After fossil fuel burning, deforestation and forest degradation are the second largest contributors to greenhouse gas emissions to the atmosphere. In order to claim the carbon credit under the reducing from deforestation and forest degradation (REDD+) scheme, a United Nation’s Framework Convention on Climate Change initiative for climate change mitigation, developing countries are required to prepare national reference emission levels for forests on the basis of historic data and national circumstances. Part of developing reference emission levels includes quantifying location, pattern, and rate of historic forest degradation, which are also called in a word the activity data for forest degradation. Applying Monte-Carlo spectral unmixing technique to Landsat images in the CLASlite^® algorithm followed by a knowledge-based classification approach, this research quantified the activity data for forest degradation at Raghunandan Hills Reserve (6143 ha) in Bangladesh. Moderate spatial resolution Landsat images were able to detect the activity data for degradation in a spatially explicit manner with high accuracy (>90  %  ). The research approach and findings can serve as valuable information for any future national level initiative for developing activity data for REDD+ projects.