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Fire plays a critical role in nutrient cycling and species diversity but also causes severe disturbances to ecosystems. Despite remote sensing has emerged as a valuable tool to study wildfires, a lack of information regarding post-fire dynamics still remains for certain regions. This study investigated the post-fire dynamics of four burned sites in the temperate forest of Chihuahua, northern Mexico from 2009 to 2017, based on the variability of spectral indices under varied topographic conditions of aspects and slopes. The Normalized Difference Vegetation index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Burn Ratio (NBR), and Normalized Difference Water index (NDWI) were utilized to assess vegetation recovery on the burned sites. One-way ANOVAs and Tukey´s tests were performed to evaluate separately the effects of aspect and slope on the indices’ variability (p < 0.05). In general, the NDVI measured the highest values of the burned sites along the evaluated lapse while the NDWI indicated the lowest measures. Results indicated significant effects of aspect on NDVI and SAVI (p < 0.05), while slope significantly influenced the four evaluated indices (p < 0.05).That in turn showed northern aspects and steeper slopes to have the highest post-fire vegetation recoveries. Furthermore, aspect only affected the bi-annual variability of SAVI (p < 0.05). However, the NBR and NDWI showed higher magnitudes of change among periods than the SAVI (i. e., recovery and non-recovery). Besides registering a continuous recovery of the burned sites from the wildfire events to 2015, this study revealed the NDVI and SAVI as the most useful spectral indices for assessing post-fire vegetation dynamics in the region. Besides aspect and slope, future studies could focus on the inclusion of additional indicators to more effectively assess post-fire ecosystem dynamics in northern Mexico.

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 México 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.