author = "Cassol, Henrique Luis Godinho and Shimabukuro, Yosio Edemir and 
                         Beuchle, Ren{\'e} and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz 
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Joint Research Centre 
                         (JRC)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Sentinel-1 time-series analysis for detection of forest 
                         degragation by selective loggin",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "755--758",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "SAR system, machine-learning, cloudcomputing, segmentation, 
                         regression trees.",
             abstract = "Forest degradation by selective logging is considered one of the 
                         main causes of biodiversity loss and CO2 emissions in tropical 
                         regions. However, persistent cloud cover limits the detection of 
                         selective logging using optical satellite systems in the Brazilian 
                         Amazon. We develop a novel approach to detect selective logging 
                         using one-year time-series (TS) from Sentinel-1 RADAR data 
                         (C-band), based on state-of-art cloud computing using Google Earth 
                         Engine. The method consists of two temporal TS reductions. The 
                         first reduces the TS for the median monthly record while the 
                         second one computes annual statistics like mean, standard 
                         deviation, and amplitude. The result is a composite band used for 
                         classifying the annual TS through the application of a 
                         machine-learning algorithm (CART). Classification showed 69% 
                         overall accuracy within five classes; however, the 
                         misclassification of the degradation class was 54%. The 
                         classification accuracy has increased to 79% with the removal of 
                         the regrowth class, with 74% of the degradation correctly 
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3TUTNJ5",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3TUTNJ5",
           targetfile = "97297.pdf",
                 type = "Degrada{\c{c}}{\~a}o de florestas",
        urlaccessdate = "22 jan. 2021"