 This study developed a novel object-based change detection method using normalized difference vegetation index, NDVI, and semivariogram indices, SIS, to detect deforestation and fires in tropical seasonal biomes, TSBs, such as the savannas, Cerrado, and semiarid woodlands, Patinga, of Brazil. The results showed that the combination of spatial and spectral information improved change detection by correctly classifying areas with seasonal changes in NDVI caused by vegetation phenology and areas with NDVI changes caused by human-induced disturbances. The use of semivariogram indices reduced the effects of vegetation phenology on change detection, and the SVM presented the highest overall classification accuracy, 92.27%. From the vegetated areas, 18.71% of changes were caused by human-induced disturbances between 2015 and 2016. The method is particularly useful for TSBs where vegetation exhibits strong seasonality and regularly space-time series of satellite images are difficult to obtain due to persistent cloud cover. This article was authored by Eduardo Martimiano de Oliveira Silvera, Fernando del Bon Espirito Santo, Fausto Vimao Serbi Jr., and others. We are article.tv, links in the description below.