Recent advancement in spatial data collection technologies dramatically increases the contribution of ordinary people to collect and disseminate geospatial data. At the same time, there is an increasing general agreement that community based forest monitoring can play a crucial role in producing and sharing information about the condition of forest resources in time and space. Despite the advantages of the community based monitoring, there are also doubts and concerns that existed in the scientific community related to the quality of the data. Therefore, this research is aiming to assess the quality of forest monitoring activity data sets, which is collected by local experts in Kafa Biosphere Reserve in Ethiopia. The research was conducted to test the quality of local experts data for REDD+ mechanism to track the forest change and carbon emissions. In this research, we examines the quality of local experts data relative to the reference data sets of remotes sensing time series images of 2005 to 2012, GIS data sets, and ground based validation measurements. The main variables are date of forest disturbances, size of the forest disturbance, drivers information, location and coverage of forest disturbances. The spatial variables of the local experts data were assessed using the spatial data quality parameters whereas the temporal variables were compared through BFAST monitoring on Landsat time series images and visual interpretations on high resolution images of Spot and Rapid Eye. The results show that the local experts can perform and produce quality data comparable to validation measurements by experts. We found a regression coefficient value of 0.84 for area/size estimation and ~65% of correctly classification accuracy of drivers information of forest disturbances. Furthermore, the result confirms that local experts have a short time delay in detecting forest disturbances compared to high resolution remote sensing time series data of Spot 5-Rapid Eye satellite images than of Landsat imagery. Based up on the findings of this study, we suggests that the local expert data can enhance the quality of forest monitoring data of remote sensing particularly in detecting near real time forest disturbances.