野火蔓延灾害风险评估研究进展

doi:10.11820/dlkxjz.2010.07.002

Abstract

Abstract:

Wildfire disasters have brought serious impacts on regional ecosystem and global climate system. The researches onf wildfire risk assessment and fire spreading have positive effect on fire prevention. In this paper, the latest research status and trends of fuel type models, approaches of mapping fuel, wildfire spreading models, computer simulation techniques about wildfire spreading, and wildfire risk assessment were reviewed. Firstly, it is concluded that better fuel models should be developed to supply effective data for the research on regional or global fire risk assessment and fire spread, based on remote sensing information and situ data. Secondly, the geo-spatial information technology and computer technology give solutions to massive data calculation of fire simulation, to establish monitoring system and network information system of real-time, dynamic simulation on fire behavior. Thirdly, wildfire risk assessment is conducted based on disaster system theory, subsequent to the evaluation of hazard factors and vulnerability of burned regions by fuel models and spreading models. Fourthly, integrated, practical, multi-dimensional and standardized wildfire spreading model and decision support system as well as a national fire danger rating system, should be developed in China, to provide a scientific basis for wildfire disaster prevention.

Key words: fuel, risk assessment, simulation technique, spread model, wildfire

GUO Zhixing,ZHONG Xingchun, FANG Weihua,CAO Xin,LIN Wei. The Research Advances of Wildfire Spreading and Wildfire Risk Assessment[J].PROGRESS IN GEOGRAPHY, 2010, 29(7): 778-788.

References

[1] 谭明艳, 陈仲新, 曹鑫, 等. 利用MODIS识别草原火灾迹地方法的研究. 遥感学报, 2007, 11(3): 340-349.

[2] Rothermel R C. A mathematical model for predicting fire spread in wildland fuels. USDA, Forest Service. Rep. No. RP INT-115, 1972.

[3] Pastor E, Zarate L, Planas E, et al. Mathematical models and calculation systems for the study of wildland fire behaviour. Progress in Energy and Combustion Science, 2003, 29(2): 139-153.

[4] 唐晓燕, 孟宪宇, 易浩若. 林火蔓延模型及蔓延模拟的研究进展. 北京林业大学学报, 2002, 24(1): 87-91.

[5] Cohen J D, Deeming J E. The National Fire Danger Rating System: Basic equations. Pacific Southwest Forest and Range Experiment Station. Rep. No. PSW-82, 1982.

[6] Albini F A. Estimating wildfire behavior and effects. USDA, Forest Service, Intermountain Forest and Range Experiment Station. Rep. No. GTR INT-30, 1976.

[7] Anderson H E. Aids to determining fuel models for estimating fire behavior. USDA, Forest Service. Rep. No. GTR INT-122, 1982.

[8] Burgan R E R, Richard C. BEHAVE: Fire behavior prediction and fuel modeling system-FUEL subsystem. USDA, Forest Service, Intermountain Forest and Range Experiment Station. General Technical Report INT-167, 1984.

[9] Andrews P L. BEHAVE: Fire behavior prediction and fuel modeling system-Burn subsystem, Part 1. USDA, Forest Service, Intermountain Forest and Range Experiment Station. Rep. No. GTR INT-194, 1986.

[10] Finney M A. FARSITE: Fire area simulator–model development and valuation. USDA, Forest Service. Rep. No. Paper RMRS-RP-4. 1998.

[11] Sandberg D V, Ottmar R D, Cushon G H. Characterizing fuels in the 21st Century. International Journal of Wildland Fire, 2001, 10(3/4): 381-187.

[12] Ottmar R D, Sandberg D V, Riccardi C L, et al. An overview of the fuel characteristics classification system: Quantifying, classifying, and creating fuelbeds for resource planning. Canadian Journal of Forest Research, 2007, 37(12): 2383-2393.

[13] Cheney P. A National Fire Danger Rating System for Australia. International Forest Fire News.
[1992-2-6].http://www.fire.uni-freiburg.de/iffn/country/au/au_4.htm.

[14] Van Wagner C E. Development and Structure of the Canadian Forest Fire Behavior Prediction System. Forestry Canada, Forestry Canada Fire Danger Group. Information Report ST-X-3. 1992.

[15] Giakoumakis M N, Gitas I Z, San-Miguel J. Object-oriented classification modeling for fuel type mapping in the Mediterranean, using LANDSAT TM and IKONOS imagery preliminary results. Viegas. Forest Fire Research & Wildland Fire Safety. Rotterdam:Millpress, 2002: 1-13.

