Environmental factors influence positively or negatively for the establishment of natural pine s regeneration, which may be linked to forest fires. Therefore, the objective of this study was to determine the environmental variables that influence the establishment of natural pines regeneration in impacted areas under three fire severity conditions (no fire, moderate fire and extreme fire) in pine - oak forests (Pinus lumholtzii BL Rob. & Fernald, Pinus devoniana Lindl., Pinus oocarpa Scheide, Quercus castanea Née, Quercus magnoliifolia Née) in the state of Jalisco (Mexico). For this purpose, different parameters of natural regeneration were evaluated: trees, live and dead fuels in circular sites of ~400 m2. Correlation analyses were performed with the data obtained to identify the most significant variables, and to use them in the stepwise regression analyses to identify predictor variables useful for the definition of the model for the estimation of natural pine regeneration in areas affected by fire. The analyzes showed that the "stepwise" process presents the best model with an R2 of 0.6837 and an AIC of 568.58, selecting the following variables: dry weight of herbs, height of shrubs, crown diameter of shrubs and grasses, 10 hour fuels and exposure. Subsequently, considering the mean square of the error as the main criterion, a comparison was made of the models resulting from the combination of these variables, with the best model excluding the dry weight of herbs. It is concluded that the establishment of the natural regeneration of pine is associated with certain variables of ground cover, understory and relief.
La problemática de incendios forestales conlleva a priorizar áreas de atención con base en criterios como el riesgo, para ubicarlas y dimensionarlas en cartografía temática específica; cuya clasificación implica: a) selección del número de clases de riesgo; y b) proceso para establecer los intervalos de clases. No obstante, esto varía dependiendo de la apreciación de quien especifica la clasificación; para evitarlo, se hace un análisis comparativo entre dos números de clases (3 y 5) y las siguientes alternativas de división de intervalos entre las clases: Intervalos iguales; Cuantiles; Rupturas naturales; e Intervalos geométricos. Se usó un mapa de riesgo de incendios forestales del estado de Jalisco (México), en el cual para definir el número de clases y el método para establecer los intervalos, se ubicaron al azar 1 000 sitios de validación (SV). Alrededor de ellos se delimitó una superficie de 100 km2, para contar el número de incendios forestales del período 2005-2014. Así, se asoció la clase de riesgo que correspondía a cada uno de los SV con el número de incendios ubicados en el área de 100 km2. A partir de esto, se comparó la variabilidad (desviación estándar) entre las clases generadas por cada uno de los cuatro métodos para definir sus intervalos. Los resultados sugieren que el método de intervalos iguales (II) es el más indicado para definir los intervalos de clase de riesgo de incendios. Referente al número de clases, existe una más clara diferenciación, entre las clases, al usar cinco clases.
The problem of forest fires requires creating methodologies that allow evaluating and predicting the response that the ecosystem willhave to the impact of fire, in order to direct restoration actions in the areas that most require it. However, evaluating these areasdirectly in the field implies investment of resources (financial and personnel) which, along with time, are generally limited. For this,satellite images are a practical tool for the evaluation of large areas, or inaccessible areas, impacted by forest fires. In this work, thecorrelation presented by different variables measured in the field and derived from remote sensors, in relation to the naturalregeneration of pine that occurs in the La Primavera forest and in Sierra de Quila, Jalisco, was evaluated. The results showeddifferent variables to determine the predictive models of the natural regeneration of pine after the occurrence of a forest fire, beingthe fuels of 100 hours and 1000 hours, bark thickness and depth of burning, the variables taken directly in the field. that wereincluded in the models. While the burn area index, the regeneration index and the exposure, the variables taken by remote censorswere included in the predictive models. The models that showed a higher R² are those obtained by field variables for the tworegions. However, the model obtained only with remote sensor variables for La Primavera obtained an R² of 0.6083, Contrary toSierra de Quila where the model does not take any spectral index for the model, therefore it is advisable to establish a greaternumber of Sampling sites evenly distributed throughout the area affected by the fire, to improve the accuracy of the remote sensingmodels
Los efectos que tienen los incendios en los ecosistemas forestales son variables, dependiendo de diversos factores entre los cuales se encuentra la severidad del fuego. Lo cual, a su vez, repercute en su recuperación. Sin embargo, evaluar áreas afectadas por fuego directamente en campo implica alta inversión de recursos que, junto con el tiempo, son generalmente limitados. No obstante, para la planeación de las estrategias de manejo y de restauración es necesario tener conocimiento del impacto del fuego. Para esto, los sensores remotos son una herramienta práctica para la evaluación de grandes áreas, o áreas inaccesibles, impactadas por incendios forestales. Cuyo uso va en aumento, siguiendo diferentes perspectivas de evaluación, como son el espectro infrarrojo, la detección de vegetación, ubicación de cenizas, etc. Por lo que para saber cuál es la mejor alternativa en el estudio de incendios forestales, es necesario conocer toda la gama de posibilidades y de esta manera poder elegir la más conveniente. Debido a esto, en este trabajo se hace una revisión de diferentes propuestas de evaluación de áreas impactadas por incendios forestales a través de sensores remotos. Las cuales se definen, principalmente, en una serie de índices espectrales, con base a los cuales, directa o indirectamente, se pretende no solo ubicar y dimensionar los incendios forestales, sino, en algunos casos, determinar el nivel de severidad. De esta forma, en este documento se agrupan las principales propuestas, con base a sus objetivos de detección de áreas impactadas: vegetación, suelo, agua, área quemada y radar.
Forest fires alter ecosystem processes and interactions. However, not all fires are the same, some are more severe than others,their impact on the vegetation varying according to the degree of severity. Therefore, it is important to have information thatallows sizing these impacts and supports the implementation of management strategies. However, monitoring the impact of fireis not an easy task, much less monitoring the response of the ecosystem over the years. Accordingly, the objective of this workwas to implement satellite technology to monitor the dynamics of forest cover recovery in areas impacted by forest fires, inorder to direct ecosystem restoration strategies. For this, the difference (before and after the fire) of the normalized burningratio (dNBR) was defined to determine the severity of a fire that occurred in the Sian Ka'an Biosphere Reserve in 2019.Subsequently, the dynamic of ecosystem recovery was evaluated, through the analysis of a series of images, using thefollowing indexes: a) Normalized Difference Vegetation Index (NDVI); b) Normalized burn ratio (NBR); c) NormalizedThermal Burnup Ratio (NBRT); d) Burned Area Index (BAI). A change in the reflectance value was observed in the burnedarea after the fire, which was restored over time. However, although the values show a recovery of the vegetation, in the fieldthe ecosystem is made up of different species from those that were present before the fire, therefore, by means of the spectralsignature of the vegetation, an image was generated showing the difference in the density of vegetation, which favors theunderstanding of the effects of fire and helps define priority areas to direct restoration actions.
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