基于地理加权泊松模型的河南省火灾风险模拟

王周; 金万洲

doi:10.3969/j.issn.1000-2006.2015.05.015

王周,金万洲

南京林业大学学报（自然科学版） ›› 2015, Vol. 39 ›› Issue (05) : 93-98.

PDF(2529593 KB)

国家林草科技领军期刊
中国精品科技期刊
中国高校百佳科技期刊
江苏省新闻出版政府奖期刊奖
RCCSE林学权威期刊（A+）
CSCD核心期刊
Scopus数据库收录期刊
中文核心期刊
SCD核心期刊

作者加群：102861116

微信公众号：南京林业大学学报

高级检索

PDF(2529593 KB)

南京林业大学学报（自然科学版） ›› 2015, Vol. 39 ›› Issue (05) : 93-98. DOI: 10.3969/j.issn.1000-2006.2015.05.015

研究论文

基于地理加权泊松模型的河南省火灾风险模拟

王周,金万洲

作者信息 +

Fire danger modeling with Geographically Weighted Poisson Model in Henan Province

WANG Zhou, JIN Wanzhou

Author information +

文章历史 +

摘要

火灾风险模拟是探究火灾风险机制和揭示影响火灾主要因素的重要途径。使用可解释火灾及其影响因素间局部变异关系的地理加权泊松模型为建模工具,选取坡度、人类活动可达性、太阳辐射、地表温度、植被状态等火环境因素,对河南省2002—2012年的火灾风险进行空间模拟,并分别从可靠性和区分能力两方面对模型进行检验。结果表明:①与广义泊松模型相比,地理加权泊松模型的模拟精度和模型性能均显著提高; ②人类活动可达性对火灾的影响在河南省内未呈现显著空间变化,地形、太阳辐射、地表温度和植被状态对火灾影响在河南省内表现出显著空间变化,邻近居民区和远离主要道路的区域更易发生火灾; ③火灾影响因素的估计系数图可用于防火管理。

Abstract

Fire danger modeling is one of the most important ways to explore the mechanism of fire occurrence and to discover the main fire-influencing factors. Geographically Weighted Poisson Model(GWPM)can account for local variation of the relationships between fire hazards and fire-influencing factors was applied to model monthly fire frequency cell by cell for fire season of Henan province(i.e. September and October). Several fire-influencing factors involving slope, accessibility, solar radiation, land surface temperature and vegetation conditions were employed and potential multicollinearity among the independent variables was excluded in modeling. The reliability and discrimination capacity of the developed GWPM fire models were respectively evaluated by using the inner testing subset and the independent validation subset. The results indicate that:① the fitting accuracy and the model's performance are greatly improved by GWPM compared to Generalized Poisson Model(GPM). ② Influences of factors present significant spatial variability, accounting for slope, solar radiation, land surface temperature and vegetation conditions on fire hazards but the influences of factors accounting for accessibility of human activity on fire hazards exhibit insignificant spatial variability. In addition, those areas which were located far away from main roads and near villages are fire-prone zones. The estimated coefficients maps have important implications for fire prevention management.

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用

王周,金万洲. 基于地理加权泊松模型的河南省火灾风险模拟[J]. 南京林业大学学报（自然科学版）. 2015, 39(05): 93-98 https://doi.org/10.3969/j.issn.1000-2006.2015.05.015

WANG Zhou, JIN Wanzhou. Fire danger modeling with Geographically Weighted Poisson Model in Henan Province[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2015, 39(05): 93-98 https://doi.org/10.3969/j.issn.1000-2006.2015.05.015

