@Article{SotheAlmeLiesSchi:2017:EvSeLa,
               author = "Sothe, Camile and Almeida, Cl{\'a}udia Maria de and Liesenberg, 
                         Veraldo and Schimalski, Marcos Benedito",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade do 
                         Estado de Santa Catarina (UDESC)} and {Universidade do Estado de 
                         Santa Catarina (UDESC)}",
                title = "Evaluating Sentinel-2 and Landsat-8 data to map sucessional forest 
                         stages in a subtropical forest in Southern Brazil",
              journal = "Remote Sensing",
                 year = "2017",
               volume = "9",
               number = "8",
                pages = "Article number 838",
                month = "Aug.",
             keywords = "textural features, vegetation indices, multitemporal information, 
                         random forest, support vector machine.",
             abstract = "Studies designed to discriminate different successional forest 
                         stages play a strategic role in forest management, forest policy 
                         and environmental conservation in tropical environments. The 
                         discrimination of different successional forest stages is still a 
                         challenge due to the spectral similarity among the concerned 
                         classes. Considering this, the objective of this paper was to 
                         investigate the performance of Sentinel-2 and Landsat-8 data for 
                         discriminating different successional forest stages of a patch 
                         located in a subtropical portion of the Atlantic Rain Forest in 
                         Southern Brazil with the aid of two machine learning algorithms 
                         and relying on the use of spectral reflectance data selected over 
                         two seasons and attributes thereof derived. Random Forest (RF) and 
                         Support Vector Machine (SVM) were used as classifiers with 
                         different subsets of predictor variables (multitemporal spectral 
                         reflectance, textural metrics and vegetation indices). All the 
                         experiments reached satisfactory results, with Kappa indices 
                         varying between 0.9, with Landsat-8 spectral reflectance alone and 
                         the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance 
                         alone also associated with the SVM algorithm. The Landsat-8 data 
                         had a significant increase in accuracy with the inclusion of other 
                         predictor variables in the classification process besides the pure 
                         spectral reflectance bands. The classification methods SVM and RF 
                         had similar performances in general. As to the RF method, the 
                         texture mean of the red-edge and SWIR bands were considered the 
                         most important ranked attributes for the classification of 
                         Sentinel-2 data, while attributes resulting from multitemporal 
                         bands, textural metrics of SWIR bands and vegetation indices were 
                         the most important ones in the Landsat-8 data classification.",
                  doi = "10.3390/rs9080838",
                  url = "http://dx.doi.org/10.3390/rs9080838",
                 issn = "2072-4292",
             language = "en",
           targetfile = "sothe_evaluating.pdf",
        urlaccessdate = "27 abr. 2024"
}