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.

Introducción: Los sistemas biométricos forestales constituyen las herramientas analíticas más utilizadas para el análisis de la producción y el crecimiento de los bosques.Objetivo: Presentar un nuevo sistema biométrico para los bosques templados y tropicales de México.Materiales y métodos: El área de estudio comprendió los estados de Chihuahua, Guerrero, Jalisco, Oaxaca, Michoacán, Puebla, Estado de México, Hidalgo, Tlaxcala, Veracruz y Quintana Roo. La toma de datos de campo se realizó mediante muestreo destructivo y no destructivo en cada Unidad de Manejo Forestal Regional (UMAFOR) en los estados. La metodología utilizada permitió generar sistemas de ecuaciones para la estimación de atributos de árboles individuales que son aditivos entre componentes, escalables a nivel de árbol completo y consistentes a lo largo de las diversas condiciones forestales del país.Resultados y discusión: El Sistema Biométrico Forestal “SiBiFor” está integrado por más de 6 000 nuevas ecuaciones para 97 especies arbóreas de los bosques templados y tropicales. SiBiFor contiene 2 917 ecuaciones de volumen, 2 868 de ahusamiento-volumen, 341 de índice de sitio y 288 de crecimiento en diámetro.Conclusión: Las ecuaciones desarrolladas mejorarán el manejo de los ecosistemas forestales del país lo que contribuirá a la sustentabilidad de los mismos.

Soil erosion is considered one of the main degradation processes in ecosystems located in developing countries. In northern Mexico, one of the most important hydrological regions is the Conchos River Basin (CRB) due to its utilization as a runoff source. However, the CRB is subjected to significant erosion processes due to natural and anthropogenic causes. Thus, classifying the CRB’s watersheds based on their erosion susceptibility is of great importance. This study classified and then prioritized the 31 watersheds composing the CRB. For that, multivariate techniques such as principal component analysis (PCA), group analysis (GA), and the ranking methodology known as compound parameter (Cp) were used. After a correlation analysis, the values of 26 from 33 geomorphometric parameters estimated from each watershed served for the evaluation. The PCA defined linear-type parameters as the main source of variability among the watersheds. The GA and the Cp were effective for grouping the watersheds in five groups, and provided the information for the spatial analysis. The GA methodology best classified the watersheds based on the variance of their parameters. The group with the highest prioritization and erosion susceptibility included watersheds RH24Lf, RH24Lb, RH24Nc, and RH24Jb. These watersheds are potential candidates for the implementation of soil conservation practices.

Candelilla (Euphorbia antisyphilitica Zucc.) is a shrub species distributed throughout the Chihuahuan Desert in northern Mexico and southern of the United States of America. Candelilla has an economic importance due to natural wax it produces. The economic importance and the intense harvest of the wax from candelilla seems to gradually reduce the natural populations of this species. The essence of this research was to project the potential distribution of candelilla populations under different climate change scenarios in its natural distribution area in North America. We created a spatial database with points of candelilla presence, according to the Global Biodiversity Information Facility (GBIF). A spatial analysis to predict the potential distribution of the species using Maxent software was performed. Thirteen of 19 variables from the WorldClim database were used for two scenarios of representative concentration pathways (RCPs) (4.5 as a conservative and 8.5 as extreme). We used climate projections from three global climate models (GCMs) (Max Planck institute, the Geophysical Fluid Dynamics Laboratory and the Met Office Hadley), each simulating the two scenarios. The final predicted distribution areas were classified in five on-site possible candelilla habitat suitability categories: none (< 19%), low (20–38%), medium (39–57%), high (58–76%) and very high (> 77%). According to the area under the curve (0.970), the models and scenarios used showed an adequate fit to project the current and future distribution of candelilla. The variable that contributed the most in the three GCMs and the two RCPs was the mean temperature of the coldest quarter with an influence of 45.7% (Jackknife test). The candelilla’s distribution area for North America was predicted as approximately 19.1 million hectares under the current conditions for the high habitat suitability; however, the projection for the next fifty years is not promising because the GCMs projected a reduction of more than 6.9 million hectares using either the conservative or extreme scenarios. The results are useful for conservation of the species in the area with vulnerable wild populations, as well as for the selection of new sites suitable for the species growth and cultivation while facing climate change.

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.

Understanding soil moisture behavior in semi-dry forests is essential for evaluating the impact of forest management on water availability. The objective of the study was to analyze soil moisture based in storm observations in three micro-catchments (0.19, 0.20, and 0.27 ha) with similar tree densities, and subject to different thinning intensities in a semi-dry forest in Chihuahua, Mexico. Vegetation, soil characteristics, precipitation, and volumetric water content were measured before thinning (2018), and after 0%, 40%, and 80% thinning for each micro-catchment (2019). Soil moisture was low and relatively similar among the three micro-catchments in 2018 (mean = 8.5%), and only large rainfall events (>30 mm) increased soil moisture significantly (29–52%). After thinning, soil moisture was higher and significantly different among the micro-catchments only during small rainfall events (<10 mm), while a difference was not noted during large events. The difference before–after during small rainfall events was not significant for the control (0% thinning); whereas 40% and 80% thinning increased soil moisture significantly by 40% and 53%, respectively. Knowledge of the response of soil moisture as a result of thinning and rainfall characteristics has important implications, especially for evaluating the impact of forest management on water availability.