@Article{SotheLiesAlmeSchi:2017:AbClEs,
               author = "Sothe, Camile and Liesenberg, Veraldo and Almeida, Cl{\'a}udia 
                         Maria de and Schimalski, Marcos Benedito",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade do Estado de Santa Catarina (UDESC)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade do 
                         Estado de Santa Catarina (UDESC)}",
                title = "Abordagens para classifica{\c{c}}{\~a}o do est{\'a}gio 
                         sucessional da vegeta{\c{c}}{\~a}o do Parque Nacional de 
                         S{\~a}o Joaquim empregando imagens Landsat-8 e Rapideye",
              journal = "Boletim de Ci{\^e}ncias Geod{\'e}sicas",
                 year = "2017",
               volume = "23",
               number = "3",
                pages = "389--404",
                month = "jul./set.",
             keywords = ": Sucess{\~a}o florestal, M{\'a}quinas de Vetor de Suporte, 
                         Florestas Rand{\^o}micas, Atributos, Secondary forests, Support 
                         Vector Machine, Random Forest, Features.",
             abstract = "A classifica{\c{c}}{\~a}o remota dos diferentes est{\'a}dios 
                         sucessionais da vegeta{\c{c}}{\~a}o ainda constitui um desafio 
                         devido {\`a} similaridade espectral destas classes. Este artigo 
                         tem o objetivo de avaliar o desempenho de imagens Landsat-8 e 
                         RapidEye para a classifica{\c{c}}{\~a}o do est{\'a}dio 
                         sucessional da vegeta{\c{c}}{\~a}o em um fragmento de Floresta 
                         Ombr{\'o}fila Mista, localizado no Parque Nacional de S{\~a}o 
                         Joaquim- SC. Para isto, tr{\^e}s grupos de vari{\'a}veis gerados 
                         a partir de cada imagem foram avaliados, sendo: (1) composto 
                         somente pelas bandas espectrais puras; (2) composto pelas 
                         m{\'e}tricas texturais GLCM geradas a partir das bandas 
                         espectrais; e (3) composto pelas vari{\'a}veis dos dois grupos 
                         anteriores, al{\'e}m de dois {\'{\i}}ndices de 
                         vegeta{\c{c}}{\~a}o no caso da imagem Landsat-8, e tr{\^e}s 
                         {\'{\i}}ndices para a RapidEye. Cada grupo foi testado com os 
                         classificadores florestas rand{\^o}micas (Random Forest- RF), 
                         m{\'a}quinas de vetor de suporte (Support Vector Machine - SVM) e 
                         m{\'a}xima verossimilhan{\c{c}}a (Maxver). Todos os experimentos 
                         alcan{\c{c}}aram resultados satisfat{\'o}rios, com 
                         {\'{\i}}ndice Kappa variando de 0,66 a 0,88 e acur{\'a}cia de 
                         usu{\'a}rio e produtor superiores a 50%. O melhor resultado 
                         alcan{\c{c}}ado foi com a imagem Landsat-8, grupo 3, associado ao 
                         algoritmo RF. A medida de import{\^a}ncia das vari{\'a}veis 
                         obtida com o algoritmo RF mostrou que as m{\'e}tricas texturais 
                         m{\'e}dia, contraste e dissimilaridade destacaram-se na 
                         classifica{\c{c}}{\~a}o para ambas as imagens. ABSTRACT: The 
                         remote classification of the different vegetation successional 
                         stages still represents a challenging task in face of the similar 
                         spectral response of such classes. This paper is committed to 
                         evaluate the performance of Landsat-8 and RapidEye images in the 
                         classification of successional stages within a patch of Mixed 
                         Ombrophilous Forest located in S{\~a}o Joaquim National Park, 
                         Santa Catarina State, south of Brazil. Three variables dataset 
                         extracted from each image were analyzed, namely; (1) one solely 
                         consisting of the spectral bands themselves; (2) a second one 
                         comprising GLCM-based texture measures derived from the spectral 
                         bands; and (3) a third one containing these two datasets and 
                         additionally two vegetation indices obtained from the Landsat 8 
                         image and three vegetation indices from the RapidEye image. Each 
                         dataset was subject to three classifiers: random forest (RF), 
                         support vector machine (SVM), and maximum likelihood estimation 
                         (MLE or maxver). All conducted experiments achieved satisfactory 
                         results, with Kappa coefficients ranging from 0.66 to 0.88, and 
                         both userīs and producerīs accuracies lying over 50%. The best 
                         result was attained with the Landsat 8 image using the third 
                         dataset and the RF classifier. The analysis of the variables 
                         relevance with this classifier showed that the texture measures 
                         mean, contrast and dissimilarity were decisive for the successful 
                         classification of both images.",
                  doi = "10.1590/S1982-21702017000300026",
                  url = "http://dx.doi.org/10.1590/S1982-21702017000300026",
                 issn = "1413-4853",
             language = "pt",
           targetfile = "sothe_abordagens.pdf",
        urlaccessdate = "27 abr. 2024"
}