@Article{SantosFePiCaZuAu:2021:IdSpPa,
               author = "Santos, Lorena Alves dos and Ferreira, Karine Reis and Picoli, 
                         Michelle Cristina Ara{\'u}jo and Camara, Gilberto and 
                         Zurita-Milla, Raul and Augustijn, Ellen-Wien",
          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 {University of Twente} and {University of 
                         Twente}",
                title = "Identifying spatiotemporal patterns in land use and cover samples 
                         from satellite image time series",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "5",
                pages = "e974",
                month = "Mar.",
             keywords = "data training, time series, clustering, spatiotemporal patterns.",
             abstract = "The use of satellite image time series analysis and machine 
                         learning methods brings new opportunities and challenges for land 
                         use and cover changes (LUCC) mapping over large areas. One of 
                         these challenges is the need for samples that properly represent 
                         the high variability of land used and cover classes over large 
                         areas to train supervised machine learning methods and to produce 
                         accurate LUCC maps. This paper addresses this challenge and 
                         presents a method to identify spatiotemporal patterns in land use 
                         and cover samples to infer subclasses through the phenological and 
                         spectral information provided by satellite image time series. The 
                         proposed method uses self-organizing maps (SOMs) to reduce the 
                         data dimensionality creating primary clusters. From these primary 
                         clusters, it uses hierarchical clustering to create subclusters 
                         that recognize intra-class variability intrinsic to different 
                         regions and periods, mainly in large areas and multiple years. To 
                         show how the method works, we use MODIS image time series 
                         associated to samples of cropland and pasture classes over the 
                         Cerrado biome in Brazil. The results prove that the proposed 
                         method is suitable for identifying spatiotemporal patterns in land 
                         use and cover samples that can be used to infer subclasses, mainly 
                         for crop-types.",
                  doi = "10.3390/rs13050974",
                  url = "http://dx.doi.org/10.3390/rs13050974",
                 issn = "2072-4292",
             language = "en",
           targetfile = "santos_identifying.pdf",
        urlaccessdate = "30 abr. 2024"
}