Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China

详细信息    查看全文

• 作者：Jinghu Pan ; Weiguo Wang ; Junfeng Li
• 关键词：Fire risk ; Logistic regression ; Zoning ; MODIS ; Shanxi Province
• 刊名：Natural Hazards
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：81
• 期：3
• 页码：1879-1899
• 全文大小：1,928 KB
• 参考文献：Avila-Flores D, Pompa-Garcia M, Antonio-Nemiga X, Rodriguez-Trejo DA, Vargas-Perez E, Santillan-Perez J (2010) Driving factors for forest fire occurrence in Durango state of Mexico: a geospatial perspective. Chin Geogr Sci 20:491–497. doi:10.​1007/​s11769-010-0437-x CrossRef
  Badia A, Serra P, Modugno S (2011) Identifying dynamics of fire ignition probabilities in two representative Mediterranean wildland–urban interface areas. Appl Geogr 31:930–940. doi:10.​1016/​j.​apgeog.​2011.​01.​016 CrossRef
  Bianchini G, Denham M, Cortés A, Margalef T, Luque E (2010) Wildland fire growth prediction method based on multiple overlapping solution. J Comput Sci 1(4):229–237. doi:10.​1016/​j.​jocs.​2010.​07.​005 CrossRef
  Bisquert M, Caselles E, Sánchez JM, Caselles V (2012) Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data. Int J Wildland Fire 21:1025–1029. doi:10.​1071/​WF11105 CrossRef
  Botequim B, Garcia-Gonzalo J, Marques S, Ricardo A, Borges JG, Tomé M, Oliveira MM (2013) Developing wildfire risk probability models for Eucalyptus globulus stands in Portugal. iForest Biogeosci For 6:217. doi:10.​3832/​ifor0821-006 CrossRef
  Carter GM, Rolph JE (1974) Empirical Bayes methods applied to estimating fire alarm probabilities. J Am Stat As 69:880–885. doi:10.​1080/​01621459.​1974.​10480222 CrossRef
  Catry FX, Moreira F, Duarte I, Acácio V (2009) Factors affecting post-fire crown regeneration in cork oak (quercus suber l) trees. Eur J For Res 128(3):231–240. doi:10.​1007/​s10342-009-0259-5 CrossRef
  Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S (2002) Designing a spectral index to estimate vegetation water content from remote sensing data: part 1—theoretical approach. Remote Sens Environ 82:188–197. doi:10.​1016/​S0034-4257(02)00037-8 CrossRef
  Chang Y, Zhu ZL, Bu RC, Chen HW, Feng YT, Li YH, Hu YM, Wang ZC (2013) Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landsc Ecol 28:1989–2004. doi:10.​1007/​s10980-013-9935-4 CrossRef
  Chou YH, Minnich RA, Chase RA (1993) Mapping probability of fire occurrence in San Jacinto Mountains, California, USA. Environ Manag 17:129–140. doi:10.​1007/​BF02393801 CrossRef
  Chuvieco E, González I, Verdú F, Aguado I, Yebra M (2009) Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. Int J Wildland Fire 18:430–441. doi:10.​1071/​WF08020 CrossRef
  Clarke KC, Brass JA, Riggan PJ (1994) A cellular automation model of wildfire propagation and extinction. Photogramm Eng Rem Sens 60:1355–1367
  Cleland DT, Crow TR, Saunders SC, Dickmann DI, Maclean AL, Jordan JK, Watson RL, Sloan AM, Brosofske KD (2004) Characterizing historical and modern fire regimes in Michigan (USA): a landscape ecosystem approach. Landsc Ecol 19:311–325. doi:10.​1023/​B:​LAND.​0000030437.​29258.​3c CrossRef
  de Vasconcelos MP, Silva S, Tome M, Alvim M, Pereira JC (2001) Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogramm Eng Rem Sens 67:73–81
  del Hoyo LV, Isabel MPM, Vega FJM (2011) Logistic regression models for human–caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data. Eur J For Res 130:983–996. doi:10.​1007/​s10342-011-0488-2 CrossRef
  Dickson BG, Prather JW, Xu Y, Hampton HM, Aumack EN, Sisk TD (2006) Mapping the probability of large fire occurrence in northern Arizona, USA. Landsc Ecol 21:747–761. doi:10.​1007/​s10980-005-5475-x CrossRef
  Dillon GK, Holden ZA, Morgan P, Crimmins MA, Heyerdahl EK, Luce CH (2011) Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006. Ecosphere 2:art 130. doi:10.​1890/​ES11-00271.​1 CrossRef
  Dimitrakopoulos AP, Papaioannou KK (2001) Flammability assessment of Mediterranean forest fuels. Fire Technol 37:143–152. doi:10.​1023/​A:​1011641601076 CrossRef
  Dlamini WM (2011) Application of Bayesian networks for fire risk mapping using GIS and remote sensing data. GeoJournal 76:283–296. doi:10.​1007/​s10708-010-9362-x CrossRef
