@InProceedings{SilvaAHDMDMCS:2023:LaUsLa,
               author = "Silva, Gabriel M{\'a}ximo da and Arai, Egidio and Hoffmann, 
                         T{\^a}nia Beatriz and Duarte, Valdete and Martini, Paulo Roberto 
                         and Dutra, Andeise Cerqueira and Mataveli, Guilherme Augusto 
                         Verola and Cassol, Henrique Lu{\'{\i}}s Godinho and Shimabukuro, 
                         Yosio Edemir",
          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)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Land Use and Land Cover Classification in S{\~a}o Paulo, Brazil, 
                         Using Landsat-8 OLI Images and Derived Specral Indices",
            booktitle = "Proceedings...",
                 year = "2023",
         organization = "IEEE International Geoscience and Remote Sensing Symposium",
            publisher = "IEEE",
             keywords = "LULC, Image classification, Random, Forest, Linear Spectral Mixing 
                         Model.",
             abstract = "This article presents a land use and land cover (LULC) 
                         classification map based on Random Forest (RF) classifier 
                         algorithm in the S{\~a}o Paulo State (Brazil), using Landsat-8 
                         OLI data. The method consists in using time series images from 
                         January to December of 2020 based on the spectral and temporal 
                         characteristics of the LULC classes. We performed the 
                         classification class by class considering: water, urban area, 
                         forest, agriculture, forest plantation and pasture. Then, we 
                         pre-processed the selected images based on the spectral 
                         characteristics of the targets to highlight each LULC class. After 
                         that, the classification was performed using RF for each class 
                         individually and then we composed the final map with all LULC 
                         classes. The results showed a global accuracy of 89.10%, kappa 
                         value of 0.8692, producer accuracies greater than 79.80% and user 
                         accuracies greater than 76.82% for the classes mapped. Therefore, 
                         the method is consistent allowing to minimize the classification 
                         errors facilitating the posclassification edition of individual 
                         classes mapped.",
  conference-location = "Pasadena",
      conference-year = "2023",
                label = "lattes: 2801941520834407 1 SilvaAHDMDMCS:2023:LaUsLa",
             language = "en",
           targetfile = "Land use and Land Cover Classification.pdf",
        urlaccessdate = "04 jun. 2024"
}