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黑龙江省森林火灾年际变化的小波分析 被引量:7
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作者 田晓瑞 李红 +1 位作者 舒立福 王明玉 《自然灾害学报》 CSCD 北大核心 2007年第2期118-121,共4页
小波分析是一种新的时一频分析工具,广泛适用于非线性科学领域。选用Daubechies小波系对黑龙江省森林火灾的特征进行了分解和重构,结果表明,黑龙江省1953—2002年受害森林面积有近15~20a的周期波动性,森林火灾次数则有近30a的周期... 小波分析是一种新的时一频分析工具,广泛适用于非线性科学领域。选用Daubechies小波系对黑龙江省森林火灾的特征进行了分解和重构,结果表明,黑龙江省1953—2002年受害森林面积有近15~20a的周期波动性,森林火灾次数则有近30a的周期波动性。未来几年内,该省森林火灾受害面积和火灾次数会在较低的水平上波动,但森林火灾可能要比2000年严重。 展开更多
关键词 小波分析 森林火烧面积 火灾次数 黑龙江省
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基于MODIS-EVI2与集成学习的森林火烧迹地面积预测
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作者 冯俊辰 董昊 +3 位作者 韩鹏 李远斌 刘靖宇 丁云鸿 《遥感技术与应用》 CSCD 北大核心 2024年第5期1261-1270,共10页
在林火救援中,根据火灾早期阶段预测最终燃烧面积,可有效指导火灾救援。然而,以往研究采用归一化差值植被指数(Normalized Difference Vegetation Index,NDVI)作为输入指标,其对土壤反射率敏感,数据噪声大。因此两波段增强植被指数(Two-... 在林火救援中,根据火灾早期阶段预测最终燃烧面积,可有效指导火灾救援。然而,以往研究采用归一化差值植被指数(Normalized Difference Vegetation Index,NDVI)作为输入指标,其对土壤反射率敏感,数据噪声大。因此两波段增强植被指数(Two-band Enhanced Vegetation Index,EVI2)被使用以准确预测野火过火面积。此外,针对单一机器学习预测算法抗干扰能力差的问题,一种基于堆叠泛化(Stacking)集成学习的Stacking-XRSK模型被提出。结果表明:使用EVI2使模型R^(2)较NDVI提高6.05%,MAE和MSE分别降低0.88%和0.41%。相比于单一模型,使用Stacking-XRSK模型的R^(2)最高,高出范围在2.8%~11.06%之间,MAE、MSE和AOC最低。验证了利用EVI2代替NDVI预测火烧迹地面积的可行性和准确性,同时表明Stacking模型能在充分发挥单一基模型优势的基础上提高模型的泛化能力,为森林火灾安全管理与及时扑救提供科学的参考。 展开更多
关键词 森林火烧迹地面积 两波段增强植被指数 预测模型 集成学习 机器学习
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An Insight into Spatial-Temporal Trends of Fire Ignitions and Burned Areas in the European Mediterranean Countries
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作者 Marcos Rodrigues Jesfis San Miguel +2 位作者 Sandra Oliveira Francisco Moreira Andrea Camia 《Journal of Earth Science and Engineering》 2013年第7期497-505,共9页
This paper presents an analysis of the fire trends in southern European countries, where forest fires are a major hazard. Data on number of fires and burned area size from 1985 until 2009 were retrieved from the Europ... This paper presents an analysis of the fire trends in southern European countries, where forest fires are a major hazard. Data on number of fires and burned area size from 1985 until 2009 were retrieved from the European Fire Database in the European Forest Fire Information System and used to study the temporal and spatial variability of fire occurrence at three different spatial scales: the whole European Mediterranean region, country level and province level (NUTS3). The temporal trends were assessed with the Mann-Kendall test and Sen's slope in the period 1985-2009. At regional (supranational) level, our results suggest a significant decreasing trend in the burned area for the whole study period. At country level, the trends vary by country, although there is a general increase in number of fires, mainly in Portugal, and a decrease in bumed areas, as is the case of Spain. A similar behavior was found at NUTS3 level, with an increase of number of fires in the Spanish and Portuguese provinces and a generalized decrease of the burned area in most provinces of the region. These results provide an important insight into the spatial distribution and temporal evolution of fires, a crucial step to investigate the underlying causes and impacts of fire occurrence in this region. 展开更多
关键词 Fire ignition burned area WILDFIRE trend test Mann-Kendall.
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Extreme fire weather is the major driver of severe bushfires in southeast Australia 被引量:2
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作者 Bin Wang Allan C.Spessa +14 位作者 Puyu Feng Xin Hou Chao Yue Jing-Jia Luo Philippe Ciais Cathy Waters Annette Cowie Rachael H.Nolan Tadas Nikonovas Huidong Jin Henry Walshaw Jinghua Wei Xiaowei Guo De Li Liu Qiang Yu 《Science Bulletin》 SCIE EI CSCD 2022年第6期655-664,M0004,共11页
In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous max... In Australia,the proportion of forest area that burns in a typical fire season is less than for other vegetation types.However,the 2019-2020 austral spring-summer was an exception,with over four times the previous maximum area burnt in southeast Australian temperate forests.Temperate forest fires have extensive socio-economic,human health,greenhouse gas emissions,and biodiversity impacts due to high fire intensities.A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia.Here,we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001-2020 on a 0.25°grid based on several biophysical parameters,notably fire weather and vegetation productivity.Our model explained over 80%of the variation in the burnt area.We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather,which mainly linked to fluctuations in the Southern Annular Mode(SAM)and Indian Ocean Dipole(IOD),with a relatively smaller contribution from the central Pacific El Niño Southern Oscillation(ENSO).Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season,and model developers working on improved early warning systems for forest fires. 展开更多
关键词 Remote sensing Forest fires Climate drivers Burnt area modelling Machine learning Southeast Australia
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