@InProceedings{RodriguesBenSoaKörFon:2020:ReSeIm,
               author = "Rodrigues, Marcos Ant{\^o}nio de Almeida and Bendini, Hugo do 
                         Nascimento and Soares, Anderson Reis and K{\"o}rting, Thales Sehn 
                         and Fonseca, Leila Maria Garcia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Remote sensing image time series metrics for distinction between 
                         pasture and croplands using the random forest classifier",
            booktitle = "Proceedings...",
                 year = "2020",
                pages = "149--154",
         organization = "IEEE Latin American GRSS \& ISPRS Remote Sensing 
                         Conference (LAGIRS)",
             keywords = "random forest, pasture, image time series.",
             abstract = "Pasture and croplands play an important role in Brazils economic 
                         and political scenarios, once its PIB (Raw Internal Product) is 
                         mainly based on what is exported from the rural production, such 
                         as meat and soybean, and government, with its regulations, is 
                         partresponsible for the establishment and maintaining of the 
                         conditions so that the trades can go well. In addition, these two 
                         types of land use correspond together to aprox. one third of the 
                         country extension. Moreover, frequently lands occupation is 
                         subject of discussion concerning its potential use for the reason 
                         of conflicts including Brazilian traditional communities, landless 
                         people and big farmers. Considering it, mapping pasture and 
                         croplands accurately is crucial for the country administration, in 
                         both economic and political spheres. Certainly, remote sensing is 
                         the very manner to tackle this issue, although this may not be an 
                         easy task due to the spectral similarity between these patterns. 
                         This work, hence, aims to distinct pasture from croplands in an 
                         experimental subset area of Brazilian Cerrado biome, using remote 
                         sensing metric images derived from one-year time series of the 
                         Landsat 8 products. In order to achieve this goal, we utilized six 
                         bands of the OLI sensor and calculated seven metrics, attaining a 
                         compiled dataset with 42 layers. We performed an object-based 
                         supervised classification with the Random Forest algorithm, 
                         considering both spectral and geometrical attributes. Results 
                         showed global accuracy of 80%, with Kappa index of 0.6, and the 
                         potential time series have in separating targets spectrally 
                         similar.",
  conference-location = "Santiago, Chile",
      conference-year = "22-26 mar.",
                  doi = "10.1109/lagirs48042.2020.9165671",
                  url = "http://dx.doi.org/10.1109/lagirs48042.2020.9165671",
                 isbn = "9781728143507",
                label = "lattes: 5123287769635741 5 
                         RodriguesBenSoaK{\"o}rFon:2020:ReSeIm",
             language = "en",
           targetfile = "rodrigues_remote.pdf",
        urlaccessdate = "27 abr. 2024"
}