# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 1 Introduction
### State-of-the-Art Forest Disturbance Monitoring with Optical Data
The Exponentially Weighted Moving Average Change Detection (EWMACD) algorithm and its evolution, the dynamic algorithm Edyn [32; 33], use the residuals from harmonic regression over many years of Landsat data in conjunction with statistical quality control charts to signal vegetation changes. As a conclusion from the literature review, we can state that algorithms that try to detect lesser-magnitude or small-scale disturbances show higher levels of commission error. **Forest Disturbance Monitoring with SAR data**  
SAR-based forest monitoring approaches are among the most promising remote sensing approaches for the NRT mapping of forest disturbances in the tropics, thanks to the ability of SAR to operate in all weather conditions at any time of day or night. However, SAR data based approaches for forest disturbance mapping have not been well developed yet and operational applications have not yet been implemented [8]. The release of the global JERS, PALSAR, and PALSAR-2 mosaics at 25 m resolution has fostered studies that are related to forest monitoring with SAR. ALOS PALSAR mosaics were used to produce the first SAR-based annual (2007-2010) global maps of forest and non-forest cover, from which some maps of forest losses and gain were generated based on thresholds [34]. Tropical forest change monitoring with SAR data is usually performed by measuring backscatter intensity changes over time. Forest disturbances and regrowth have been assessed astubcontinental scale over South-East Asia while using SAR intensity changes [35]. Indirect approaches relate the backscatter signal to forest biomass using regional empirical regression models [36; 37] and then calculate biomass changes over time. Such an approach has been tested in a small area in Central Mozambique [38]: forest aboveground biomass (AGB) is estimated from SAR backscatter at two periods, and then changes in AGB are determined by subtracting the two estimates. This method is relevant in the context of REDD+ measurement, reporting, and verification (MRV), because both disturbance areas and biomass losses are estimated in one approach. However, the method suffers from error propagation, as errors in both maps may be summed. Applications that are based on ALOS PALSAR data are constrained by the small number of available observations: one observation per year in the case of the mosaics and one observation every 42 days at best with the original data. Most applications are therefore limited to bi-temporal analyses. Other SAR based forest disturbance mapping approaches use three-dimensional (3D) information from radargrammetry and InSAR to detect gaps in the forest canopy [39; 40; 41]. The complementarity of SAR sensors of different frequency for forest disturbance monitoring was demonstrated at a test site in the Republic of the Congo for Sentinel-1 C-band and TerraSAR-X data [24]. C-band SAR data is less suited for forest disturbance assessment and above-grove biomass estimation than L-band SAR due to its shorter wavelength, which limits the penetration into the canopy (as evidenced in [42]). C-band SAR has therefore been used to a much lesser degree than L-band data in past forest monitoring studies. However, the dense time series of the Sentinel-1 constellation offer a unique opportunity to systematically monitor forests at a repeat cycle of six to 12 days, depending on the data type and location. In addition, the continuity of Sentinel data is guaranteed up to 2030 with S-1C/D and S-2C/D, and the next generation of Sentinel satellites is already planned beyond 2030, allowing the development of long-term environmental monitoring systems. A number of recent studies have already used Sentinel-1 data for forest disturbance mapping. Most of the approaches measure changes in SAR backscatter intensity over time, either directly from image to image or by calculating the coefficient of variation of a data stack for a pre-defined time period. Empirical thresholds are then applied to derive the forest disturbances.