当前位置:
X-MOL 学术
›
Geomat Nat. Hazards Risk
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal
Geomatics, Natural Hazards and Risk ( IF 4.2 ) Pub Date : 2020-01-01 , DOI: 10.1080/19475705.2020.1853251 Ashok Parajuli 1 , Ambika Prasad Gautam 2 , Sundar Prasad Sharma 3 , Krishna Bahadur Bhujel 2 , Gagan Sharma 4 , Purna Bahadur Thapa 2 , Bhuwan Singh Bist 5 , Shrijana Poudel 6
Geomatics, Natural Hazards and Risk ( IF 4.2 ) Pub Date : 2020-01-01 , DOI: 10.1080/19475705.2020.1853251 Ashok Parajuli 1 , Ambika Prasad Gautam 2 , Sundar Prasad Sharma 3 , Krishna Bahadur Bhujel 2 , Gagan Sharma 4 , Purna Bahadur Thapa 2 , Bhuwan Singh Bist 5 , Shrijana Poudel 6
Affiliation
Abstract Forest fires have increased at an alarming rate in recent years, with multiple consequences in Nepal's forest ecosystem and landscapes. The research used remote sensing and GIS technology as well as statistical tools for developing forest fires risk models in two major landscapes of Nepal, i.e., Terai Arc Landscape (TAL) and Chitwan Annapurna Landscape (CHAL). A multi-parametric weighted index model was adopted to derive and demarcate the forest fire-risk map with risk variables such as vegetation, topographic factors, land surface temperature, and proximity to the road and settlements. To enhance the use of a fire risk map, collinearity between variables was checked (VIF <2) and validated with the Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots and Kernel Density Estimation (KDE) method. The MODIS hotspot data from 2001 to 2018 was also evaluated which indicates that the number of fire counts has a strong relation (R2 =0.82) with the burn area. Broadleaved forest in the pre-monsoon season is highly vulnerable to forest fire. More than half of the total forested area (65%) is in high fire risk, particularly in the TAL region. The study results could assist the decision-makers to implement preventive measures by minimizing the risk and impacts of forest fires.
中文翻译:
使用地理信息系统和遥感在尼泊尔两大景观中绘制森林火灾风险图
摘要 近年来,森林火灾以惊人的速度增加,对尼泊尔的森林生态系统和景观造成了多重后果。该研究使用遥感和 GIS 技术以及统计工具在尼泊尔的两个主要景观中开发森林火灾风险模型,即 Terai Arc Landscape (TAL) 和 Chitwan Annapurna Landscape (CHAL)。采用多参数加权指数模型,利用植被、地形因素、地表温度以及与道路和居民点的接近程度等风险变量来推导和划分森林火灾风险图。为了加强火灾风险图的使用,检查了变量之间的共线性 (VIF <2) 并使用中分辨率成像光谱仪 (MODIS) 热点和核密度估计 (KDE) 方法进行验证。还评估了 2001 年至 2018 年的 MODIS 热点数据,表明火灾计数与燃烧面积有很强的相关性(R2 = 0.82)。季风前季节的阔叶林极易受到森林火灾的影响。森林总面积的一半以上 (65%) 处于高火灾风险中,特别是在 TAL 地区。研究结果可以帮助决策者通过最小化森林火灾的风险和影响来实施预防措施。
更新日期:2020-01-01
中文翻译:
使用地理信息系统和遥感在尼泊尔两大景观中绘制森林火灾风险图
摘要 近年来,森林火灾以惊人的速度增加,对尼泊尔的森林生态系统和景观造成了多重后果。该研究使用遥感和 GIS 技术以及统计工具在尼泊尔的两个主要景观中开发森林火灾风险模型,即 Terai Arc Landscape (TAL) 和 Chitwan Annapurna Landscape (CHAL)。采用多参数加权指数模型,利用植被、地形因素、地表温度以及与道路和居民点的接近程度等风险变量来推导和划分森林火灾风险图。为了加强火灾风险图的使用,检查了变量之间的共线性 (VIF <2) 并使用中分辨率成像光谱仪 (MODIS) 热点和核密度估计 (KDE) 方法进行验证。还评估了 2001 年至 2018 年的 MODIS 热点数据,表明火灾计数与燃烧面积有很强的相关性(R2 = 0.82)。季风前季节的阔叶林极易受到森林火灾的影响。森林总面积的一半以上 (65%) 处于高火灾风险中,特别是在 TAL 地区。研究结果可以帮助决策者通过最小化森林火灾的风险和影响来实施预防措施。