可燃物干燥指数在草地火险预警中的应用
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摘要
在草地生物物理特性基础上,结合能量交换原则(由遥感和气象数据得到显热和潜热通量)提出了可燃物干燥指数(Fd),并将其应用于山东省草地火险监测。Fd较好解决了山东省草地火灾风险预警时空预测问题,提高了火险的估算精度,能够随时间变化动态预警山东省每日高火灾风险区域。将Fd与美国潜在火险模型(fire potential index,FPI)用于2010年4月8日的火险预警研究,结果表明Fd较FPI能够更好地指示火险。在等间距火险分类法中,2010年31个火点数据Fd值在Ⅲ级以上的占87. 1%,Ⅰ级为0,火灾发生地点与火灾风险预警高的区域吻合较好。由Fd曲线图可以看出Fd与草地植被生长季节有着紧密的关系,初期和发育期的Fd值较高,但呈下降趋势;中期Fd值低;晚期Fd值高,并呈现上升趋势。总体说明了Fd指数在草地生长阶段火险预报中的重要作用。
In this paper,on the basis of prairie biophysical characteristics and in combination with the principle of energy exchange( sensible heat and latent heat flux obtained by remote sensing and meteorological data),the fuel dry index( Fd) was proposed and applied to the Shandong prairie fire monitoring. Fdcan better solve the prairie fire forecast,fire danger early warning in time and space and the estimation accuracy. It can change dynamic warning daily high fire risk areas with time in Shandong Province. Fdand fire potential index( FPI) were used to study the fire danger on April 8,2010. Fire indicating effect of Fdis better than that of FPI. In the equidistance fire classification,data of 31 fire points in 2010 indicated by Fdfell in grade III,accounting for 87. 1%,and 0 fell in grade I; the fire locations were in good agreement with areas of high fire risk early warning. In fuel dry index( Fd) graph,it can be seen that Fdhas close relationship with the prairie vegetation growing season; the early development of Fdis high,but later it exhibits decreasing trend; at the medium stage,Fdis low; at the late stage,Fdis high,and shows a trend of rising. Overall,the Fdindex plays an important role in fire danger forecast at the grassland growing stage.
In this paper,on the basis of prairie biophysical characteristics and in combination with the principle of energy exchange( sensible heat and latent heat flux obtained by remote sensing and meteorological data),the fuel dry index( Fd) was proposed and applied to the Shandong prairie fire monitoring. Fdcan better solve the prairie fire forecast,fire danger early warning in time and space and the estimation accuracy. It can change dynamic warning daily high fire risk areas with time in Shandong Province. Fdand fire potential index( FPI) were used to study the fire danger on April 8,2010. Fire indicating effect of Fdis better than that of FPI. In the equidistance fire classification,data of 31 fire points in 2010 indicated by Fdfell in grade III,accounting for 87. 1%,and 0 fell in grade I; the fire locations were in good agreement with areas of high fire risk early warning. In fuel dry index( Fd) graph,it can be seen that Fdhas close relationship with the prairie vegetation growing season; the early development of Fdis high,but later it exhibits decreasing trend; at the medium stage,Fdis low; at the late stage,Fdis high,and shows a trend of rising. Overall,the Fdindex plays an important role in fire danger forecast at the grassland growing stage.
引文
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[2]黄宝华,张华,周利霞.集成点燃和火源的火险指数应用研究[J].中国安全科学学报,2013,23(8):37-42.Huang B H,Zhang H,Zhou L X. Research on application of integrated fire danger index based on ignition and fire[J]. China Safety Science Journal,2013,23(8):37-42.
[3]黄宝华,孙治军,张华,等.潜在森林火险评估方法研究———以山东省为例[J].灾害学,2014,29(4):116-121.Huang B H,Sun Z J,Zhang H,et al. Research on the evaluation method of potential forest fire:Taking Shandong Provience as an example[J]. Journal of Catastrophology,2014,29(4):116-121.
[4] Agee J K,Wright C S,Williamson N,et al. Foliar moisture content of Pacific Northwest vegetation and its relation to wildland fire behavior[J]. Forest Ecology and Management,2002,167(1-3):57-66.
[5] Zarco-Tejada P J,Rueda C A,Ustin S L. Water content estimation in vegetation with MODIS reflectance data and model inversion methods[J]. Remote Sensing of Environment,2003,85(1):109-124.
