author = "Sothe, Camile and Damponte, Michele and Almeida, Cl{\'a}udia 
                         Maria de and Schimalski, Marcos Benedito and Lima, Carla Luciane 
                         and Liesenberg, Veraldo and Miyoshi, Gabriela Takahashi and 
                         Tommaselli, Antonio Maria Garcia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Research 
                         and Innovation Centre, Fondazione E. Mach and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Estadual de Santa 
                         Catarina (UDESC)} and {Universidade Estadual de Santa Catarina 
                         (UDESC)} and {Universidade Estadual de Santa Catarina (UDESC)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Universidade 
                         Estadual Paulista (UNESP)}",
                title = "Tree species classification in a highly diverse subtropical forest 
                         integrating UAV-based photogrammetric point cloud and 
                         hyperspectral data",
              journal = "Remote Sensing",
                 year = "2019",
               volume = "11",
               number = "11",
                pages = "1--25",
                 note = "Setores de Atividade: Atividades dos servi{\c{c}}os de tecnologia 
                         da informa{\c{c}}{\~a}o, Pesquisa e desenvolvimento 
             keywords = "tree species mapping, tropical biodiversity, imaging spectroscopy, 
                         photogrammetry, support vector machine.",
             abstract = "The use of remote sensing data for tree species classification in 
                         tropical forests is still a challenging task, due to their high 
                         floristic and spectral diversity. In this sense, novel sensors on 
                         board of unmanned aerial vehicle (UAV) platforms are a rapidly 
                         evolving technology that provides new possibilities for tropical 
                         tree species mapping. Besides the acquisition of high spatial and 
                         spectral resolution images, UAV-hyperspectral cameras operating in 
                         frame format enable to produce 3D hyperspectral point clouds. This 
                         study investigated the use of UAV-acquired hyperspectral images 
                         and UAV-photogrammetric point cloud (PPC) for classification of 12 
                         major tree species in a subtropical forest fragment in Southern 
                         Brazil. Different datasets containing hyperspectral 
                         visible/near-infrared (VNIR) bands, PPC features, canopy height 
                         model (CHM), and other features extracted from hyperspectral data 
                         (i.e., texture, vegetation indices-VIs, and minimum noise 
                         fraction-MNF) were tested using a support vector machine (SVM) 
                         classifier. The results showed that the use of VNIR hyperspectral 
                         bands alone reached an overall accuracy (OA) of 57% (Kappa index 
                         of 0.53). Adding PPC features to the VNIR hyperspectral bands 
                         increased the OA by 11%. The best result was achieved combining 
                         VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa 
                         index of 0.70). When only the CHM was added to VNIR bands, the OA 
                         increased by 4.2%. Among the hyperspectral features, besides all 
                         the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF 
                         features and the textural mean of 565 and 679 nm spectral bands 
                         were pointed out as more important to discriminate the tree 
                         species according to Jeffries Matusita (JM) distance. The SVM 
                         method proved to be a good classifier for the tree species 
                         recognition task, even in the presence of a high number of classes 
                         and a small dataset.",
                  doi = "10.3390/rs11111338",
                  url = "http://dx.doi.org/10.3390/rs11111338",
                 issn = "2072-4292",
                label = "lattes: 1861914973833506 3 S{\"o}theDASLLMT:2019:TrSpCl",
             language = "en",
           targetfile = "remotesensing-11-01338.pdf",
                  url = "https://www.mdpi.com/2072-4292/11/11/1338",
        urlaccessdate = "15 abr. 2021"