期刊信息

  • 刊名: 河北师范大学学报(自然科学版)Journal of Hebei Normal University (Natural Science)
  • 主办: 河北师范大学
  • ISSN: 1000-5854
  • CN: 13-1061/N
  • 中国科技核心期刊
  • 中国期刊方阵入选期刊
  • 中国高校优秀科技期刊
  • 华北优秀期刊
  • 河北省优秀科技期刊

基于MODIS数据的2002-2012年河南省火灾时空特征分析

  • 1. 南阳师范学院 环境科学与旅游学院, 河南 南阳 473061;
    2. 南阳师范学院 实验室与设备管理处, 河南 南阳 473061
  • DOI: 10.13763/j.cnki.jhebnu.nse.2015.04.013

Spatio-temporal Characteristics Analysis of Fires Based on MODIS Data in Henan Province from 2002 to 2012

摘要/Abstract

摘要:

火灾的时空特征分析可为防火管理和决策提供支持和参考.基于MODIS数据,利用ArcGIS空间分析和统计方法对2002—2012年河南省的火灾时空分布进行分析.研究表明:1) 2002,2006,2008年为河南省火烧的峰值年份,2005,2007,2011年为谷值年份.各市的火烧面积年际间波动较大,11a来,周口、驻马店、商丘、平顶山、新乡、安阳、漯河和鹤壁的火灾最严重.2) 谷类作物火、阔叶作物火、城市和建成区火是河南省最主要的3种火烧类型.火灾表现出随高程、坡度增大和随远离居民区和道路而逐渐减小的趋势,高程<150m或坡度<1°的地区火灾最严重,距离居民区和主要道路的2km内是火灾的最密集区.3) 9月和10月为河南省火烧最严重的月份.9月火灾的LST,NDVI和GVMI的最大和最强响应区分别为301~303K,0.7~0.8,0.3~0.4和305~307K,0.8~0.9,0.3~0.4;10月火灾的LST,NDVI和GVMI的最大和最强响应区分别为303~305K,0.3~0.4,0.1~0.2和305~307K,0.2~0.3,0.0~0.1.4)谷类作物火、城市与建成区火10月均略高于9月,阔叶作物火10月则远高于9月.

Abstract:

Spatio-temporal characteristics analysis of fires can provide support and reference for effective fire prevention management.MODIS data from 2002 to 2012 were used to analyze spatio-temporal distribution patterns of fires in Henan province using ArcGIS spatial analysis and statistical methods.Results showed that more fires (burned area and fire density) appeared in 2002, 2006 and 2008 and less fires appeared in 2005,2007 and 2011.The burned area in each city of Henan province fluctuated obviously year to year,and the most serious cities influenced by fires from 2002 to 2012 included in order Zhoukou,Zhumadian,Shangqiu,Pingdingshan,Xinxiang,Anyang,Luohe and Hebi.Three main fire types were cereal crops fire,broad-leaf crops fire,urban and built-up fire.Fires decreased with the increase of altitude,slope,distance from the nearest village and road.Fires were the most serious in those regions with the altitude of less than 150 meters or with the slope of less than one degree.Regions within two kilometers distance from the nearest village or main road were the most dense areas influenced by fires.September and October were influenced the most seriously by fires in Henan province.In September,the biggest and the strongest fire-response ranges for LST,NDVI and GVMI were 301~303 K,0.7~0.8,0.3~0.4 and 305~307 K,0.8~0.9,0.3~0.4,respectively.In contrast,they were 303~305 K,0.3~0.4,0.1~0.2 and 305~307 K,0.2~0.3,0.0~0.1 in October.Cereal crops fire and urban and built-up fire occurrence in October were slightly higher than that in September.However,broad-leaf crops fire occurrence was much higher in October than that in September.

参考文献 16

  • [1] 张冬有,邓欧,李亦秋,等.黑龙江省1980-2005年森林火灾时空特征[J].林业科学,2012,48(2):175-179.
  • [2] ARDAKANI A S,ZOEJ M J V,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.
  • [3] 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.
  • [4] 何宁,韩晓鹏.2000—2009年中国火灾时空分布特征分析[J].消防科学与技术,2012,31(2):210-213.
  • [5] 徐波,何宁,宫鹏,等.中国火灾的时空演变特征——基于GIS的统计分析[J].自然灾害学报,2012,21(1):198-203.
  • [6] 张海军.河南省火灾影响因素的空间分析[J].地理科学进展,2014,33(7):958-968.
  • [7] HERNANDEZ-LEAL P A,GONZALEZ-CALVO A,ARBELO M,et al.Synergy of GIS and Remote Sensing Data in Forest Fire Danger Modeling[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(4):240-247.
  • [8] LOZANO F J,SUÁREZ-SEOANE S,KELLY M,et al.A Multi-scale Approach for Modeling Fire Occurrence Probability Using Satellite Data and Classification Trees:A Case Study in a Mountainous Mediterranean Region[J].Remote Sensing of Environment,2008,112(3):708-719.
  • [9] HENDERSON S B,LCHOKU C,BURKHOLDER B J,et al.The Validity and Utility of MODIS Data for Simple Estimation of Area Burned and Aerosols Emitted by Wildfire Events[J].International Journal of Wildland Fire,2010,19(7):844-852.
  • [10] PREISLER H K,WESTERLING A L,GEBERT K M,et al.Spatially Explicit Forecasts of Large Wildland Fire Probability and Suppression Costs for California[J].International Journal of Wildland Fire,2011,20(4):508-517.
  • [11] 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.
  • [12] 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.
  • [13] WANG L T,ZHOU Y,ZHOU W Q,et al.Fire Danger Assessment with Remote Sensing:A Case Study in Northern China[J].Natural Hazards,2013,65(1):819-834.
  • [14] 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.
  • [15] 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.
  • [16] 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.