[6] Huesca M,Litago J,Palacios-Orueta A,et al. Assessment of forest fire seasonality using MODIS fire potential:A time series approach[J]. Agricultural and Forest Meteorology,2009,149(11):1946-1955.
[7] Dasgupta S,Qu J J,Hao X,et al. Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia,USA[J]. Remote Sensing of Environment,2007,108(2):138-150.
[8] Dennison P E,Moritz M A,Taylor R S. Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains,California[J]. International Journal of Wildland Fire,2008,17(1):18-27.
[9] Chuvieco E,González I,VerdúF,et al. Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem[J]. International Journal of Wildland Fire,2009,18(4):430-441.
[10] Dennison P E,Roberts D A,Thorgusen S R,et al. Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index[J]. Remote Sensing of Environment,2003,88(4):442-452.
[11] Chowdhury E H,Hassan Q K. Operational perspective of remote sensing-based forest fire danger forecasting systems[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2015,104:224-236.
[12] Snyder R L,Spano D,Duce P,et al. A fuel dryness index for grassland fire-danger assessment[J]. Agricultural and Forest Meteorology,2006,139(1-2):1-11.
[13]田国良.热红外遥感[M].北京:电子工业出版社,2006:302-304.Tian G L. Thermal Remote Sensing[M]. Beijing:Publishing House of Electronics Industry,2006:302-304.
[14]王丁,张丽琴,薛建辉.林木对干旱胁迫的生理与分子响应研究综述[J].安徽农业科学,2011,39(25):15426-15431.Wang D,Zhang L Q,Xue J H. A review about physiological and molecular responses to drought stress of forest tree[J]. Journal of Anhui Agricultural Sciences,2011,39(25):15426-15431.
[15]张川,闫浩芳,大上博基,等.表层有效土壤水分参数化及冠层下土面蒸发模拟[J].农业工程学报,2015(2):102-107.Zhang C,Yan H F,Oue H,et al. Parameterization of surface soil available moisture and simulation of soil evaporation beneath canopy[J]. Transaction of the Chinese Society of Agricultural Engineering,2015(2):102-107.
[16] Van de Griend A,Owe M. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces[J]. International Journal of Remote Sensing,1993,14(6):1119-1131.
[17]孙敏章,刘作新,吕谋超,等.基于陆面能量平衡方程的遥感模型[J].灌溉排水学报,2005,24(3):74-76.Sun M Z,Liu Z X,Lyu M C,et al. The study of monitoring ET by SEBEL and its application in water resource management of Hai River Basin[J]. Journal of irrigation and orainage,2005,24(3):74-76.
[18]曾丽红,宋开山,张柏,等.应用Landsat数据和SEBAL模型反演区域蒸散发及其参数估算[J].遥感技术与应用,2008,23(3):255-263.Zeng L H,Song K S,Zhang B,et al. Applying Landsat data and SEBAL model to inverse regional evapotranspiration and its parameters estimation[J]. Remote Sensing Technology and Application,2008,23(3):255-263.
[19] Allen R G,Pereira L S,Raes D,et al. Crop EvapotranspirationGuidelines for Computing Crop Water Requirements[M]. Italy:Food and Agriculture Organization of the United Nations Rome,1998.
[20] Lagouarde J P,Brunet Y. Spatial integration of surface latent heat flux and evaporation mapping[J]. Advances in Space Research.1989,9(7):259-264.
[21] Su Z,Jacobs C. ENVISAT:Actual Evaporation[R]. Delft:Beleidscommissie Remote Sensing,2001.
[22]田贵全,张明才.山东省生态环境遥感监测[J].国土资源遥感,2006,18(4):63-67. doi:10. 6046/gtzyyg. 2006. 04. 16.Tian G Q,Zhang M C. The remote sensing monitoring of ecological environment in Shandong Province[J]. Remote Sensing for Land and Resources,2006,18(4):63-67. doi:10. 6046/gtzyyg. 2006.04. 16.
[23]冉有华,李新,卢玲.基于多源数据融合方法的中国1 km土地覆盖分类制图[J].地球科学进展,2009,24(2):192-203.Ran Y H,Li X,Lu L. China land cover classification at 1 km spatial resolution based on a multi-source data fusion approach[J].Advances in Earth Science,2009,24(2):192-203.
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