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Nathália Suemi Saito Fernanda Viana Paiva Arguello Maurício Alves Moreira Alexandre Rosa dos Santos Fernando Coelho Eugenio Alvaro Costa Figueiredo


The landscape ecology metrics associated with data mining can be used to increase the potential of remote sensing data analysis and applications, being an important tool for decision making. The present study aimed to use data mining techniques and landscape ecology metrics to classify and quantify  different types of vegetation using a multitemporal analysis (2001 and 2011), in São Luís do Paraitinga city, São Paulo, Brazil. Object-based image analyses and the C4.5 data-mining algorithm were used for automated classification. Classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Four land use and land cover classes were mapped, including Eucalyptus plantations, whose area increased from 4.4% to 8.6%. The automatic classification showed a kappa index of 0.79 and 0.80, quantity disagreements of 2% e 3.5% and allocation measures of 5.5% and 5% for 2001 and 2011, respectively. We therefore concluded that the data mining method and landscape ecology metrics were efficient in separating vegetation classes.

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How to Cite
SAITO, Nathália Suemi et al. GEOTECNOLOGY FOR FOREST COVER TEMPORAL ANALISYS. CERNE, [S.l.], v. 22, n. 1, p. 11-18, apr. 2016. ISSN 2317-6342. Available at: <>. Date accessed: 23 sep. 2020.
Data Mining; Landscape ecology; GeoDMA