Integrating Remote Sensing Techniques, Geographical Information Systems (GIS), and Stochastic Models for Monitoring Land Use and Land Cover (LULC) Changes in the Northern Coastal Region of Nayarit, Mexico

Abstract

Land use and land cover (LULC) changes in northern Nayarit, Mexico were estimated using post-classification change detection methods and a Markov chain model. Three thematic maps were generated by classifying Landsat images from 1973, 1900, and 2000, which were then overlaid to generate three change-detection matrices to assess the intensity and direction of changes. Between 25% and 30% of the region displayed LULC changes, attributable to a stochastic behavior that can be modeled with a first-order Markov chain. The steady-state distribution estimates indicate that the LULC patterns in the region have not yet reached equilibrium and predict the expansion of the agricultural boundaries.