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@Article{DoblasCaShSaArPe:2020:StSeSa,
               author = "Doblas, Juan Prieto and Carneiro, Arian and Shimabukuro, Yosio 
                         Edemir and Sant'Anna, Sidnei Jo{\~a}o Siqueira and Arag{\~a}o, 
                         Luiz Eduardo Oliveira e Cruz de and Pereira, Francisca Rocha de 
                         Souza",
          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 = "Stabilization of sentinel-1 sar time-series using climate and 
                         forest structure data for early tropical deforestation detection",
              journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
                         Information Sciences",
                 year = "2020",
               volume = "5",
               number = "3",
                pages = "89--96",
                month = "Aug.",
                 note = "2020 24th ISPRS Congress on Technical Commission III; Nice, 
                         Virtual; France; 31 August 2020 through 2 September 2020;",
             keywords = "Remote Sensing, Time-series Data, SAR, Modelling, Deforestation 
                         Detection, Change Detection.",
             abstract = "In this study we analyse the factors of variability of Sentinel-1 
                         C-band radar backscattering over tropical rainforests, and propose 
                         a method to reduce the effects of this variability on 
                         deforestation detection algorithms. To do so, we developed a 
                         random forest regression model that relates Sentinel-1 gamma 
                         nought values with local climatological data and forest structure 
                         information. The model was trained using long time-series of 26 
                         relevant variables, sampled over 6 undisturbed tropical forests 
                         areas. The resulting model explained 71.64% and 73.28% of the SAR 
                         signal variability for VV and VH polarizations, respectively. Once 
                         the best model for every polarization was selected, it was used to 
                         stabilize extracted pixel-level data of forested and 
                         non-deforested areas, which resulted on a 10 to 14% reduction of 
                         time-series variability, in terms of standard deviation. Then a 
                         statistically robust deforestation detection algorithm was applied 
                         to the stabilized time-series. The results show that the proposed 
                         method reduced the rate of false positives on both polarizations, 
                         especially on VV (from 21% to 2%, \α=0.01). Meanwhile, the 
                         omission errors increased on both polarizations (from 27% to 37% 
                         in VV and from 27% to 33% on VV, \α=0.01). The proposed 
                         method yielded slightly better results when compared with an 
                         alternative state-of-the-art approach (spatial normalization).",
                  doi = "10.5194/isprs-Annals-V-3-2020-89-2020",
                  url = "http://dx.doi.org/10.5194/isprs-Annals-V-3-2020-89-2020",
                 issn = "0924-2716",
             language = "en",
           targetfile = "doblas_stabilization.pdf",
        urlaccessdate = "28 mar. 2024"
}


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