模拟加纳阿哈福地区Bosomkese森林保护区的森林火灾风险

IF 0.3 Q4 REMOTE SENSING
Adams Elias Dadzie, A. Mary
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引用次数: 2

摘要

森林火灾是现实生活中的一种破坏性现象,造成了巨大的生命、财产和生态损失。本文提出了一种识别、分类和绘制森林火灾危险区的风险评估模型。该模型考虑了四个风险模型,即点火模型、检测模型、响应模型和燃料模型分析。第一个模型集中于森林火灾中的人为影响因素,包括土地利用、与道路的距离和与定居点的距离,第二个模型由从道路和定居点的角度来看火灾能见度的可能性组成。森林火灾响应包括与消防站的距离和运动阻力是第三个模型。作为第四个模型,对燃料的类型(干或湿)、燃料含水量、森林植被的健康状况和该地区的地形进行了分析。研究结果表明,高风险区面积38.8平方公里,占森林总面积的25.6%。研究结果有助于开发森林火灾管理系统。管理层可以使用快速和适当的方向来有效地阻止火灾的蔓延。它还有助于提供保护森林免受火灾影响的有效手段,并制定适当的方法来控制和管理森林火灾的破坏及其蔓延。工作结束时,提出了实施消防塔、断线和使用无人机等现代探测技术来改进火灾探测和响应的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling the risk of forest to fire for the Bosomkese Forest Reserve, Ahafo Region, Ghana
Forest fire is a devastating phenomenon in real life, causing huge losses of lives, properties and ecologies. A risk assessment model to identify, classify and map forest fire risk areas is presented in this paper. This model considers four risk models, i.e. ignition model, detection model, response model and fuel model analysis. The first model concentrates on human influence factors in forest fires, including the land use, distance from roads, and distance from settlements and the second model is made up of the possibility of fire visibility from road and settlement viewpoint. The forest fire response included distance from fire stations and motion resistance is the third model. The type of fuel (dry or wet), fuel moisture content, health of the forest vegetation and topography of the area were analysed as the fourth model. The study results indicate that very high-risk zones covered 38.8km2 representing 25.6% of the total forest area. Findings of the research are helpful in developing forest fire management systems. Fast and appropriate direction could be used by management to stop the spread of fire effectively. It also helps to provide effective means for protecting forests from fires as well as to formulate appropriate methods to control and manage forest fire damages and its spread. Recommendations were made at the end of the work to implement fire towers, break lines and employ the use of modern detection techniques such drones, etc to improve fire detection and response.
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