STABILIZATION OF SENTINEL-1 SAR TIME-SERIES USING CLIMATE AND FOREST STRUCTURE DATA FOR EARLY TROPICAL DEFORESTATION DETECTION

Doblas, J.; Carneiro, A.; Shimabukuro, Y.; Sant’Anna, S.; Aragão, L.; Pereira, F. R. S.

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).

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Doblas, J. / Carneiro, A. / Shimabukuro, Y. / et al: STABILIZATION OF SENTINEL-1 SAR TIME-SERIES USING CLIMATE AND FOREST STRUCTURE DATA FOR EARLY TROPICAL DEFORESTATION DETECTION. 2020. Copernicus Publications.

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