The Tucson metropolitan area, located in the Sonoran Desert of southeastern Arizona (USA), is affected by both massive population growth and rapid climate change, resulting in important land use and land cover (LULC) changes. As its fragile arid ecosystem and scarce resources are increasingly under pressure, there is a crucial need to monitor such landscape transformations. For such ends, we propose a method to compute yearly 30 m resolution LULC maps of the region from 1986 to 2020, using a combination of Landsat imagery, derived transformation and indices, texture analysis and other ancillary data fed to a Random Forest classifier. The entire process was hosted in the Google Earth Engine with tremendous computing capacities that allowed us to process a large amount of data and to achieve high overall classification accuracy for each year, ranging from 86.7 to 96.3%. Conservative post-processing techniques were also used to mitigate the persistent confusions between the numerous isolated houses in the region and their desert surroundings and to smooth year-specific LULC changes in order to identify general trends. We then show that policies to lessen urban sprawl in the area had little effects and we provide an automated tool to continue monitoring such dynamics in the future.