Mapping of agricultural areas with support vector machine in the Northwest of Minas Gerais, Brazil

Abstract

Agriculture is one of the sectors that stands out most in the Brazilian economy, often requiring the use of remote sensing to identify the expansion of agricultural areas and estimates of their production. This work aims to map the agricultural areas of the northwest of Minas Gerais by means of a support vector machine and compare the results obtained with the statistical census of the Brazilian Institute of Geography and Statistics. For the identification of the agricultural areas, the Service Vector Machine algorithm and images of the Landsat 8 / OLI and Terra / MODIS satellites and sensors were used. The training samples of the algorithm were obtained by high resolution spatial image, available in Google Earth Pro software, in the categories rivers, forest, agriculture, pasture and forestry. The OLI image mapping showed better Global Accuracy (0.81) and Kappa (0.66). The classification with OLI and MODIS images showed greater precision in the agriculture class when compared to the other classes, presenting confusion with pasture, due to the high phytomass of the pasture at the time of acquisition of the images (summer). The calculation of the agricultural areas shows an overestimation of the Service Vector Machine in the classification of the OLI and MODIS images, with a strong ratio of the MODIS data to the IBGE census (R²=0.83). Only municipalities with agricultural areas greater than 50,000 ha presented less error in the estimation of agricultural areas. The application of the algorithm shows the potential for mapping agriculture through images of the MODIS and OLI sensors, but it is necessary to evaluate the time of acquisition of the orbital images and variations in the parameters of the algorithm to improve the accuracy of the classification.