@Article{ShimabukuroASDMDMCFJ: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 and Cassol, Henrique Lu{\'{\i}}s Godinho and Ferreira, 
                         Danilo S. and Junqueira, Luis R.",
          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 Sylvamo and Sylvamo",
                title = "Mapping and Monitoring Forest Plantations in Sao Paulo State, 
                         Southeast Brazil, Using Fraction Images Derived from Multiannual 
                         Landsat Sensor Images",
              journal = "Forests",
                 year = "2022",
               volume = "13",
               number = "10",
                pages = "e1716",
                month = "Oct.",
             keywords = "linear spectral mixing model, fraction images, eucalypt, pine, 
                         forest plantation, image processing.",
             abstract = "This article presents a method, based on orbital remote sensing, 
                         to map the extent of forest plantations in Sao Paulo State 
                         (Southeast Brazil). The proposed method uses the random forest 
                         machine learning algorithm available on the Google Earth Engine 
                         (GEE) cloud computing platform. We used 30 m annual mosaics 
                         derived from Landsat-5 Thematic Mapper (TM) images and from 
                         Landsat-8 Operational Land Imager (OLI) images for the 1985 to 
                         1995 and 2013 to 2021 time periods, respectively. These time 
                         periods were selected based on the planted areas' rotation, 
                         especially the eucalypt's short rotation. To classify the forest 
                         plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, 
                         NDWI, and NBR spectral indices, and vegetation, shade, and soil 
                         fractions were used for both sensors. These indices and the 
                         fraction images have the advantage of reducing the volume of data 
                         to be analyzed and highlighting the forest plantations' 
                         characteristics. In addition, we also generated one mosaic for 
                         each fraction image for the TM and OLI datasets by computing the 
                         maximum value through the period analyzed, facilitating the 
                         classification of areas occupied by forest plantations in the 
                         study area. The proposed method allowed us to classify the areas 
                         occupied by two forest plantation classes: eucalypt and pine. The 
                         results of the proposed method compared with the forest plantation 
                         areas extracted from the land use and land cover maps, provided by 
                         the MapBiomas product, presented the Kappa values of 0.54 and 0.69 
                         for 1995 and 2020, respectively. In addition, two pilot areas were 
                         used to evaluate the classification maps and to monitor the 
                         phenological stages of eucalypt and pine plantations, showing the 
                         rotation cycle of these plantations. The results are very useful 
                         for planning and managing planted forests by commercial companies 
                         and can contribute to developing an automatic method to map forest 
                         plantations on regional and global scales.",
                  doi = "10.3390/f13101716",
                  url = "http://dx.doi.org/10.3390/f13101716",
                 issn = "1999-4907",
             language = "en",
           targetfile = "forests-13-01716.pdf",
        urlaccessdate = "20 maio 2024"
}