Characterizing the impact of Land-Use/Land-Cover changes on a Temperate Forest using the Markov model

Land-Use/Land-Cover (LULC) change is one of the main factors contributing to ecosystem degradation and to the global climate change. The Markov Chains (MC) model is a widely used technique for the spatio-temporal evaluation of LULC changes, allowing the projection of the landscape variability based on the multidirectional potential of LULC changes. This study assessed the LULC changes in a disturbed temperate forest basin of northern Mexico during the period 1990–2019. In addition, three LULC scenarios, employing the MC model, were projected for 2048. Supervised classification techniques were performed on data from Landsat sensors to generate LULC maps. Results from the Kappa Index showed a precision of 85 and 85.8% for the classifications of 1990 and 2019, respectively. During the evaluated period, degradation and deforestation processes in the basin were the main factors of disturbance, causing the pine-oak forest to show the biggest loss of coverage (190.81 km2). Conversely, open lands showed the biggest increase in its coverage, which indicates anthropogenic activities as the main driver causing changes on the ecosystem. Projections for 2048 indicate processes of degradation and deforestation will continue, expecting increases in coverage for open lands, deciduous forest, and secondary forest. The current and projected conditions of the landscape highlight the importance of the implementation of conservation and restoration strategies, as well as responsible public policies in the study area to mitigate the impacts on these ecosystems.