@InProceedings{PadilhaSchiLies:2017:AvInDe,
               author = "Padilha, Alan Schreiner and Schimalski, Marcos Benedito and 
                         Liesenberg, Veraldo",
                title = "Avalia{\c{c}}{\~a}o das informa{\c{c}}{\~o}es derivadas de uma 
                         varredura LASER terrestre em uma unidade amostral de 
                         reflorestamento de Pinus taeda",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5936--5943",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Terrestrial laser scanning (TLS) provides measurements with a 
                         millimeter-level of details from a certain object. Such 
                         measurements over a forest stand allow precise estimates of 
                         important forest inventory attributes such as the diameter at the 
                         breast height (DBH) and tree height. We selected a common Pinus 
                         taeda stand with 17 years old destinated to pulp and paper 
                         production. Our objective was to explore different data processing 
                         steps, registering and point editing using FARO Scene and 
                         CloudCompare software. After, routines were written in Python to 
                         detect the spatial position of each tree in the forest stand. We 
                         also perform several tests changing the square dimensions to 
                         select individual trees as well as to remove duplicated points 
                         aiming to obtain the DBH. We validate the TLS measurements with 
                         continuous forest inventory data. Our results show that the best 
                         parameters were found for a square size of 43 by 43 cm with a 
                         Pearson coefficient of 0,86 and a Coefficient of determination of 
                         0,74. The point density can still be reduced until the factor of 
                         0.025 in which there is not any significant difference 
                         (\α=5%).",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59529",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMC25",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMC25",
           targetfile = "59529.pdf",
                 type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
        urlaccessdate = "27 abr. 2024"
}