Methodology for classification of land use and vegetation cover using MODIS-EVI data

Abstract

ABSTRACT This study aimed to verify the applicability of using MODIS-EVI sensor time series for land use and vegetation cover mapping in the Pampa biome, Rio Grande do Sul state, Brazil. The study period comprised the months from June 2013 to June 2014. The procedures included the use of MODIS Sensor images, altimetric data and nighttime images, associated with a hierarchical decision tree classifier, constructed using the C4.5 algorithm. The proposed approach stems from the consideration that the study area has varying characteristics and, therefore, should be treated simultaneously by different and intuitive classifiers, which justifies the choice of decision tree. To evaluate the results, reference data acquired from Landsat 8-OLI satellite images and IBGE data were used. The classification using the MODIS time series showed a global accuracy of 90.09% and Kappa index of 0.8885. When compared to the IBGE reference data, the Soybean class obtained a correlation coefficient of 0.94, the rice class obtained 0.97 and the silviculture class obtained the lowest value, 0.78. The highest spectral similarities were found in the vegetation cover classes, such as grassland, forest and silviculture. Therefore, with the use of multitemporal data from the MODIS sensor, combined with the use of altimetric data and nighttime images, it is possible to generate a land use and vegetation cover map for the Pampa biome with an acceptable accuracy, considering the MODIS sensor resolution limitations.