Forest degradation affects biodiversity and human society and contributes to greenhouse gas emissions. However, since there is no consensus about the definition of forest degradation, it is difficult to operate its characterization, evaluation and monitoring. Unlike deforestation, forest degradation does not imply a change in land cover and it happens in fine scales and is difficult to detect by means of remote sensors. Images from MODIS, Landsat, Sentinel-2, and UAVs form the main source of data for the national forest cover monitoring. The assumption here is that satellite images of different spatial and temporal resolutions as input may affect the measurement of forest degradation. The main objective is to study the influence of the spatial and temporal resolution of remote sensing images on forest degradation patterns detected. Data of multi-temporal MODIS, Landsat-8, and Sentinel-2 for an area of Michoacán, Mexico were collected. Forests were classified into primary and secondary status based on the existing land cover maps and visual interpretation of the images. The obtained land cover maps with forests at different stage of degradation were compared to derive information of forest degradation and regeneration.