Land-cover classification methods for multi-year AVHRR data

Abstract

Advanced Very High Resolution Radiometer (AVHRR) data have been extensively used for global land-cover classification, but few studies have taken direct and full advantage of the multi-year properties of AVHRR data. This study focused on generating effective classification features from multi-year AVHRR data to improve classification accuracy. Three types of features were derived from 12-year monthly composite normalized difference vegetation index (NDVI) and channel 4 brightness temperature from the NOAA/NASA Pathfinder AVHRR Land data for land-cover classification. The first is based on the shape of the annual average NDVI or brightness-temperature profile, which was then approximated by a Fourier series. The coefficients estimated by the weighted least-squares method were used for classification. The second and third features were based on the raw periodogram of the time series and the auto-regressive modelling. A global land-cover training database created from Landsat Thematic Mapper and Multi-spectr...