@Article{SantanaFeSaMaBaReRo:2019:VeViMa,
               author = "Santana, L. S. and Ferraz, G. A. S. and Santos, L. M. and Maciel, 
                         Daniel Andrade and Barata, R. A. P. and Reynaldo, {\'E}. F. and 
                         Rossi, G.",
          affiliation = "{Universidade Federal de Lavras (UFLA)} and {Universidade Federal 
                         de Lavras (UFLA)} and {Universidade Federal de Lavras (UFLA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Lavras (UFLA)} and {Maintenance Manager 
                         at Syngenta} and {University of Florence}",
                title = "Vegetative vigor of maize crop obtained through vegetation indexes 
                         in orbital and aerial sensors images",
              journal = "Revista Brasileira de Engenharia de Biossistemas",
                 year = "2019",
               volume = "13",
               number = "3",
                pages = "195",
             keywords = "remote sensing, vegetative vigor, precision agriculture, Unmanned 
                         Aircraft System (UAS).",
             abstract = "Currently, images from unmanned aerial vehicles (UAVs) are being 
                         used due to their high spatial and temporal resolution. Studies 
                         comparing different mobile data acquisition platforms, such as 
                         satellites, are important due to the limited spatial and temporal 
                         resolution of some satellites as well of the presence of clouds in 
                         such images. The objective of this study was to compare the 
                         vegetation indices (VIs) generated from images obtained by orbital 
                         (satellite) and sub-orbital (unmanned aerial vehicles - UAV) 
                         platforms. The experiment was conducted in a maize-growing area in 
                         Paran{\'a}, Brazil. Landsat 8 and UAV images of the study area 
                         were collected. Four VIs were applied: NDVI, VIgreen, ExG and VEG. 
                         The NDVI was selected as the control and compared with the other 
                         VIs. There was a good correlation (0.79) between the NDVI and the 
                         VEG for the UAV images. For the Landsat images, the highest 
                         correlation found was between the NDVI and the VIgreen derived 
                         from UAV images, which was 0.89. It is concluded that the images 
                         obtained by UAVs generated better indices, mainly in the dry 
                         season.",
                  doi = "10.18011/bioeng2019v13n3p195-206",
                  url = "http://dx.doi.org/10.18011/bioeng2019v13n3p195-206",
                 issn = "1981-7061",
                label = "lattes: 9511166263268121 4 SantanaFeSaMaBaReRo:2019:VEVIMA",
             language = "pt",
           targetfile = "santana.pdf",
        urlaccessdate = "27 abr. 2024"
}