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Title: Comparative analysis of MODIS time-series classification using support vector machines and methods based upon distance and similarity measures in the Brazilian cerrado-caatinga boundary
Authors: Abade, Natanael Antunes
Carvalho Júnior, Osmar Abílio de
Guimarães, Renato Fontes
Oliveira, Sandro Nunes de
Assunto:: Caatinga
Sensoriamento remoto
Issue Date: Sep-2015
Publisher: MDPI
Citation: ABADE, Natanael Antunes et al. Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary. Remote Sensing, v. 7, p. 12160-12191, set. 2015. Disponível em: <>. Acesso em: 10 ago. 2016. doi:10.3390/rs70912160
Abstract: We have mapped the primary native and exotic vegetation that occurs in the Cerrado-Caatinga transition zone in Central Brazil using MODIS-NDVI time series (product MOD09Q1) data over a two-year period (2011–2013). Our methodology consists of the following steps: (a) the development of a three-dimensional cube composed of the NDVI-MODIS time series; (b) the removal of noise; (c) the selection of reference temporal curves and classification using similarity and distance measures; and (d) classification using support vector machines (SVMs). We evaluated different temporal classifications using similarity and distance measures of land use and land cover considering several combinations of attributes. Among the classification using distance and similarity measures, the best result employed the Euclidean distance with the NDVI-MODIS data by considering more than one reference temporal curve per class and adopting six mapping classes. In the majority of tests, the SVM classifications yielded better results than other methods. The best result among all the tested methods was obtained using the SVM classifier with a fourth-degree polynomial kernel; an overall accuracy of 80.75% and a Kappa coefficient of 0.76 were obtained. Our results demonstrate the potential of vegetation studies in semiarid ecosystems using time-series data.
Licença:: © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( Fonte: Acesso em: 10 ago. 2016.
Appears in Collections:GEA - Artigos publicados em periódicos

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