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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.

Sotol (Dasylirion leiophyllum) grows in the Chihuahan Desert in the north of Mexico and south of the United States. The stem of this species is used to produce a liquor widely known as sotol. A field sampling and a laboratory analysis were carried out to determine the influence of the rainy season, altitude, topography, and aspect over the soluble solid content (°Brix) and bromatological variables of mature plants of sotol in Chihuahua, Mexico. Analyses of variance and Tukey tests were performed. The rainy season and altitude showed significant statistical influence (p ≤ 0.05) on the total soluble solids content; meanwhile, only altitude showed a significant effect on the bromatological variables (p ≤ 0.05). According to the results of the Tukey tests (p < 0.05), sotol plants have the highest concentrations of soluble solid contents before the rainy season (25 °Brix) and at sites with altitudes lower than 1300 m (25.95 °Brix). In addition, the means of raw protein and carbohydrates content were the highest in plants living at altitudes lower than 1500 m, at 1.93% and 31.74%, respectively. Meanwhile, topography, aspect, and the interactions of the evaluated factors did not show significant influence (p ≤ 0.05) over the total soluble solid content, nor over the bromatological variables studied.

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.

In Mexico, buffelgrass (Cenchrus ciliaris) was introduced in the middle of the 20th century. Currently, buffelgrass has become an invasive species and has colonized various ecosystems in the country. In addition to its invasive capacity, climate change is a factor that has to be taken into account when considering how to effectively manage and control this species. The climatic niche models (CNM) and their projections for climate change scenarios allow for estimating the extent of biological invasions. Our study aimed to calibrate a CNM for buffelgrass in Mexico under the current climatic conditions and to project the extent of its biological invasion under climate change scenarios. For that, we used MaxEnt to generate the current CNM and to detect if climate change could cause future changes, we then evaluated the distribution patterns over the periods of 2041–2060, 2061–2080, and 2081–2100 for all the shared socioeconomic pathways (SSPs). Linear regressions were used to compare the outputs between current and future scenarios. Under the current climate, the CNM estimated that 42.2% of the continental surface of Mexico is highly suitable for buffelgrass. The regression analyses indicated no effects from climate change on the distribution of buffelgrass. Moreover, when the projected period is further in the future, and when the SSPs intensify, the surface of suitable areas for the species increases. These analyses clearly suggest Mexico is facing a biological invasion from buffelgrass, which may represent a threat to native biodiversity.

Understanding the genetic structure adopted by natural populations and its relation to environmental adaptation is critical for the success of restoration programs. We evaluated the genetic structure and temporal environmental niche dynamics of blue grama (Bouteloua gracilis) in 48 populations. The genetic evaluation was performed through amplified fragment length polymorphism (AFLP) molecular markers. The maximum entropy method was used to model the past, present, and future environmental niches of the three clusters derived from the genetic analysis. The environmental niches of the three genetic clusters showed dynamic overlaps and isolations during the last interglacial and glacial maximum. The paleoclimatic events, which occurred during those periods, may have reinforced genetic exchange among populations and affected their genetic structure. Genetic clusters also presented different environmental niches in the present. Thus, they can be considered as three distinct ecotypes and restoration programs must be carried out using local germplasm from each environmental niche to increase their chance of success. Based on the environmental niches of the genetic clusters, changes are expected in the near and mid-century future. Therefore, climate change must be considered for species conservation management and future restoration programs.

The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

One of the fastest-growing renewable energy sources is solar energy. A strategic step for a well-performing solar project is site identification. The evaluation of site-suitability is a complex task, where multiple qualitative and quantitative criteria, inherent to the territory, are involved. In this study, a GIS-based multi-criteria decision-making (MCDM) methodology for site-suitability evaluation in the development of solar farms (DSF) is presented. Two scenarios, the ranking method (RM) and the Analytical Hierarchy Process (AHP), each representing a different weighting approach, were tested. A case study was performed for the Desert of Chihuahua, Mexico, a region with the potential to provide a significant portion of the country’s energy demand. The RM was more stringent and identified less area with high suitability (1237 km2) compared to the AHP (4983 km2). Given its flexibility in assigning weights, the AHP is considered to have greater potential in identifying site-suitability levels. The final suitability maps of the AHP showed the northern part of the study region to have high suitability for the DSF. Thus, sites in this area could be used for the construction of solar energy projects in the future. This methodology provides a useful tool for land-use planning based on its suitability level.

