# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 5 Conclusions  
Our study confirms, that combining optical and SAR data considerably improves the accuracies of forest disturbance maps. The overall accuracies increase by about 3% at both test sites in Peru and Gabon. Producer accuracies of the disturbance class increase by 13% in Gabon and by 25% in Peru when compared to using only one sensor type. The assessment by disturbance patch size shows, that about 30% of very small disturbances (\(<0.2\) ha) in Gabon can be detected by using both data sets compared to 17.5/15.7% when using a single sensor. The detection of forest disturbances of this small size from satellite imagery has not been studied yet, as most studies base their validations on Landsat pixel-size validation plots. A combination of S-1 and optical data at data level instead of at result level could proof to be difficult. The detected forest disturbance patches are often not identical but instead highly complementary. This is something to be tested in future research. We could also show that a combination of optical and SAR data at result level does not yield the same amount of accuracy gain at the two test sites (PA increase of 13% in Gabon and of 25% in Peru). More studies are needed in different parts of the tropics and under different forest conditions to better understand the potential and limitations of sensor type combinations for tropical forest disturbance detections.  
Further improvements should focus on developing more advanced methods for deriving forest disturbances from SAR and optical data. Simple thresholding, as applied in this study for the optical data, could be replaced by more advanced time series analysis approaches. However, the applicability of such approaches in tropical areas is limited by frequent cloud cover and the lack of dense time series. Here, Planet imagery could play a major role in the future. Commercial Planet data will be available at a daily repetition rate and the new generation of Planet imagery will be spectrally congruent with Sentinel-2. A combination of these two data sets to generate a dense optical time series could stimulate the development of more sophisticated time series analysis tools for tropical forest disturbance monitoring. Such tools are already being used in other fields of remote sensing with good results. In the SAR domain, advancements to the proposed method should focus on an improved detection of larger deforestation patches.. Alternatively, a combination of SAR backscatter-based approaches, which are able to detect large deforestation, and the shadow detection method used in this study could also improve the detection accuracy. Our study supports the use of high resolution, dense time series data for improved forest monitoring, specifically for the detection of small areas of forest disturbance. We could clearly show the high complementarity of the SAR and optical data sets for forest disturbance detection and, therefore, pledge for further development of multi-sensor approaches.  
Supervision, M.H. and M.S.; Validation, J.D.; Writing--original draft, M.H. and J.D.; Writing--review & editing. S.M., J.D. and M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Horizon 2020 Framework Programme: 685761.  
**Acknowledgments:** The presented work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 685761 (Project EOMonDis).  
**Conflicts of Interest:** The authors declare no conflict of interest.