Potential of recurrence quantification analysis of Sentinel-1 time series for deforestation mapping

The UNFCCC REDD+ framework increases the need for highly accurate maps of deforestation and degradation in the tropics. Operational forest/non-forest maps are commonly based on optical imagery. However, especially in the tropics optical images are frequently degraded by the presence of clouds. Therefore, we investigated the potential of hyper-temporal Sentinel-1 synthetic aperture radar (SAR) data to derive forest/non-forest and deforestation maps. Feature selection has been used, to decrease the amount of data and to enhance the signal to noise ratio. This is especially relevant for the use of machine learning, because it is one way to deal with the curse of dimensionality. In this study we compared the use of recurrence quantification analysis (RQA) with traditional multi-temporal metrics for feature extraction from dense Sentinel-1 time series. Recurrence quantification analysis (RQA) is a non-linear time series analysis technique. It quantifies the patterns of recurrences in time series. By means of RQA a number of metrics can be calculated (e.g., determinism, recurrence Rate, laminarity), which describe the complex behaviour of dynamic systems. In contrast to traditional multi-temporal metrics (e.g., mean, median, quartiles, standard deviation), RQA considers the temporal order of the images of the time series. After calculating RQA and traditional multi-temporal metrics from the Sentinel-1 image time stacks, we performed a signature analysis. For this, we selected forested and deforested areas based on visual interpretation of annual very high resolution (1 m) optical imagery over temperate and tropical forests of Mexico. The signature analysis of the traditional and RQA metrics showed promising results for the classification of deforestation. Obviously the consideration of the temporal order of time series provides additional information compared to traditional multi-temporal statistics. Therefore RQA can enhance the accuracies of forest/non-forest and deforestation maps. In the future we plan to combine RQA metrics and multi-temporal metrics in order to further improve the map accuracies