Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesDespite nowadays there are many optical sensors out there, meteorological conditions in some places on the Earth makes very difficult to have access to images without clouds. Some of those places have unique ecosystems and landscape with natural forest that should be taken care of. SAR images has proven its capabilities for monitoring deforestation since the first sensors were deployed. Sentinel-1 allows to have free access to SAR data with high temporal resolution. Therefore, this study explores the use of SAR data for monitoring deforestation in places where the precipitation rate is too high. A time-series approach is used as framework to detect forest disturbances; the work tests if performing a combination of the Sentinel-1 bands through a modified version from the RFDI gets better results than the original bands; two methods for detecting changes along the time focus on deforestation are compared. The results show that VH band is the best input with similar overall accuracy with the two methods, around 80%, the mRFDI showed acceptable results but it does not prove any improvement on the deforestation events detected. It was concluded that with a workflow optimization, it can be used to overcome the optical images problem to monitor deforestation events
