@InProceedings{SouzaAranPareJúni:2017:AnPaIm,
               author = "Souza, Guilherme Ferreira Arantes and Arantes, Arielle Elias and 
                         Parente, Leandro Leal and J{\'u}nior, Laerte Guimar{\~a}es 
                         Ferreira",
                title = "Padr{\~o}es e tend{\^e}ncias das pastagens do Brasil: uma 
                         an{\'a}lise a partir de imagens {\'{\i}}ndice de 
                         vegeta{\c{c}}{\~a}o MODIS e algoritmos de detec{\c{c}}{\~a}o 
                         de mudan{\c{c}}as",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4977--4984",
         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 = "A pasture undergoing degradation is characterized by a decrease in 
                         vegetative vigor through time, which culminates in different 
                         environmental impacts (e.g.soil erosion) and economic losses. As a 
                         phenomenon occurring in the temporal domain, the use of satellite 
                         vegetation index time series, associated with robust algorithms 
                         for detecting land cover change and trend estimations, such as 
                         BFAST, can be instrumental in identifying pasture degradation. 
                         Thus, the objective of this study was to evaluate the potential 
                         and performance of the BFAST algorithm to identify patterns of 
                         change (breakpoints), and loss of vegetative vigor (trend) of the 
                         Brazilian pasturelands. To this end, MODIS NDVI time-series (2000 
                         to 2016) were analyzed via BFAST, considering both specific 
                         pasture points, as well as the entire area of the Rio Vermelho 
                         Watershed (BHRV, State of Goi{\'a}s). BFAST proved capable of 
                         detecting major land cover transitions, as well as pasture trends 
                         related to the loss of vegetative vigor / degradation. At the 
                         landscape scale (i.e. BHRV), even though the processing was done 
                         pixel by pixel, the resulting slopes and breakpoints (dates of 
                         major changes) showed a spatial consistency, indicating the 
                         potential of BFAST to identify spatial patterns for large areas.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59414",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM444",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM444",
           targetfile = "59414.pdf",
                 type = "Agricultura e pecu{\'a}ria",
        urlaccessdate = "27 abr. 2024"
}