Abstract
One of the most biodiverse regions on the planet, Cerrado is the second largest biome in Brazil. Among the land changes in the Cerrado, over 500,000 km\(^2\) of the biome have been changed into cultivated pastures in recent years. Categorizing types of land cover and its native formations is important for protection policy and monitoring of the biome. Based on remote sensing techniques, this work aims at developing a methodology to map pasture and native grassland areas in the biome. Data related to EVI vegetation indices obtained by MODIS images were used to perform image classification. Support Vector Machine, Multilayer Perceptron and Autoencoder algorithms were used and the results showed that the analysis of different attributes extracted from EVI indices can aid in the classification process. The best result obtained an accuracy of 85.96 % in the study area, identifying data and attributes required to map pasture and native grassland in Cerrado.
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Costa, W., Fonseca, L., Körting, T. (2015). Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science(), vol 9166. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_13
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