@Article{DoblasPrietoRBQMAMCSS:2022:OpNeTi,
               author = "Doblas Prieto, Juan and Reis, Mariane Souza and Belluzzo, Amanda 
                         Pinoti and Quadros, Camila Barata and Moraes, Douglas Rafael Vidal 
                         de and Almeida, Claudio Aparecido de and Maurano, Lu{\'{\i}}s 
                         Eduardo Pinheiro and Carvalho, Andr{\'e} Fernando Ara{\'u}jo de 
                         and Sant'Anna, Sidnei Jo{\~a}o Siqueira and Shimabukuro, Yosio 
                         Edemir",
          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)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "DETER-R: An Operational Near-Real Time Tropical Forest Disturbance 
                         Warning System Based on Sentinel-1 Time Series Analysis",
              journal = "Remote Sensing",
                 year = "2022",
               volume = "14",
               number = "15",
                pages = "e3658",
                month = "Aug.",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
             keywords = "forest monitoring, SAR, Sentinel-1, time series analysis.",
             abstract = "Continuous monitoring of forest disturbance on tropical forests is 
                         a fundamental tool to support proactive preservation actions and 
                         to stop further destruction of native vegetation. Currently most 
                         of the monitoring systems in operation are based on optical 
                         imagery, and thus are flaw-prone on areas with frequent cloud 
                         cover. As this, several Synthetic Aperture Radar (SAR)-based 
                         systems have been developed recently, aiming all-weather 
                         disturbance detection. This article presents the main aspects and 
                         the results of the first year of operation of the SAR based Near 
                         Real-Time Deforestation Detection System (DETER-R), an automated 
                         deforestation detection system focused on the Brazilian Amazon. 
                         DETER-R uses the Google Earth Engine platform to preprocess and 
                         analyze Sentinel-1 SAR time series. New images are treated and 
                         analyzed daily. After the automated analysis, the system 
                         vectorizes clusters of deforested pixels and sends the 
                         corresponding polygons to the environmental enforcement agency. 
                         After 12 months of operational life, the system has produced 
                         88,572 forest disturbance warnings. Human validation of the 
                         warning polygons showed a extremely low rate of misdetections, 
                         with less than 0.2% of the detected area corresponding to false 
                         positives. During the first year of operation, DETER-R provided 
                         33,234 warnings of interest to national monitoring agencies which 
                         were not detected by its optical counterpart DETER in the same 
                         period, corresponding to an area of 105,238.5 ha, or approximately 
                         5% of the total detections. During the rainy season, the rate of 
                         additional detections increased as expected, reaching 8.1%.",
                  doi = "10.3390/rs14153658",
                  url = "http://dx.doi.org/10.3390/rs14153658",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-14-03658-v2_compressed.pdf",
        urlaccessdate = "01 maio 2024"
}