中图分类号： X43

参考文献

[1] Ardakani A S, Valadan Zoej M J, Mohammadzadeh A, et al. Spatial and temporal analysis of fires detected by MODIS data in Northern Iran from 2001 to 2008[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(1): 216-225.
[2] 王超,高红真,程顺,等. 塞罕坝林区森林可燃物含水率及火险预报[J]. 林业科技开发, 2009,33(3):59-62. Wang C, Gao H Z, Cheng S. Study on water content of forest fuel and forest fire risk prediction in Saihanba forest area[J]. China Forestry Science and Technology, 2009, 33(3): 59-62.
[3] 张海军,戚鹏程.基于频率比和逻辑回归模型的东北地区火险制图研究[J].地理与地理信息科学,2012,28(5):35-38,42. Zhang H J, Qi P C. Mapping fire occurrence susceptibility in Northeast China: comparison of frequency ratio and binary logistic regression[J]. Geography and Geo-information Science, 2012, 28(5): 35-38,42.
[4] 张海军,白景锋.空间抽样对东北地区逻辑火险建模的影响[J].实验室研究与探索,2014,33(5):20-25. Zhang H J, Bai J F. Influences of different spatial sampling programs on logistic fire danger modeling—a case study in for Northeast China[J]. Research and Exploration in Laboratory, 2014, 33(5): 20-25.
[5] Zhang H J, Han X Y, Dai S. Fire occurrence probability mapping of Northeast China with binary logistic regression model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(1): 121-127.
[6] 肖化顺,张贵,蔡学理. 基于模糊数据挖掘技术的林火行为预测研究[J]. 南京林业大学学报:自然科学版,2006, 30(4): 97-100. Xiao H S, Zhang G, Cai X L. Predicting forest fire behavior based on fuzzy data mining technique[J]. Journal of Nanjing Forestry University: Natural Sciences Edition, 2006, 30(4):97-100.
[7] Bisquert M, Caselles E, Sánchez J M, et al. Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data[J]. International Journal of Wildland Fire, 2012, 21(8): 1025-1029.
[8] Dlamini W M. Application of Bayesian networks for fire risk mapping using GIS and remote sensing data[J]. GeoJournal, 2011, 76(3): 283-296.
[9] Prestemon J P, Chas-Amil M L, Touza J M, et al. Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations[J]. International Journal of Wildland Fire, 2012, 21(6): 743-754.
[10] Koutsias N, Martínez-fernández J, Allgöwer B. Do factors causing wildfires vary in space? Evidence from geographically weighted regression[J]. GIScience & Remote Sensing, 2010, 47(2): 221-240.
[11] Zhang H J, Qi P C, Guo G M. Improvement of fire danger modelling with geographically weighted logistic model[J]. International Journal of Wildland Fire, 2014, 23(8): 1130-1146.
[12] Nakaya T, Fotheringham A S, Brunsdon C, et al. Geographically weighted Poisson regression for disease association mapping [J]. Statistics in Medicine, 2005, 24(17): 2695-2717.
[13] Hadayeghi A H, Shalaby A S, Persaud B N, et al. Development of planning level transportation safety tools using Geographically Weighted Poisson Regression[J]. Accident Analysis and Prevention, 2010, 42(2): 676-688.
[14] 郭其乐,陈怀亮,邹春辉,等.河南省近年来遥感监测的森林火灾时空分布规律分析[J].气象与环境科学,2009,32(4):29-32. Guo Q L, Chen H L, Zou C H, et al. Analysis of the laws of spatial and temporal distribution of remote sensing monitoring forest fires of Henan Province in recent years[J]. Meteorological and Environmental Sciences, 2009, 32(4): 29-32.
[15] 景国勋,王卫敏,温宏民.基于BP神经网络的河南省火灾风险评价[J].中国安全科学学报,2007,17(8):16-19. Jing G X, Wang W M, Wen H M. Risk assessment of fire accidents in Henan Province of China based on BP neural network[J]. China Safety Science Journal, 2007, 17(8): 16-19.
[16] Boschetti L, Roy D P, Justice C O, et al. Global assessment of the temporal reporting accuracy and precision of the MODIS burned area product[J]. International Journal of Wildland Fire, 2010, 19(6): 705-709.
[17] Ceccato P, Gobron N, Flasse S, et al. Designing a spectral index to estimate vegetation water content from remote sensing data: part 1-theoretical approach[J]. Remote Sensing of Environment, 2002, 82(2): 188-197.
[18] Wheeler D C. Diagnostic tools and a remedial method for collinearity in geographically weighted regression[J]. Environment and Planning A, 2007, 39(10): 2464-2481.
[19] Krebs P, Koutsias N, Conedera M. Modelling the eco-cultural niche of giant chestnut trees: new insights into land use history in southern Switzerland through distribution analysis of a living heritage[J]. Journal of Historical Geography, 2012, 38(4): 372-386.
[20] Jiang Y Y, Zhuang Q L, Mandallaz D. Modeling large fire frequency and burned area in Canadian terrestrial ecosystems with Poisson models[J]. Environmental Modeling & Assessment, 2012, 17(5): 483-493.
[21] Hurvich C M, Simonoff J S, Tsai C L. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion[J]. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 1998, 60(2): 271-293.

基金

收稿日期:2014-12-05 修回日期:2015-04-20
基金项目:国家自然科学基金项目(41201099)
第一作者:王周,工程师。E-mail: xbygis@163.com。
引文格式:王周,金万洲. 基于地理加权泊松模型的河南省火灾风险模拟[J]. 南京林业大学学报:自然科学版,2015,39(5):93-98.