@InProceedings{ShimabukuroArSiDuMaDuMa:2022:MaMoFo,
               author = "Shimabukuro, Yosio Edemir and Arai, Egidio and Silva, Gabriel 
                         M{\'a}ximo da and Dutra, Andeise Cerqueira and Mataveli, 
                         Guilherme Augusto Verola 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)}",
                title = "Mapping and Monitoring Forest Plantation using Fraction Images 
                         Derived from Multi-Annual Landsat TM Datasets",
            booktitle = "Proceedings...",
                 year = "2022",
                pages = "5969--5972",
         organization = "IEEE International Geoscience and Remote Sensing Symposium (IGARSS 
                         )",
            publisher = "IEEE",
             keywords = "Eucalypt and Pine plantations, Fraction image, Image Processing, 
                         Linear Spectral Mixing Model.",
             abstract = "This article presents a method to map the extent of forest 
                         plantation in an area located in the S{\~a}o Paulo State 
                         (Brazil). The proposed method applies the Linear Spectral Mixing 
                         Model (LSMM) to Landsat Thematic Mapper (TM) datasets to derive 
                         annually vegetation, soil and shade fraction images for local 
                         analysis. We used 30 m annual mosaics of TM images during the 1985 
                         to 1995 time period. These fraction images have the advantage to 
                         reduce the volume of data to be analyzed highlighting the target 
                         characteristics. Then, we generated only one mosaic for each 
                         fraction images for TM dataset computing de maximum value through 
                         this period, facilitating the classification of areas occupied by 
                         forest plantation. The proposed method allowed to classify two 
                         forest plantation classes: Eucalypt and Pine. In addition, it 
                         allowed to monitor the phenological stages of Eucalypt according 
                         to its growth cycle. The results are very important for planning 
                         and management by the commercial companies and can contribute to 
                         develop an automatic method to map forest plantation areas in a 
                         regional and global scales.",
  conference-location = "Kuala Lampur",
      conference-year = "17-22 July 2022",
                  doi = "10.1109/IGARSS46834.2022.9884210",
                  url = "http://dx.doi.org/10.1109/IGARSS46834.2022.9884210",
                 isbn = "978-166542792-0",
             language = "en",
           targetfile = "
                         
                         Mapping_and_Monitoring_Forest_Plantation_using_Fraction_Images_Derived_from_Multi-Annual_Landsat_TM_Datasets.pdf",
        urlaccessdate = "30 abr. 2024"
}