[16] Keane R E, Burgan R E, Wagtendonk J V. Mapping wildland fuel for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire, 2001, 10(4): 301-319.

[17] van Wagtendonk J W, Root R R. The USE of multitemporal Landsat normalized difference vegetation index (NDVI) data for mapping fuels models in Yosemite National Park, USA. International Journal of Remote Sensing, 2003, 24(3): 1639-1651.

[18] Lanorte A, Lasaponara R. Fuel type characterization based on coarse resolution MODIS satellite data. Journal of Biogeosciences and Forestry, 2008, 1: 60-64.

[19] Lefsky M A, Cohen W B, Parker G G, et al. Lidar Remote Sensing for Ecosystem Studies. BioScience, 2002, 52(1): 19-30.

[20] Austin J M, Mackey B G, Van Niel K P. Estimating forest biomass using satellite radar: an exploratory study in a temperate Australian Eucalyptus forest. Forest Ecology and Management, 2003, 176(1/2/3): 575-583.

[21] Andersen H E, McGaughey R J, Reutebuch S E. Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 2005, 94(4): 441-449.

[22] Kalabokidis K, Hay C, Hussin Y. Spatially resolved fire growth simulation//Proceedings of the 11th Conference on Fire and Forest Meterology, 1991: 188-195.

[23] Vasconcelos M J, Guertin D P. FIREMAP: Simulation of fire growth with a geographic information system. International Journal of Wildland Fire, 1992, 2(2): 87-96.

[24] Lopes A, Cruz M, Viegas D. Firestation-an integrated software system for the numerical simulation of fire spread on complex topography. Environmental Modelling & Software, 2002, 17(3): 269-285.

[25] Perry G L, Sparrow A D, Owens I F. A gis-supported model for the simulation of the spatial structure of wildland fire, Cass Basin, New Zealand. Journal of Applied Ecology, 1999, 36(4): 502-518.

[26] Weise D R, Biging G S. A qualitative comparison of fire spread models incorporating wind and slope effects. Forest Science, 1997, 43(2): 170-180.

[27] Noble I R, Bary G A V, Gill A M. McArthur's fire-danger meters expressed as equations. Australian Journal of Ecology, 1980, 5(2): 201-203.

[28] Ensis. SiroFire: A computer-based fire spread simulator
[EB/OL].

[2006] . http://www.ensisjv.com/ResearchCapabilitiesAchievements/ForestHealthBiosecurityandFire/BushfireResearch/BushfireSoftware.

[29] Cheney N P, Gould J S, Catchpole W R. Prediction of fire spread in grasslands. International Journal of Wildland Fire, 1998, 8(1): 1-13.

[30] Van Wagner C E, The development and structure of the Canadian Forest Fire Weather Index System. Canadian Forest Service, Petawawa National Forestry Institute. FTR-35. 1987.

[31] Richards G D. A general mathematical framework for modeling two-dimensional wildfire spread. International Journal of Wildland Fire, 1995, 5(2): 63-72.

[32] Karafyllidis I, Thanailakis A. A model for predicting forest fire spreading using cellular automata. Ecological Modelling, 1997, 99(1): 87-97.

[33] Berjak S G, Hearne J W. An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system. Ecological Modelling, 2002, 148(2): 133-151.

[34] Encinas L H, White S H, del Rey A M, et al. Modelling forest fire spread using hexagonal cellular automata. Applied Mathematical Modelling, 2007, 31(6): 1213-1227.

[35] Alexandridis A, Vakalis D, Siettos C I, et al. A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990. Applied Mathematics and Computation, 2008, 204(1): 191-201.

[36] Yassemi S, Dragicevic S, Schmidtb M. Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour. Ecological Modelling, 2008, 210(1/2): 71-84.

[37] Karafyllidis I, Thanailakis A. Design of a dedicated parallel processor for the prediction of forest fire spreading using cellular automata and genetic algorithms. Engineering Applications of Artificial Intelligence, 2004, 17(1): 19-36.

[38] Innocenti E, Silvani X, Muzya A, et al. A software framework for fine grain parallelization of cellular models with OpenMP: Application to fire spread. Environmental Modelling & Software, 2009, 24(7): 1-13.

[39] Richards G D. An elliptical growth model of forest fire fronts and its numerical solution. International Journal for Numerical Methods in Engineering, 1990, 30(6):1163-1179.

[40] 毛贤敏. 风和地形对林火蔓延速度的作用. 应用气象学报, 1993, 4(1): 100-104.

[41] 温广玉, 刘勇. 林火蔓延的数学模型及其应用. 东北林业大学学报, 1994, 22(2): 31-36.