  Dong X, Shao GF, Dai LM, Hao ZQ, Tang L, Wang H (2006) Mapping forest fire risk zones with spatial data and principal component analysis. Sci China Ser E Technol Sci 49(Supplement 1):140–149. doi:10.​1007/​s11434-006-8115-1
  Eskandari S, Chuvieco E (2015) Fire danger assessment in Iran based on geospatial information. Int J Appl Earth Obs Geoinf 42:57–64. doi:10.​1016/​j.​jag.​2015.​05.​006 CrossRef
  Geldenhuys CJ (1996) Forest management systems to sustain resource use and biodiversity: examples from the southern Cape, South Africa. Springer, Netherlands, pp 317–322. doi:10.​1007/​s10980-013-9935-4
  Guisan A, Edwards TC, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model 157:89–100. doi:10.​1016/​S0304-3800(02)00204-1 CrossRef
  Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36CrossRef
  Hawbaker TJ, Radeloff VC, Stewart SI, Hammer RB, Keuler NS, Clayton MK (2013) Human and biophysical influences on fire occurrence in the United States. Ecol Appl 23:565–582. doi:10.​1890/​12-1816.​1 CrossRef
  Hegeman EE, Dickson BG, Zachmann LJ (2014) Probabilistic models of fire occurrence across National Park Service units within the Mojave Desert Network, USA. Landsc Ecol 29:1587–1600. doi:10.​1007/​s10980-014-0078-z CrossRef
  Henderson M, Kalabokidis K, Marmaras E, Konstantinidis P, Marangudakis M (2005) Fire and society: a comparative analysis of wildfire in Greece and the United States. Hum Ecol Rev 12:169–182
  Hering AS, Bell CL, Genton MG (2009) Modeling spatio-temporal wildfire ignition point patterns. Environ Ecol Stat 16:225–250. doi:10.​1007/​s10980-013-9935-4 CrossRef
  Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New YorkCrossRef
  Jung J, Kim C, Jayakumar S, Kim S, Han S, Kim DH, Heo J (2013) Forest fire risk mapping of Kolli Hills, India, considering subjectivity and inconsistency issues. Nat Hazards 65:2129–2146. doi:10.​1007/​s11069-012-0465-1 CrossRef
  Kandya AK, Kimothi MM, Jadhav RN, Agarwal JP (1998) Application of geographic information system in identification of ‘fire-prone’ areas—a feasibility study in parts of Junagadh (Gujarat, India). Indian For 124(7):531–535
  Li XW, Fu GB, Zeppel MJB, Yu XB, Zhao G, Eamus D, Qiang Y (2012) Probability models of fire risk based on forest fire indices in contrasting climates over China. J Resour Ecol 3:105–117. doi:10.​5814/​j.​issn.​1674-764x.​2012.​02.​002 CrossRef
  Liu Z, Yang J, Chang Y, Weisberg PJ, He HS (2012) Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Glob Change Biol 18:2041–2056. doi:10.​1111/​j.​1365-2486.​2012.​02649.​x CrossRef
  Lozano FJ, Suárez-Seoane S, Kelly M, Luis E (2008) A multi–scale approach for modeling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region. Remote Sens Environ 112:708–719. doi:10.​1016/​j.​rse.​2007.​06.​006 CrossRef
  Maeda EE, Formaggio AR, Shimabukuro YE, Arcoverde GFB, Hansen MC (2009) Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks. Int J Appl Earth Obs 11:265–272. doi:10.​1016/​j.​jag.​2009.​03.​003 CrossRef
  Maingi JK, Henry MC (2007) Factor influencing wildfire occurrence and distribution in eastern Kentucky, USA. Int J Wildland Fire 16:23–33. doi:10.​1071/​WF06007 CrossRef
  Martínez-Fernández J, Chuvieco E, Koutsias N (2013) Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazard Earth Sys 13:311–327. doi:10.​5194/​nhess-13-311-2013 CrossRef
  Massada AB, Syphard AD, Stewart SI, Radeloff VC (2013) Wildfire ignition–distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA. Int J Wildland Fire 22:174–183. doi:10.​1071/​WF11178 CrossRef
  Matthews SA, Yang TC (2012) Mapping the results of local statistics: using geographically weighted regression. Demogr Res 26:151–166. doi:10.​4054/​DemRes.​.​26.​6 CrossRef
  McCune B, Grace JB, Urban DL (2002) Analysis of ecological communities. MjM software design, 28, Gleneden Beach, Oregon
  Mohammadi F, Bavaghar MP, Shabanian N (2014) Forest fire risk zone modeling using logistic regression and GIS: an Iranian case study. Small scale For 13:117–125. doi:10.​1007/​s11842-013-9244-4 CrossRef