A key step in the processing of satellite imagery is the radiometric correction of images to account for reflectance that water vapor, atmospheric dust, and other atmospheric elements add to the images, causing imprecisions in variables of interest estimated at the earth’s surface level. That issue is important when performing spatiotemporal analyses to determine ecosystems’ productivity. In this study, three correction methods were applied to satellite images for the period 2010–2014. These methods were Atmospheric Correction for Flat Terrain 2 (ATCOR2), Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Dark Object Substract 1 (DOS1). The images included 12 sub-scenes from the Landsat Thematic Mapper (TM) and the Operational Land Imager (OLI) sensors. The images corresponded to three Permanent Monitoring Sites (PMS) of grasslands, ‘Teseachi’, ‘Eden’, and ‘El Sitio’, located in the state of Chihuahua, Mexico. After the corrections were applied to the images, they were evaluated in terms of their precision for biomass estimation. For that, biomass production was measured during the study period at the three PMS to calibrate production models developed with simple and multiple linear regression (SLR and MLR) techniques. When the estimations were made with MLR, DOS1 obtained an R2 of 0.97 (p < 0.05) for 2012 and values greater than 0.70 (p < 0.05) during 2013–2014. The rest of the algorithms did not show significant results and DOS1, which is the simplest algorithm, resulted in the best biomass estimator. Thus, in the multitemporal analysis of grassland based on spectral information, it is not necessary to apply complex correction procedures. The maps of biomass production, elaborated from images corrected with DOS1, can be used as a reference point for the assessment of the grassland condition, as well as to determine the grazing capacity and thus the potential animal production in such ecosystems.

A field characterization of the grassland vegetation structure, represented by the coverage of grass canopy (CGC) and the grass height, was carried out during three years (2009–2011) in a priority area for the conservation of grasslands of North America. Landsat Thematic Mapper (TM5) images were selected and the information of reflectance was obtained based on the geographical location of each field-sampling site. Linear models, constructed with field and satellite data, with high coefficients of determination for CGC (R2 = 0.81, R2 = 0.81 and R2 = 0.72) and grass height (R2 = 0.82, R2 = 0.79 and R2 = 0.73) were obtained. The maps showed a good level of CGC (>25%) and grass height (>25 cm), except for the year 2009, which presented the lowest values of grass height in the area. According to the Kappa Index, a moderate concordance among the three CGC maps was presented (0.49–0.59). Conversely, weak and moderate concordances were found among the grass height maps (0.36–0.59). It was observed that areas with a high CGC do not necessarily correspond to areas with greater grass height values. Based on the data analyzed in this study, the grassland areas are highly dynamic, structurally heterogeneous and the spatial distribution of the variables does not show a definite pattern. From the information generated, it is possible to determine those areas that are the most important for monitoring to then establish effective strategies for the conservation of these grasslands and the protection of threatened migratory bird species.

In this study, the transitions of land use that occurred in the urban and peripheral areas of Chihuahua City, Mexico, were determined for the period 1989–2014. Landsat TM and OLI scenes, as well as the method of Markov Chains (MC) were used. Grasslands and Shrublands were the land uses that experienced the highest pressures for land use. Grasslands occupied 23.5% of the area in 1989, decreasing to 16.01% in 2014. Likewise, Shrublands were reduced from 54.53% to 48.06%. The areas occupied by Croplands, Oak forest, Water bodies and Riparian vegetation stayed in general constant. In contrast, the urban area increased from 13.6% to 28.6% of the total area studied. In addition, projections of land use for 2019 and 2024 were generated through the method of MC and Cellular Automata (CA). According to the projections, validated with an agreement of 0.90, the Human settlements would continue to expand, occupying 38.57% by 2019 and almost half of the studied territory (47.33%) by 2024. The ecosystems with the highest pressure for land use change will continue to be the Grasslands and Shrublands. By 2024, the former would lose 15.8% while the latter would lose 16.7% of the area. These methods are valuable for urban planning and the results could support growth plans for Chihuahua City, Mexico, with a sustainable approach.

