@Article{FerreiraZorZanShiSou:2016:MaTrSp,
               author = "Ferreira, Matheus Pinheiro and Zortea, Maciel and Zanotta, Daniel 
                         Capella and Shimabukuro, Yosio Edemir and Souza Filho, Carlos 
                         Roberto de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Institute 
                         of Informatics, Federal University of Rio Grande do Sul, Av. Bento 
                         Gon{\c{c}}alves 9500, Porto Alegre, RS, Brazil and National 
                         Institute for Science, Education and Technology, R. Eng. Alfredo 
                         Huch 475, Rio Grande, RS, Brazil and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and Universidade Estadual de Campinas, 
                         Institute of Geosciences, Campinas, Brazil",
                title = "Mapping tree species in tropical seasonal semi-deciduous forests 
                         with hyperspectral and multispectral data",
              journal = "Remote Sensing of Environment",
                 year = "2016",
               volume = "179",
               number = "66",
                pages = "66--78",
                month = "Jun.",
             keywords = "Brazilian Atlantic Forest, Classification, Imaging spectroscopy, 
                         Individual tree crowns, WorldView-3.",
             abstract = "Accurately mapping the spatial distribution of tree species in 
                         tropical environments provides valuable insights for ecologists 
                         and forest managers. This process may play an important role in 
                         reducing fieldwork costs, monitoring changes in canopy 
                         biodiversity, and locating parent trees to collect seeds for 
                         forest restoration efforts. However, mapping tree species in 
                         tropical forests with remote sensing data is a challenge because 
                         of high floristic and spectral diversity. In this research, we 
                         discriminated and mapped tree species in tropical seasonal 
                         semi-deciduous forests (Brazilian Atlantic Forest Biome) by using 
                         airborne hyperspectral and simulated multispectral data in the 450 
                         to 2400 nm wavelength range. After quantifying the spectral 
                         variability within and among individual tree crowns of eight 
                         species, three supervised machine learning classifiers were 
                         applied to discriminate the species at the pixel level. Linear 
                         Discriminant Analysis outperformed Support Vector Machines with 
                         Linear and Radial Basis Function (RBF-SVMs) kernels and Random 
                         Forests in almost all the tested cases. An average classification 
                         accuracy of 70% was obtained when using the visible/near-infrared 
                         (VNIR, 450-919 nm) bands. The inclusion of shortwave infrared 
                         bands (SWIR, 1045-2400 nm) increased the accuracy to 84%. 
                         Narrow-band vegetation indices (VIs) were also tested and 
                         increased the classification accuracy by up to 5% when combined 
                         with VNIR features. Furthermore, the spectral bands of the 
                         WorldView-3 (WV-3) satellite sensor were simulated for 
                         classification purposes. WV-3 VNIR bands provided an accuracy of 
                         57.4%, which increased to 74.8% when using WV-3 SWIR bands. We 
                         also tested the production of species maps by using an 
                         object-oriented approach that integrated a novel segmentation 
                         algorithm that was tailored to delineate tree crowns and label 
                         high class membership pixels inside each object. In this scenario, 
                         RBF-SVMs produced the best species maps, correctly identifying 
                         84.9% of crowns with hyperspectral data and 78.5% with simulated 
                         WV-3 data. The use of a reduced set of hyperspectral bands, which 
                         were selected with stepwise regression, did not significantly 
                         affect the classification accuracies but allowed us to depict the 
                         most important wavelengths to discriminate the species. These 
                         wavelengths were located around the green reflectance peak (550 
                         nm), at the red absorption feature (650 nm) and in the SWIR range 
                         at 1200, 1700, 2100 and 2300 nm. These encouraging results suggest 
                         the feasibility of the proposed approach for mapping pioneering 
                         and climax tree species in the Brazilian Atlantic Forest Biome, 
                         highlighting its potential use in forest recovery and inventory 
                         initiatives.",
                  doi = "10.1016/j.rse.2016.03.021",
                  url = "http://dx.doi.org/10.1016/j.rse.2016.03.021",
                 issn = "0034-4257",
             language = "en",
           targetfile = "1_ferreira_mapping.pdf",
        urlaccessdate = "27 abr. 2024"
}