@InProceedings{AlbuquerqueViViArFeGr:2023:CoFoRe,
               author = "Albuquerque, Rafael Walter and Vieira, Daniel Luis Mascia and 
                         Vicente, Luiz Eduardo and Ara{\'u}jo, Luciana Spinelli de and 
                         Ferreira, Manuel Eduardo and Grohmann, Carlos Henrique",
          affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Empresa Brasileira de 
                         Pesquisa Agropecu{\'a}ria (EMBRAPA)} and {Embrapa Meio Ambiente} 
                         and {Embrapa Meio Ambiente} and {Universidade Federal de 
                         Goi{\'a}s (UFG)} and {Universidade de S{\~a}o Paulo (USP)}",
                title = "Comparing forest restoration canopy cover measurements using RGB 
                         and multispectral sensors onboard drones",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155275",
         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 = "Remotely Piloted Aircrafts, Unmanned Aerial Vehicle, 
                         Red-Green-Blue, Infra-Red, Forest Restoration Monitoring.",
             abstract = "Remotely Piloted Aircrafts (RPA) coupled with Red-Green- Blue 
                         (RGB) sensors have a high potential to monitor Forest Restoration 
                         (FR), but multispectral sensors onboard RPA are more expensive and 
                         still demand more studies when applied to FR monitoring. This work 
                         aims to compare an RGB and a multispectral sensor capacity to 
                         measure the canopy cover of a FR project. Four canopy cover 
                         methods were evaluated using: the point cloud data generated by 
                         the RGB sensor; a vegetation index for RGB sensors; the Normalized 
                         Difference Vegetation Index (NDVI); and the Near Infra-Red band 
                         (Nir) only. The point cloud data method was the most accurate and 
                         the only one that presented all accuracies greater than 0.9. 
                         However, the multispectral sensor presented more potential for 
                         scientific research because it seems to be capable of detecting 
                         different photosynthetic activities on the trees and, 
                         consequently, different responses to FR treatments, which should 
                         be confirmed by future studies.",
  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/48UR2RS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/48UR2RS",
           targetfile = "155275.pdf",
                 type = "VANTs, videografia e alta resolu{\c{c}}{\~a}o",
        urlaccessdate = "02 maio 2024"
}