Land Use/land Cover Mapping in Brazilian Amazon Using Neural Network with Aster/terra Data

The objective of this study is to show the applicability of the genetic synthesis of the unsupervised artificial neural network ART2 (Adaptive Resonance Theory) in the classification of ASTER image for land use/land cover mapping. The area under study is located in northern Mato Grosso State, Brazil, and is characterized by a strong human occupation process, which caused intensive changes at the landscape, by deforestation, selective logging and agriculture. Field data were acquired in May/June 2003. The use of ASTER data allowed an improvement of the analysis of the occupation process in tropical forest areas. ASTER images have adequate spatial and spectral resolution and are an alternative to the remaining remote sensing data available. The data had a correction of the cross-talk problem, after realized a resampling from SWIR bands (spatial resolution 30 to 15 m), a atmospheric correction and rectification of ASTER images from both data sets 2002 e 2003. The input parameters for the neural network ART2 were optimized by genetic algorithm and the neural network was evaluated by a comparison of classification results with field data. The evaluation of accuracy was done using Kappa statistics. The results of the classification were of satisfactory quality. ASTER bands 2 (630-690 nm), 3 (760-860 nm) and 4 (1600-1700 nm) allowed an increased differentiation of classes, while bands 8 (22952365 nm) and 6 (2185-2225 nm) were complementary for the identification of classes. The main land use changes that occurred between 2002 and 2003 were related to deforestation, since many areas of tropical forest were replaced by agriculture and pastures. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.