The REDD+ framework requires accurate estimates of deforestation. These are derived by ground measurements supported by methods based on remote sensing data to automatically detect and delineate deforestations over large areas. In particular, in the tropics, optical data is seldom available due to cloud cover. As synthetic aperture radar (SAR) data overcomes this limitation, we performed a separability analysis of two statistical metrics based on the Sentinel-1 SAR backscatter over forested and deforested areas. We compared the range between the 5th and 95th temporal percentiles (PRange) and the recurrence quantification analysis (RQA) Trend metric. Unlike the PRange, the RQA Trend considers the temporal order of the SAR data acquisitions, thus contrasting between dropping backscatter signals and yearly seasonalities. This enables the estimation of the timing of deforestation events. We assessed the impact of polarization, acquisition orbit, and despeckling on the separability between forested and deforested areas and between different deforestation timings for two test sites in Mexico. We found that the choice of the orbit impacts the detectability of deforestation. In all cases, VH data showed a higher separability between forest and deforestations than VV data. The PRange slightly outperformed RQA Trend in the separation between forest and deforestation. However, the RQA Trend exceeded the PRange in the separation between different deforestation timings. In this study, C-Band backscatter data was used, although it is commonly not considered as the most suitable SAR dataset for forestry applications. Nevertheless, our approach shows the potential of dense C-Band backscatter time series to support the REDD+ framework.

中文翻译：

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来自 Sentinel-1 时间序列的重复指标在森林砍伐映射中的潜力

REDD+ 框架需要对森林砍伐进行准确估计。这些是通过基于遥感数据的方法支持的地面测量得出的，以自动检测和描绘大面积的森林砍伐。特别是在热带地区，由于云层覆盖，光学数据很少可用。由于合成孔径雷达 (SAR) 数据克服了这一限制，我们基于 Sentinel-1 SAR 后向散射对森林和森林砍伐地区的两个统计指标进行了可分离性分析。我们比较了第 5 个和第 95 个时间百分位数 (PRange) 和重复量化分析 (RQA) 趋势指标之间的范围。与 PRange 不同，RQA 趋势考虑了 SAR 数据采集的时间顺序，从而在下降反向散射信号和年度季节性之间形成对比。这使得能够估计毁林事件的时间。我们评估了极化、采集轨道和去斑对墨西哥两个试验场森林和森林砍伐区域之间以及不同森林砍伐时间之间可分离性的影响。我们发现轨道的选择会影响森林砍伐的可探测性。在所有情况下，与 VV 数据相比，VH 数据显示森林和森林砍伐之间的可分离性更高。PRange 在森林和森林砍伐之间的分离方面略微优于 RQA Trend。然而，RQA 趋势超过了不同森林砍伐时间间隔的 PRange。在这项研究中，使用了 C 波段反向散射数据，尽管它通常不被认为是最适合林业应用的 SAR 数据集。尽管如此，