A METHOD INTEGRATING GF-1 MULTI-SPECTRAL AND MODIS MULTI-TEMPORAL NDVI DATA FOR FOREST LAND COVER CLASSIFICATION

In this paper a method was demonstrated that GF-1 multi-spectral and MODIS multi-temporal NDVI data were integrated for forest land cover classification. The test site is located in the central of the Xiaoxing'anling region in Heilongjiang province where covered the area of one scene of GF-1 image. The random forests algorithm was adopted to select the best features automatically which contains spectral, texture and shape features from GF-1 multi-spectral data and phenological features from multi-temporal MODIS NDVI data. A decision tree was used to supervise the classification result. Experimental results show that the overall classification accuracy and Kappa coefficient of the developed method combing multi-sources data can reach 89.46% and 0.874 respectively, with significant improvement compared with that using either GF-1 multi-spectral data or MODIS NDVI time series data alone, especially for the classification of evergreen forest.