@InProceedings{UeharaSoQuKöFoAd:2020:LaCoCl,
               author = "Uehara, Tatiana Dias Tardelli and Soares, Anderson Reis and 
                         Quevedo, Renata Pacheco and K{\"o}rting, Thales Sehn and Fonseca, 
                         Leila Maria Garcia and Adami, Marcos",
          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)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Land cover classification of an area susceptible to landslides 
                         using random forest and NDVI time series data",
                 year = "2020",
         organization = "IEEE International Geoscience and Remote Sensing Symposium 
                         (IGARSS)",
             keywords = "landslide, time series, Random Forest, land cover, disasters.",
             abstract = "Landslides are a natural, gravity driven phenomena which can cause 
                         great economic and human losses. To prevent them, Land Use and 
                         Land Cover (LULC) maps are essential to identify areas of high 
                         susceptibility and to detect landslide scars. This paper presents 
                         results of a classification of a landslide susceptible area, using 
                         Random Forest algorithm and time series. The time series dataset 
                         is composed by the Normalized Difference Vegetation Index (NDVI) 
                         values and 16 metrics derived from the time series. The best 
                         performance was achieved using 14 metrics plus the NDVI values, 
                         with overall accuracy of 93.23% and kappa equals to 0.8937. The 
                         metrics revealed a great capability for landslides detection.",
  conference-location = "Virtual Symposium",
      conference-year = "26 Sept. - 02 Oct.",
             language = "en",
           targetfile = "uehara_land.pdf",
        urlaccessdate = "20 abr. 2024"
}