@Article{PicoliSCSSSSFQ:2020:PoTeMa,
               author = "Picoli, Michelle Cristina Ara{\'u}jo and Sim{\~o}es, Rolf 
                         Ezequiel de Oliveira and Chaves, Michel Eust{\'a}quio Dantas and 
                         Santos, Lorena Alves dos and Sanchez Ipia, Alber Hamersson and 
                         Soares, Anderson Reis and Sanches, Ieda Del'Arco and Ferreira, 
                         Karine Reis and Queiroz, Gilberto Ribeiro",
          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 = "CBERS data cube: a powerful technology for mapping and monitoring 
                         brazilian biomes",
              journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
                         Information Sciences",
                 year = "2020",
               volume = "3",
                pages = "533--539",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
             keywords = "Analysis Ready Data, Earth observations, information extraction, 
                         LULC classification, time series, Random Forest.",
             abstract = "Currently, the overwhelming amount of Earth Observation data 
                         demands new solutions regarding processing and storage. To reduce 
                         the amount of time spent in searching, downloading and 
                         pre-processing data, the remote Sensing community is coming to an 
                         agreement on the minimum amount of corrections satellite images 
                         must convey in order to reach the broadest range of applications. 
                         Satellite imagery meeting such criteria (which usually include 
                         atmospheric, radiometric and topographic corrections) are 
                         generically called Analysis Ready Data (ARD). Furthermore, ARD is 
                         being assembled into multidimensional data cubes, minimising 
                         preprocessing tasks and allowing scientists and users in general 
                         to focus on analysis. A particular instance of this is the Brazil 
                         Data Cube (BDC) project, which is processing remote sensing images 
                         of medium spatial resolution into ARD datasets and assembling them 
                         as multidimensional cubes of the Brazilian territory. For example, 
                         BDC users are released from performing tasks such as image 
                         co-registration , aerosol interference correction. This work 
                         presents a BDC proof of concept, by analysing a BDC data cube made 
                         with images from the fourth China-Brazil Earth Resources Satellite 
                         (CBERS-4) of one of the largest biodiversity hotspot in the world, 
                         the Cerrado biome. It also shows how to map and monitor land use 
                         and land cover using the CBERS data cube. We demonstrate that the 
                         CBERS data cube is effective in resolving land use and and land 
                         cover issues to meet local and national needs related to the 
                         landscape dynamics, including deforestation, carbon emissions, and 
                         public policies.",
                  doi = "10.5194/isprs-annals-V-3-2020-533-2020",
                  url = "http://dx.doi.org/10.5194/isprs-annals-V-3-2020-533-2020",
                 issn = "0924-2716",
                label = "lattes: 3441488230835922 2 PicoliSCSSSSFQ:2020:POTEMA",
             language = "en",
           targetfile = "picoli_cbers.pdf",
        urlaccessdate = "15 maio 2024"
}