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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 R 2 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.

árboles en un rodal natural con presencia de muérdago enano: nueve sanos y nueve enfermos; con edades entre 30 y 40 años a los cuales se les realizaron análisis troncales para reconstruir su historial de crecimiento. Se consideró el grado de infección 6, con base en el sistema de clasificación de Hawksworth. Se probaron las ecuaciones de Shumacher y Chapman-Richards a fin de seleccionar el modelo más adecuado para la descripción del fenómeno. El modelo de Shumacher presentó mejores ajustes en la predicción del crecimiento. La infestación por muérdago enano lo redujo 22 % en altura, 9 % en diámetro y 50 % en volumen; además afectó en porcentajes similares al incremento corriente anual e incremento medio anual, lo cual disminuye el volumen de madera producido y aumenta el turno técnico maderable del arbolado, en un promedio de 10 años. Los resultados sugieren la aplicación de prácticas de prevención y combate del parásito, así como el establecimiento de sitios de monitoreo continuo para la generación de conocimiento sobre la relación parásito-hospedero. AbstractAlthough mistletoes are abundant in Mexico, very little is known about their behavior on different hosts. There is some work on damage assessments and less on the behavior of trees infested with varying degrees of attack. The aim of this study was to determine the effect of Arceuthobium vaginatum subsp. vaginatum in height, diameter and volume of Pinus hartwegii in the Nevado de Colima Volcano National Park. 18 trees were chosen in a natural stand in the presence of dwarf mistletoe: nine sick and nine healthy, aged between 30 and 40 years to which stem analyzes were performed to reconstruct their history of growth. Based on the classification system of Hawksworth, the degree of infection was considered 6. Schumacher and Chapman-Richards equations to find the most appropriate model for the description of the phenomenon were tested. The first one showed better fit in predicting growth. Dwarf mistletoe infestation reduced it at 22 % in height, 9 % in diameter and 50 % in volume; it also affected by similar percentages the current annual increment and the mean annual increment, which lowers the volume of timber produced and the wooden technical rotation increases, at an average of 10 years. These results suggest the application of practices to prevent and combat the parasite and the establishment of continuous monitoring sites for the generation of knowledge about the host-parasite relationship.

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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.

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