@InProceedings{FerreiraSantMart:2023:ImUrTr,
               author = "Ferreira, Matheus Pinheiro and Santos, Daniel Rodrigues dos and 
                         Martins, Gabriela Barbosa",
          affiliation = "{Instituto Militar de Engenharia (IME)} and {Instituto Militar de 
                         Engenharia (IME)} and {Instituto Militar de Engenharia (IME)}",
                title = "Improving urban tree species classification with Lidar-derived 
                         metrics",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155777",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Surface normals, LIDAR intensity, Canopy structure.",
             abstract = "Urban tree species mapping provides valuable insights into the 
                         green infrastructure management of cities. However, information on 
                         the spatial distribution of tree species in urban areas is usually 
                         acquired with costly procedures such as field surveys. Remote 
                         sensing combined with field data provides an efficient way to 
                         obtain spatially explicit information on tree species distribution 
                         over broad spatial extents. In this study, we investigate the 
                         utility of light detection and ranging (LiDAR) metrics to improve 
                         tree species classification in a highly diverse tropical urban 
                         setting. LiDAR metrics were estimated using a statistical approach 
                         that retrieved surface normals. Moreover, we explore the use of 
                         LiDAR reflectivity intensity and canopy height to discriminate 
                         among species. The results show that intensity and canopy height 
                         improve the classification accuracy, while the use of surface 
                         normals reduces it. However, more research is needed to evaluate 
                         the utility of surface normals since the species have highly 
                         variable patterns, particularly in the nz direction.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/48TMC62",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/48TMC62",
           targetfile = "155777.pdf",
                 type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
        urlaccessdate = "28 abr. 2024"
}