It is well-known that forests play a vital role in maintaining biodiversity and the health of ecosystems across the Earth. This important ecological resource is under threat from both anthropogenic and biogenic pressures, ranging from insect infestations to commercial logging. Detecting, quantifying and reporting the magnitude of forest degradation are therefore critical to efforts towards minimizing the loss of one of Earth's most crucial resources. Traditional approaches that use image-based comparison for detecting forest degradation are frequently domain- or region-specific, which require expensive training, and are thus not suited for application at global scale. More recently, time series based change detection methods applied on remote sensing datasets have gained much attention because of their scalability, accuracy, and monitoring capability at frequent regular intervals. In this paper, we propose a novel approach to identify regions where forest degradation occurs gradually. The proposed approach complements traditional domain- and region-specific approaches by providing information on where degradation is occurring, and during what time, at a global scale.