[42] 唐晓燕, 孟宪宇, 葛宏立, 等. 基于栅格结构的林火蔓延模拟研究及其实现. 北京林业大学学报, 2003, 25(1): 54-58.

[43] 黄作维, 张贵. 基于GIS模型的林火蔓延研究. 湖南林业科技, 2004, 31(2): 17-19.

[44] 宋丽艳, 周国模, 汤孟平, 等. 基于GIS的林火蔓延模拟的实现. 浙江林学院学报, 2007, 24(5): 614-618.

[45] 毛学刚, 范文义, 李明泽. 基于GIS模型的林火蔓延计算机仿真. 东北林业大学学报, 2008, 36(9): 38-41.

[46] 单延龙, 张敏, 胡海清. 大兴安岭地区樟子松林地表可燃物模型. 东北林业大学学报, 2005, 33(2): 74-76.

[47] 郭利峰, 牛树奎, 阚振国. 北京八达岭人工油松林地表枯死可燃物负荷量研究. 林业资源管理, 2007(5): 53-58

[48] 田晓瑞, 戴兴安, 王明玉. 北京市森林可燃物分类研究. 林业科学, 2006, 42(11): 76-80.

[49] 覃先林, 易浩若. 基于MOD IS 数据的森林可燃物分类方法:以黑龙江省为实验区. 遥感技术与应用, 2004, 19(4): 236-239.

[50] Tian X R, McRae D J, Shu L F, et al. Fuel classification and mapping from satellite imagines. Journal of Forestry Research, 2005, 16(4): 311-316.

[51] 黄华国, 张晓丽. 基于三维曲面元胞自动机模型的林火蔓延模拟. 北京林业大学学报, 2005, 27(3): 94-97.

[52] 王长缨, 周明全, 张思玉. 基于规则学习的林火蔓延元胞自动机模型. 福建林学院学报, 2006, 26(3): 229-234.

[53] 王惠, 周汝良, 庄娇艳, 等. 林火蔓延模型研究及应用开发. 济南大学学报: 自然科学版, 2008, 22(3): 295-300.

[54] 薛晔, 黄崇福. 自然灾害风险评估模型的研究进展. 应用基础与工程科学学报, 2006, 14(增): 1-10.

[55] 史培军. 三论灾害研究的理论与实践. 自然灾害学报, 2002, 11(3): 1-9.

[56] Gonzalez-alonso F, Cuevas J M, Casanova J L, et al. A forest fire risk assessment using NOAA AVHRR images in the Valencia area, eastern Spain. International Jounrnal Remote Sensing, 1997, 18(10): 2201-2207.

[57] Burgan R E, Klaver R W, Klaver J M. Fuel models and fire potential from satellite and surface observation. International Journal of Wildland Fire, 1998, 8(3): 159-170.

[58] Ana. Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale. International Jounrnal Remote Sensing, 2002, 23(13): 2713-2719.

[59] Peng G, Li J, Chen Y, et al. A forest fire risk assessment using ASTER images in Peninsular Malaysia. Journal of China University of Mining & Technology, 2007, 17(2): 232-237.

[60] Jaiswal R K, Mukherjee S, Raju K D, et al. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoinformation, 2002, 4(1): 1-10.

[61] Xu D, Dai L M, Shao G F, et al. Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research, 2005, 16(3): 169-173.

[62] Kalabokidis K D, Koutsias N, Konstantinidis P, et al. Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece. Area, 2007, 39(3): 392-402.

[63] Martinez J, Garcia C V, Chuvieco E. Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management, 2009, 90(2): 1241-1252.

[64] Vasconcelos M J P, Silva S, Tomé M. Spatial prediction of fire ignition probabilities: Comparing logistic regression and neural networks. Photogrammetric Engineering and Remote Sensing, 2001, 67(1): 73-81.

[65] Mbow C, Goita K, Benie G. Spectral indices and fire behavior simulation for fire risk assessment in savanna ecosystems. Remote Sensing of Environment, 2004, 91(1):1-13.

[66] Carmel Y, Paz b S, Jahashan F, et al. Assessing fire risk using Monte Carlo simulations of fire spread. Forest Ecology and Management, 2009, 257(1): 370-377.

[67] Tong Z, Zhang J, Liu X. GIS-based risk assessment of grassland fire disaster in western Jilin province, China. Stoch Environ Res Risk Assess, 2009, 23(4): 463-471.

[68] Chuviecoa E, Aguadoa I, Yebra M, et al. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling, 2009, 221(1): 46-58.

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