@InProceedings{ShimabukuroASDHCDM:2023:FrImDe,
               author = "Shimabukuro, Yosio Edemir and Arai, Egidio and Silva, Gabriel 
                         M{\'a}ximo da and Dutra, Andeise Cerqueira and Hoffmann, Tania 
                         Beatriz and Cassol, Henrique Lu{\'{\i}}s Godinho and Duarte, 
                         Valdete and Martini, Paulo Roberto",
          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)}",
                title = "Fraction images derived from landsat mss, tm and oli images for 
                         monitoring forest cover of rond{\^o}nia state, brazilian amazon",
            booktitle = "Proceedings...",
                 year = "2023",
         organization = "IEEE International Geoscience and Remote Sensing Symposium",
             keywords = "Fraction Image, Image Processing,, Deforestation, Forest, Linear 
                         Spectral Mixing Model,, Brazilian Amazon, Landsat series.",
             abstract = "This article presents a new method for monitoring forest cover in 
                         the state of Rond{\^o}nia, in the Brazilian Amazon. The proposed 
                         method applies the Linear Spectral Mixing Model (LSMM) to Landsat 
                         datasets (MSS, TM and OLI) to derive annual vegetation, soil, and 
                         shade fraction images for the period 1980 2020. These fraction 
                         images have the advantages of reducing the volume of data to be 
                         analyzed and highlighting the target characteristics. Then, we 
                         applied a threshold method to classify forest, non-forest, 
                         hydrography, and deforestation areas. The proposed method showed 
                         to be consistent and flexible allowing to change the threshold 
                         values according to the fraction images to obtain the results with 
                         high accuracy. The results obtained by the proposed method can be 
                         easily checked over the RGB image mosaic. This kind of information 
                         is very important for environmental and climate change studies and 
                         for supporting government conservation efforts.",
  conference-location = "Pasadena, CA",
      conference-year = "2023",
                label = "lattes: 1913003589198061 1 ShimabukuroASDHCDM:2023:FRIMDE",
             language = "en",
           targetfile = "Fraction Images Derived from Landsat Mss.pdf",
        urlaccessdate = "06 maio 2024"
}