 This article discusses a method for mapping tropical deforestation using time series and machine learning algorithms, which involves reducing seasonality using photosynthetic vegetation, PV, index fractions obtained from Landsat images, applying single and multi-temporal filters to reduce speckle noise from synthetic aperture radar, SAR, images, fusing SAR and optical images through principal component analysis, PCA, detecting changes in the time series using a non-seasonal detection approach, and machine learning algorithms, obtaining for categories, forest, cropland, bare soil, and water, comparing the change map obtained with time series and that obtained with classification algorithms, and discussing the advantages and disadvantages of the detections made with time series and machine learning algorithms. This article was authored by Unitont Arizona, Elate Zabala, Xavier Ponce, and others. We are article.tv, links in the description below.