fourier Analysis can decompose a time series signal into different frequency components, which represent different seasonal mode.3) The Phenology characteristics extracted from multi-temporal NDVI data are used for a vegetation classification.The time series analysis presented in this paper may have a good value on research about how to make the best of multi-temporal remotely sensed data and land cover classification.