## 1 Introduction  
Land use and land cover (LULC) changes are among the research topics more recurrent in remote sensing [1; 2; 3; 4]. Remote sensing provides comprehensive and extensive information to understand the interaction between terrestrial ecosystems and their responses to environmental factors [5; 6; 7; 8; 9; 10; 11]. This technology is considered a powerful source to obtain information from terrestrial surface characteristics at different temporal and spatial scales [12; 13; 14; 15; 16]. For more than half a century, scientists have used several types of remote sensing data. From those, the most commonly used source is the Landsat satellite based-data [17]. One of main uses of the Landsat-based satellite images is the identification or classification of LULC changes [18; 19; 20; 21; 22]. Some interesting conclusions have been driven through the use of remote sensing in LULC changes. It has been observed that changes in land uses are dynamic, non-linear interactions between humans and nature and are driven by complex stochastic processes [23]; worldwide changes in LULC for the last 300 years have shown gains in agriculture and losses in forest [24]; rapid population growth in Africa has been identified as the main driving force for the expansion of agricultural land, whereas in developing countries, urbanization dynamics are attributed to demographic factors [25] and transitions from cropland to urban and water bodies areas have been identified as an important trend in Europe [26]. Some of the more remarkable research found in literature is the work conducted by [PERSON] et al. [27]. Their research consists of the use of multi-temporal Landsat Thematic Mapper data to map and quantify the LULC changes in seven counties of the Twin Cities located in the metropolitan area of the state of Minnesota, United States. They used satellite images corresponding to the years 1986, 1991, 1998 and 2002. [PERSON] et al. demonstrated an urban zone increment from 23.7 to 32.8% in the study area. Surfaces such as rural areas, croplands, forests and wetlands decreased from 69.6 to 60.5%. Another unique example is the work carried out by [PERSON] et al. [28], where Landsat satellite data from 1973 to 2015 were classified to study land use change and its possible causes in Gonder, Ethiopia. The study showed that about 60.1% of the area experienced land use changes within the study period. In Mexico, little research related to the quantification of LULC changes has been found within the literary review. One of the few works found was carried out by [PERSON] et al. [29]. Their research presents a methodology to develop a land use map in Mexico for the year 2005. The scheme was based on time series from a Moderate Resolution Imaging Spectroradiometer (MODIS) with a 250 m resolution and an extensive sampling of data for the different geographic zones of the Mexican surface. The results showed a map with an overall precision of 82.5% (Kappa statistic = 0.79). A further evaluation with 780 randomly generated samples within the classes with referenced field data indicated a precision equal to 83.4% (Kappa statistic = 0.80). Another study found in the literature includes the generation of a land use map of the Latin American and Caribbean zone for the year 2008. This project was developed within the framework of the project of the Latina American Network for Monitoring and Studying of Natural Resources (SERENA) [30]. Similar to [PERSON] et al. [29], time series and decision trees with MODIS data (250 m) were used for the classification of land use changes. The discrete SERENA model showed an overall accuracy of 84%. Other uses of remote sensing in Mexico include the detection of chinampas in the Xaltocan area in the Northern Basin of Mexico [31], the evolution of sea temperature on the west coast of Baja California [32] and/or hydropower assessment [33].