Los seres humanos han alterado sustancialmente la superficie, el patrón y la composición de la vegetación natural mundial, esto causado por una cada vez más creciente intervención humana en los paisajes naturales. Los procesos de deforestación de los bosques han ocasionado una disminución de la capacidad productiva y biodiversidad, fragmentando dichos ecosistemas. El objetivo central de este trabajo de investigación fue evaluar el grado de fragmentación de una porción de bosques templados ubicados en una microcuenca. Se tomaron como base los datos de imágenes de satélite de los sensores MSS y TM, se emplearon clasificaciones supervisadas para determinar coberturas de los años 1974, 1990, 2000 y 2011; se determinó la tasa de cambio durante el periodo total, así como la anual. Se utilizaron los índices de fragmentación de Simpson y Shannon. Los principales resultados encontrados determinan una buena precisión en las clasificaciones generadas para los diferentes periodos consecutivos 1974, 1990, 2000 y 2011, con valores Khat de 87%, 83%, 81% y 77% respectivamente, lo que indica una continua deforestación de las áreas de pino, perdiéndose en el periodo evaluado un total de 8,216 ha. Los índices de Simpson y Shannon muestran una tendencia a aumentar conforme se vuelve más complejo el ecosistema. Las técnicas de sensores remotos asociados a los índices de fragmentación representan metodologías precisas para la evaluación de las áreas forestales. Abstract Humans have substantially altered the surface, pattern and the composition of the world natural vegetation; this is caused by increasing human intervention in natural landscapes. The deforestation of forests has caused a decline in the productive capacity and biodiversity, fragmenting these ecosystems. The objective of this research was to evaluate the degree of fragmentation of a portion located in a temperate forest watershed. The data were based on satellite images of MSS and TM sensors, supervised classifications were used to determine coverage for the years 1974, 1990, 2000 and 2011, it was determined the rate of change of period and annual. Simpson and Shannon indices of fragmentation were used. The main results determine a good accuracy in the rankings generated for different consecutive periods 1974, 1990, 2000 and 2011, Khat values of 87%, 83%, 81% and 77% respectively, indicating continued deforestation of the pine areas, a total of 8,216 ha were lost in the period under review. The Simpson and Shannon indices show a tendency to increase as it becomes more complex the ecosystem. Remote sensing techniques associated with fragmentation indices represent accurate methodologies for evaluating forest areas. Keywords: fragmentation, remote sensing, Landsat TM, diversity index, error matrix, temperate forests.

