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Review of methods for modelling forest fire risk and hazard

机译:审查森林火灾风险和危害的建模方法

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Forest fires (wildfires) have become a major concern for several environmental experts. Assessment of fire effects at local scale is increasingly considered a critical aspect of ecosystem functioning, since fire plays a crucial role in vegetation composition, biodiversity, soil erosion and the hydrological cycle. At global scale, fire is the most generalized means of transforming tropical forest in agricultural areas, and it has severe impacts on global atmospheric chemistry. Fire is a natural factor in many climates with high levels of vegetation stress. However, changes in traditional land use such as hunting, charcoal production, inefficient logging practices and rural abandonment patterns, which have been identified as major causes of wild fires, have recently modified the incidence of fire. Several assessment techniques and methods have been developed to help model and evaluate forest fire risk and hazard. There is the need to identify a method or combination of methods to help model forest fire risk and hazard to enable the sustainability of the natural resources. In this paper, the various methods are reviewed in order to enhance the use of appropriate method(s) for forest fire risk and hazard management. From the review and deductions of the methods, it was concluded that spatial multi-criteria modelling and evaluation (SMCME) of fire risk and hazard is preferred. It was also deduced that combination of SMCME with other methods has proven to be more efficient and effective when compared with the use of individual methods.
机译:森林火灾(野火)已成为一些环境专家的主要关注点。由于火灾在植被组成,生物多样性,水土流失和水文循环中起着至关重要的作用,因此在地方范围内评估火灾的影响日益被认为是生态系统功能的关键方面。在全球范围内,火灾是在农业地区改造热带森林的最普遍手段,它对全球大气化学产生了严重影响。在许多植被压力高的气候中,火灾是自然因素。但是,传统土地用途的变化,例如狩猎,木炭生产,低效率的伐木方式和农村弃置模式,这些已被确定为野火的主要原因,最近改变了火灾的发生率。已经开发了几种评估技术和方法来帮助建模和评估森林火灾的风险和危害。需要确定一种方法或方法的组合来帮助对森林火灾的风险和危害进行建模,以实现自然资源的可持续性。在本文中,对各种方法进行了综述,以加强对森林火灾风险和危害管理的适当方法的使用。从方法的回顾和推论中得出结论,火灾风险和危害的空间多标准建模和评估(SMCME)是首选的。还推断出,与使用单个方法相比,SMCME与其他方法的组合已被证明更加有效。

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