# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 1 Introduction  
The mapping of forest disturbances is an important component in sustainable forest management and in implementing climate policy initiatives, such as the UN's Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme. In this paper, we use the term forest disturbance as an umbrella term for all forest changes that result from both deforestation and from forest degradation. According to both FAO and UNFCCC, deforestation is a conversion from forest land to non-forest land. Since, in many cases, we do not know the future fate of the disturbance patches, we cannot further classify them. Instead, the analysis of this study puts a focus on the size of the detected disturbance patches. Today, remote sensing applications are widely used to monitor tropical forests and their changes at large spatial scales. Most tropical forest monitoring systems are based on optical data sets and focus on large-deforestation areas, for which user accuracies around 90% and producer accuracies above 75% are reported [1; 2; 3]. In addition, recent developments have led to automated forest monitoring systems that are based on medium to high spatial resolution Earth Observation (EO) data and allow tracking forest changes in near real-time (NRT), such as Global Forest Watch Alerts for the humid tropical forests [4] and the DETER system in Brazil [5]. While methods for large area deforestation monitoring have improved considerably over the last years, there still is a lack ofmethods that accurately detect forest degradation and small forest changes. Forest degradation is thought to be a major source of carbon emissions [6], and thus needs to be better understood and integrated in REDD+ Monitoring & Measurement, Reporting and Verification (MRV) systems. A key driver of forest degradation is timber extraction by selective logging, where only a small subset of trees is harvested. The intensity of selective logging varies, depending on the amount of wood that is harvested, but usually the individual patches of change are small. Other less prominent drivers of forest degradation in developing countries include fuelwood collection and charcoal production, uncontrolled fire, and livestock grazing [7]. To accurately detect small patches of forest disturbance from selective logging and other degradation drivers, EO data of both high spatial and temporal resolution is required because gaps left by e.g., individual tree extraction are very small and quickly overgrow in tropical climates. EO data that have the potential to map forest degradation are now available: the Sentinel missions provide data at 10 m spatial resolution both in the optical and Synthetic Aperture Radar (SAR) domain and data are provided every five to 12 days. Apart from rapid forest regrowth, tropical forest disturbance mapping with optical data is also limited by the number of available cloud-free observations. C-band SAR data from the Sentinel-1 mission (central frequency of 5.404 GHz) can be acquired independent of weather conditions and daytime, leading to very dense time series of EO data. The two-satellite constellation of Sentinel-1 has a potential six-day exact repeat cycle, but the tropical regions are only covered by one satellite, leading to a 12-day repeat cycle. Some tropical areas are only covered by one descending or one ascending orbit. Information on revisit frequency and the available pass directions can be obtained from the Sentinel-1 observation scenario from ESA ([[https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario)]([https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario))).