@InProceedings{SantoroAlmeMoli:2023:StMeRe,
               author = "Santoro, Giulio Brossi and Almeida, Danilo Roberti Alves de and 
                         Molin, Paulo Guilherme",
          affiliation = "{Universidade de S{\~a}o Paulo (USP) } and {Universidade de 
                         S{\~a}o Paulo (USP) } and {Universidade Federal de S{\~a}o 
                         Carlos (UFSCar)}",
                title = "Comparison between RBG and Lidar point clouds: structure metrics 
                         in restored forest",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155794",
         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 = "Remote sensing, Forest structure, Drones, Photogrammetry, 
                         Monitoring.",
             abstract = "The use of Light Detection and Ranging (LiDAR) sensors onboard 
                         Remotely Piloted Aircraft Systems (RPAS) has proven to be capable 
                         to monitor forest restoration by analyzing forest structure. 
                         However, such systems often require substantial investments. 
                         Overtime, the access to RPAS with high resolution RGB sensors was 
                         facilitated. This study sought to explore the potential of 
                         monitoring tropical forest restoration using digital aerial 
                         photogrammetry as opposed to LIDAR technology. Thus, a comparison 
                         was established between 6 structural metrics calculated from RGB 
                         and LiDAR data. The products were compared through linear 
                         regressions modeled for each variable. Results show great 
                         relationship between both sensors (Rē=0.48-0.98), diverging only 
                         for 2 metrics (RMSE of 5.6 and 13.05; and MAE of 4.2 and 9.857). 
                         Therefore, the products of digital photogrammetry achieved results 
                         similar to those provided by LiDAR technology. However, we 
                         highlight the limitation of RGB data for generating the Digital 
                         Terrain Model and the need to spatially adjust the images. 
                         Finally, considering these limitations, the use of commercial RGB 
                         RPAS proves to be a viable and cost-effective option for 
                         monitoring restoration initiatives.",
  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/492QF7H",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/492QF7H",
           targetfile = "155794.pdf",
                 type = "Monitoramento e modelagem ambiental",
        urlaccessdate = "27 abr. 2024"
}