El manejo, su condición y el conocimiento del potencial actual de los bosques templados en México es fundamental para la evaluación de estos ecosistemas. El uso de tecnologías actuales tales como los sensores remotos asociados a Sistemas de Información Geográfica (SIG), son herramientas muy precisas en la evaluación del potencial natural de bosques templados. El objetivo fue determinar el potencial natural de bosques templados en la región del ejido de Pueblo Nuevo, Durango. Se utilizó un Modelo Digital de Elevación (MDE), cartografía de suelos y una escena de satélite. A través de técnicas geoespaciales se crearon los mapas base de pendientes y exposiciones; una vez obtenida la información, y con la utilización de operadores matemáticos, se generó el mapa que ubicó las áreas con las condiciones ambientales adecuadas para el óptimo desarrollo de la especie (potencial natural). Los potenciales altos ocuparon una superficie de 5,969.38 ha, lo que comprende un 23.64% del área de estudio, el potencial medio 16,221.93 ha, con un 64.26% y el potencial bajo 3,054.65 ha, con un 12.10% de la superficie. Para la clasificación de la imagen de satélite Landsat TM, los bosques de pino-encino (Bpq) son los que mayor superficie ocuparon, con un 58.32% seguidos de bosque de encino-pino (Bqp) con un 23.97%, y áreas de pastizales (Ap) con 15.60%; finalmente, las áreas sin vegetación aparente (Asa) representaron un 2.10% del total del área. La exactitud global de la clasificación derivada del método supervisado para los datos de Landsat TM5 fue de 0.99, con un coeficiente Kappa (Khat) de 0.99, lo que indica que la clasificación es 99% mejor que la esperada por el simple azar. Estos resultados demuestran que el relieve y el suelo son factores determinantes en la distribución del potencial natural de bosques templados. Abstract Management, status and knowledge of the current potential of temperate forests in Mexico is essential for the assessment of these ecosystems. The use of current technologies such as remote sensing associated with Geographic Information Systems (GIS) are very precise tools in assessing the natural potential of temperate forests. The objective was to determine the natural potential of temperate forests in the region of Ejido Pueblo Nuevo, Durango. It was used a Digital Elevation Model (DEM), soil maps and a satellite scene. Through techniques were generated base maps of the slopes and exhibits geospatial after obtaining the information, and with the use of mathematical operators, it was generated a map to locate areas with suitable environmental conditions for the optimal development of the species (natural potential). The high potential occupied an area of 5,969.38 ha, which comprises a 23.64% of the study area, the average potential 16,221.93 ha, with 64.26% and the low potential 3,054.65 ha with 12.10% of the surface. For the classification of Landsat TM satellite image, the pine-oak forest (Bpq) are the largest area occupied, with 58.32% followed by oak-pine forest (Bqp) with 23.97% and grassland areas (Ap) with 15.60%, finally, the apparent nonvegetated areas (Asa) with 2.10% of total area. The overall accuracy of the classi- fication derived from the supervised method for Landsat TM5 data was 0.99, with a coefficient Kappa (Khat) of 0.99, indicating that the classification is 99% better than expected by chance alone. These results demonstrate that the relief and soil are determining factors in the distribution of natural potential of temperate forests. Keywords: Landsat, natural potential, classification, error matrix.

Ore mining has served as a predictor of economic wellbeing since it brought development to countries. However, these benefits do not always extend to all localities that comprised the center of this industry. This paper examined the contribution of mining to local communities. An index of local sustainability was constructed based on economic, social, and land-use data from twelve localities where mining and forestry are their major economic activities. Land-use variables were obtained from Landsat Thematic Mapper (TM 5) images for 2000, and Landsat Operational Land Imager (OLI8) for 2014, while the socio-economic variables were collected in twelve localities with an 85-question survey. A sustainability index was developed for each group of variables—economic (ESI), social (SSI) and land-use sustainability index (LUSI)—to further build a local sustainability index (LSI). Three localities showed the highest ESI (0.61, 0.53 and 0.43) and SSI (0.90, 0.79 and 0.78), while two localities had the lowest values in the ESI and SSI. In contrast, the highest value of LUSI was found in two other different localities and in one with lower SSI. Income from mining activities is positively associated with the ESI and SSI, but there was no evidence of linear association with the LUSI. A local index of sustainability provides useful information for planning and development strategies.

Water in arid and semi-arid environments is characterized by the presentation of complex interactions, where dissolved chemical species in high concentrations have negative effects on the water quality. Radon is present in areas with a high uranium and radium content, and it is the main contributor of the annual effective dose received by humans. The objective of this study was to evaluate concentrations of 222Rn and the water quality of spring waters. Water was classified as calcium sulfated and sodium sulfated. Most of the water samples with high radon concentrations presented higher concentrations of sulfates, fluorides, and total dissolved solids. 222Rn concentrations may be attributed to possible enhancement of 226Ra due to temperature and salinity of water, as well as evaporation rate. In 100% of the sampled spring waters the 222Rn levels exceeded the maximum acceptable limit which is proposed by international institutions. Aridity increases radiological risk related to 222Rn dose because spring waters are the main supply source for local populations. The implementation of environmental education, strategies, and technologies to remove the contaminants from the water are essential in order to reduce the health risk for local inhabitants.