Abstract
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).
Highlights
Weather and seasonal-related conditions of the surface can considerably affect Synthetic Aperture Radar (SAR) measurements modifying SAR timeseries characteristics (Benninga et al, 2019)
The results of the linear regression modelling lead us to test a nonparametric approach, more suited to deal with the non-normality of the used predictors
In this work we have tried to model and reduce the variability related to short-term and seasonal precipitation and the lack of it, making use of globally available climatological and forest structure data
Summary
Weather and seasonal-related conditions of the surface can considerably affect SAR measurements modifying SAR timeseries characteristics (Benninga et al, 2019). Several authors have studied susceptibility of C-band measurements to dense rain cells (Atlas et al, 1993; Kasilingam et al, 1997; Lin et al, 1997), to intercepted precipitation water in the canopy (Dobson et al, 1991; Henderson and Lewis, 1998; De Jong et al, 2000; Cisneros Vaca and Van Der Tol, 2018), and to canopy humidity (see for example Quegan et al, 2000). C-band SAR backscattering can suffer attenuations between -2 and -2.4 dB when crossing dense storm cells (Moore et al, 1997; Danklmayer et al, 2009) and can increase 1 to 1.5 dB (Dobson et al, 1991) due to intercepted rain. Seasonal changes on water content of the canopy can lead to oscillations of 2.5 dB (Quegan et al, 2000, on ERS-C) to 1.5 dB (Benninga et al, 2019, on Sentinel1) over mature temperate forests. Frolking et al (2011) found a strong negative anomaly on SeaWinds active microwave Kuband backscatter data collected over the Amazon Basin during the 2005 drought and detected a striking correlation between water deficit measurements and Ku signal
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