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The use of remote sensing in natural resource management is an easily accessible input for obtaining detailed information on the ground and landscape. There is a wide range of procedures to analyze the forest canopy through satellite images. The purpose of this work is to obtain a map of forest opening with remote sensing by relating several vegetation indices, Kauth-Thomas transformation and texture filters, to a Landsat 8OLI image. A factor analysis was made to evaluate the contribution of these variable to identify the opening of the forest cover, yielding a σ2 = 76%. The results show that the Modified Soil Adjusted Vegetation Index (MSAVI), Soil Adjusted Vegetation Index (SAVI), and brightness factor have the best correlation (0.225-0.216 component coefficient). The resulting model was reclassified into five categories of forest opening and associated with land use data from the National Institute of Statistics and Geography (INEGI-México). Thus, 95% of human settlements have a canopy opening between medium and very high, the crops areas 72%, and the low deciduous forest with secondary shrub vegetation 100% of the opening. Coniferous and mixed forests have a low to very low canopy opening 46% and 55%, respectively of their surface. The forests with secondary vegetation, both shrub and arboreal, present greater openness than the same forests in the primary state. Verification of the spatial representation data of canopy opening was made by comparing 94 hemispheric photographs with 94 sites located in open areas obtaining an r = 0.57. This work offers a simple and straightforward methodology, easily replicable in different types of vegetation using free satellite imagery. Hence, it is a helpful tool for decision-makers when considering the general status of conservation of forest systems and their spatial distribution.

. An ordination analysis of vegetation and flora of Los Cabos Region, Baja California Peninsula, Mexico and its relationships with their environment and geomorphic characteristics was studied at 38 sites, selected based on ecoregion mapping units (EMU) previously delineated for the study area. Multivariate methods were used to analyse the relationship between physical characteristics of the environment and the two primary vegetation types, the Dry tropical forest (DTF) and the Xerophilous scrubland (XS), which together represent 83% of the vegetation cover in the study area. Sites were grouped based on both physical characteristics and relative abundance of plant species. The results of the statistical analysis suggest that altitude, rainfall, temperature, and lithology can help explaining differences between these important vegetation types. In the case of XS, the morpho‐physiognomic characteristics of vegetation in each site discriminated between two scrub types.

An assessment of the vulnerability of forest ecosystems in Mexico to climate change is carried out on the basis of the scenarios projected by 3 climate models. A vegetation classification was performed according to 2 models, the Holdridge Life Zone Classification and the so-called Mexican Classification (a climate-vegetation classification based on typologies developed for Mexico). Projections of climate models were based on a doubled CO₂ concentration condition. The models used were: the CCCM, which estimates an average increase in temperature for the country of 2.8°C and a decrease in annual precipitation of 7%; the GFDL-R30, which estimates an increase in both parameters by 3.2°C and 20% respectively; and a sensitivity model in which a homogeneous increase of 2°C in temperature and a 10% decrease in precipitation are applied throughout the country. In general, the cool temperate and warm temperate ecosystems were the most affected and tended to disappear under the conditions of the 3 scenarios. In contrast, the dry and very dry tropical forests and the warm thorn woodlands tended to occupy larger areas than at present, particularly under the conditions projected by the CCCM model. However, under the GFDL-derived scenario an increase in the distribution of moist and wet forests, which would be favoured by an increase in precipitation, was predicted.

An ordination analysis of vegetation and flora of Los Cabos Region, Baja California Peninsula, Mexico and its relationships with their environment and geomorphic characteristics was studied at 38 sites, selected based on ecoregion mapping units (EMU) previously delineated for the study area. Multivariate methods were used to analyse the relationship between physical characteristics of the environment and the two primary vegetation types, the Dry tropical forest (DTF) and the Xerophilous scrubland (XS), which together represent 83% of the vegetation cover in the study area. Sites were grouped based on both physical characteristics and relative abundance of plant species. The results of the statistical analysis suggest that altitude, rainfall, temperature, and lithology can help explaining differences between these important vegetation types. In the case of XS, the morpho-physiognomic characteristics of vegetation in each site discriminated between two scrub types. Abbreviations: DTF = Dry tropical forest; XS = Xerophilous scrubland.

PM2.5 and arsenic are two of the most hazardous substances for humans that coexist worldwide. Independently, they might cause multiple organ damage. However, the combined effect of PM2.5 and arsenic has not been studied. Here, we used an animal model of simultaneous exposure to arsenic and PM2.5. Adult Wistar rats were exposed to PM2.5, As, or PM2.5 + As and their corresponding control groups. After 7, 14, and 28 days of exposure, the animals were euthanized and serum, lungs, kidneys, and hearts were collected. Analysis performed showed high levels of lung inflammation in all experimental groups, with an additive effect in the coexposed group. Besides, we observed cartilaginous metaplasia in the hearts of all exposed animals. The levels of creatine kinase, CK-MB, and lactate dehydrogenase increased in experimental groups. Tissue alterations might be related to oxidative stress through increased GPx and NADPH oxidase activity. The findings of this study suggest that exposure to arsenic, PM2.5, or coexposure induces high levels of oxidative stress, which might be associated with lung inflammation and heart damage. These findings highlight the importance of reducing exposure to these pollutants to protect human health.