Speaker
Description
In this study we investigated the potential of dense synthetic aperture radar (SAR) time series collected by the ESA’s Sentinel-1 satellites to detect deforestation and forest degradation areas. Since SAR data are affected by speckle, it is crucial to filter speckle before the time series analysis. Accordingly, we explored the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the high frequency components are corresponding to speckle, these effects can be isolated and removed. Since the EMD approach operates in the time domain only, it fully preserves the geometric resolution, which is required to detect small scale changes (e.g., forest degradation). We assessed the speckle filtering performance of the EMD approach. The results over forested areas showed similar statistics compared to the multi-temporal Quegan speckle filter in terms of speckle suppression (based on Equivalent Number of Looks) and an improved edge preservation. In the next step, we analyzed EMD‑filtered Sentinel-1 data for detection of deforestation and forest degradation areas. For this, we first selected forested, deforested and degraded areas based on visual interpretation of multi-temporal very high resolution (1 m) optical imagery over temperate and tropical forests of Mexico. Further, we plotted EMD‑filtered Sentinel-1 time series for the three reference classes and were able to determine the time frame of deforestation and forest degradation. The initial analyses showed promising results regarding the separation of forest and forest-change classes with EMD-filtered Sentinel-1 data in contrast to original SAR backscatter images. Furthermore, we present preliminary deforestation maps for study sites in Mexico and South Africa based on Bayesian probability approach and EMD-filtered Sentinel-1 time series backscatter.
This study is supported by DLR in the Sentinel4REDD project (FKZ:50EE1540) to develop new remote sensing based methods using Sentinel-1 and Sentinel-2 data to support UNFCC (United Nations Framework Convention on Climate Change) REDD+ MRV (Measurement, Reporting and Verification) Systems.
