@Article{DoblasPrietoShSaCaArAl:2020:OpNeRe,
               author = "Doblas Prieto, Juan and Shimabukuro, Yosio Edemir and Sant'Anna, 
                         Sidnei Jo{\~a}o Siqueira and Carneiro, Arian Ferreira and 
                         Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and Almeida, Claudio 
                         Aparecido de",
          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)}",
                title = "Optimizing near real-time detection of deforestation on tropical 
                         rainforests using sentinel-1 data",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "23",
                pages = "e3922",
                month = "Dec",
             keywords = "early warning systems, synthetic aperture radar, brazilian amazon, 
                         time series analysis.",
             abstract = "Early Warning Systems (EWS) for near real-time detection of 
                         deforestation are a fundamental component of public policies 
                         focusing on the reduction in forest biomass loss and associated 
                         CO2 emissions. Most of the operational EWS are based on optical 
                         data, which are severely limited by the cloud cover in tropical 
                         environments. Synthetic Aperture Radar (SAR) data can help to 
                         overcome this observational gap. SAR measurements, however, can be 
                         altered by atmospheric effects on and variations in surface 
                         moisture. Different techniques of time series (TS) stabilization 
                         have been used to mitigate the instability of C-band SAR 
                         measurements. Here, we evaluate the performance of two different 
                         approaches to SAR TS stabilization, harmonic deseasonalization and 
                         spatial stabilization, as well as two deforestation detection 
                         techniques, Adaptive Linear Thresholding (ALT) and maximum 
                         likelihood classification (MLC). We set up a rigorous, Amazon-wide 
                         validation experiment using the Google Earth Engine platform to 
                         sample and process Sentinel-1A data of nearly 6000 locations in 
                         the whole Brazilian Amazonian basin, generating more than 8M 
                         processed samples. Half of those locations correspond to 
                         non-degraded forest areas, while the other half pertained to 2019 
                         deforested areas. The detection results showed that the spatial 
                         stabilization algorithm improved the results of the MLC approach, 
                         reaching 94.36% global accuracy. The ALT detection algorithm 
                         performed better, reaching 95.91% global accuracy, regardless of 
                         the use of any stabilization method. The results of this 
                         experiment are being used to develop an operational EWS in the 
                         Brazilian Amazon.",
                  doi = "10.3390/rs12233922",
                  url = "http://dx.doi.org/10.3390/rs12233922",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-12-03922-v2.pdf",
        urlaccessdate = "09 maio 2024"
}