@Article{ToniolGalPonSanAmo:2017:PoHyMe,
               author = "Toniol, Alana Carla and Galv{\~a}o, L{\^e}nio Soares and 
                         Ponzoni, Fl{\'a}vio Jorge and Sano, Edson Eyji and Amore, Diogo 
                         de Jesus",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Brasileiro do 
                         Meio-Ambiente e dos Recursos Naturais Renov{\'a}veis (IBAMA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Potential of hyperspectral metrics and classifiers for mapping 
                         Brazilian savannas in the rainy and dry seasons",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2017",
               volume = "8",
                pages = "20--29",
                month = "Nov.",
             keywords = "Hyperspectral remote 
                         sensingSavannasCerradoHyperionClassification.",
             abstract = "Land cover mapping of savannas in Brazil, a world's hotspot of 
                         biodiversity, is still challenging due to the tree cover gradient 
                         and the spectral similarity between some vegetation physiognomies. 
                         Here, we evaluated the potential of four classifiers (Decision 
                         Tree (DT), Random Forest (RF), Spectral Angle Mapper (SAM) and 
                         Support Vector Machine (SVM)) for discriminating eight savanna 
                         physiognomies in the rainy and dry seasons of the 
                         Bras{\'{\i}}lia National Park (BNP). Five sets of Hyperion/Earth 
                         Observing One (EO-1) metrics (reflectance, first-order derivative 
                         of reflectance; narrow-band vegetation indices (VIs); absorption 
                         band parameters; and the combination of these attributes) were 
                         tested as input data for each classifier. Before classification, 
                         the Correlation-based Feature Selection (CFS) algorithm was 
                         applied to reduce data dimensionality. To evaluate the agreement 
                         between the classifications of the different techniques, we 
                         calculated the Shannon entropy. Finally, Monte Carlo simulation 
                         was applied to determine the presence of statistical differences 
                         in classifiers and metrics between seasons. The results showed 
                         that the greater spectral confusion between the savannas generally 
                         observed in the rainy season was compensated by the selection of a 
                         greater number of hyperspectral metrics for classification. SVM 
                         and RF had the highest overall classification accuracy (OA) and 
                         Kappa values in the rainy and dry seasons for each set of metrics. 
                         The reflectance and VIs presented better discrimination capability 
                         than the absorption band parameters and first-order derivative 
                         data. When all metrics were considered in the analysis, gains of 
                         6% and 8% in OA were obtained over the first ranked classifiers 
                         and metrics (SVM with reflectance in the rainy season; RF with VIs 
                         in the dry season). The lowest Shannon entropy values in the rainy 
                         and dry seasons were observed for VIs and reflectance and for 
                         physiognomies with larger vegetation cover, while the largest 
                         uncertainties were noted in savanna grassland/shrub areas. From 
                         the Monte Carlo simulations, differences in Kappa between seasons 
                         were not statistically significant for most of the metrics and 
                         classifiers at the 99% confidence level. Variations in brightness 
                         and VIs, associated with canopy structure, biochemistry and 
                         physiology, were therefore more important for classification than 
                         variations in spectral features, spectra shape and absorption 
                         bands.",
                  doi = "10.1016/j.rsase.2017.07.004",
                  url = "http://dx.doi.org/10.1016/j.rsase.2017.07.004",
                 issn = "2352-9385",
             language = "en",
           targetfile = "toniol_potential.pdf",
        urlaccessdate = "27 abr. 2024"
}