  Moreira F, Viedma O, Arianoutsou M, Curt T, Koutsias N, Rigolot F, Barbati A, Corona P, Vaz P, Xanthopoulos G, Mouillot F, Bilgili E (2011) Landscape–wildfire interactions in southern Europe: implications for landscape management. J Environ Manag 92:2389–2402. doi:10.​1016/​j.​jenvman.​2011.​06.​028 CrossRef
  Nadeau LB, Englefield P (2006) Fine-resolution mapping of wildfire fuel types for Canada: fuzzy logic modeling for an Alberta pilot area. Environ Monit Assess 120:127–152. doi:10.​1007/​s10661-005-9053-0 CrossRef
  Oliveira S, Oehler F, San–Miguel–Ayanz J, Camia A, Pereira JM (2012) Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. Forest Ecol Manag 275:117–129. doi:10.​1016/​j.​foreco.​2012.​03.​003 CrossRef
  Pew KL, Larsen CPS (2001) GIS analysis of spatial and temporal patterns of human-caused wildfires in the temperate rainforest of Vancouver Island, Canada. For Ecol Manag 140:1–18. doi:10.​1016/​S0378-1127(00)00271-1 CrossRef
  Preisler HK, Westerling AL, Gebert KM, Munoz-Arriola F, Holmes TP (2011) Spatially explicit forecasts of large wildland fire probability and suppression costs for California. Int J Wildland Fire 20:508–517. doi:10.​1071/​WF09087 CrossRef
  Renard Q, Pélissier R, Ramesh BR, Kodandapani N (2012) Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int J Wildland Fire 21:368–379. doi:10.​1071/​WF10109 CrossRef
  Ruiz JAM, Riaño D, Arbelo M, French NH, Ustin SL, Whiting ML (2012) Burned area mapping time series in Canada (1984–1999) from NOAA–AVHRR LTDR: a comparison with other remote sensing products and fire perimeters. Remote Sens Environ 117:407–414. doi:10.​1016/​j.​rse.​2011.​10.​017 CrossRef
  Vega-García C, Chuvieco E (2006) Applying local measures of spatial heterogeneity to Landsat–TM images for predicting wildfire occurrence in Mediterranean landscapes. Landsc Ecol 21:595–605. doi:10.​1007/​s10980-005-4119-5 CrossRef
  Vilar L, Woolford DG, Martell DL, Martín MP (2010) A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain. Int J Wildland Fire 19(3):325–337. doi:10.​1071/​WF09030 CrossRef
  Wang L, Zhou Y, Zhou W, Wang S (2013) Fire danger assessment with remote sensing: a case study in Northern China. Nat Hazards 65:819–834. doi:10.​1007/​s11069-012-0391-2 CrossRef
  Wooster MJ, Roberts G, Smith AM, Johnston J, Freeborn P, Amici S, Hudak AT (2013) Thermal remote sensing of active vegetation fires and biomass burning events. In: Thermal infrared remote sensing. Springer, Netherlands, pp 347–390. doi:10.​1007/​978-94-007-6639-6_​18
  Wu Z, He HS, Yang J, Liu Z, Liang Y (2014) Relative effects of climatic and local factors on fire occurrence in boreal forest landscapes of northeastern China. Sci Total Environ 493:472–480. doi:10.​1016/​j.​scitotenv.​2014.​06.​011 CrossRef
  Xu D, Dai LM, Shao GF, Tang L, Wang H (2005) Forest fire risk zone mapping from satellite images and GIS for Baihe Forestry Bureau, Jilin. Chin J For Res 16(3):169–174
  Yang J, He HS, Shifley SR, Gustafson EJ (2007) Spatial patterns of modern period human-caused fire occurrence in the Missouri Ozark Highlands. For Sci 53:1–15. doi:10.​1071/​WF13136
  Yebra M, Chuvieco E, Riaño D (2008) Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agric For Meteorol 148:523–536. doi:10.​1016/​j.​agrformet.​2007.​12.​005 CrossRef
  Zhang H, Qi P, Guo G (2014) Improvement of fire danger modelling with geographically weighted logistic model. Int J Wildland Fire 23:1130–1146. doi:10.​1071/​WF13195 CrossRef
  
• 作者单位：Jinghu Pan (1)
  Weiguo Wang (1)
  Junfeng Li (1)
  
  1. College of Geographic and Environmental Science, Northwest Normal University, 967 Anning East Road, Lanzhou, 730070, People’s Republic of China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geophysics and Geodesy
  Geotechnical Engineering
  Civil Engineering
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-0840

文摘

Fires are a recurrent environmental and economic emergency throughout the world. Fire risk analysis and forest fire risk zoning are important aspects of forest fire management. MODIS remote sensing datasets for Shanxi Province from 2002 to 2012 were used to build a spatial logistic forest fire risk model, based on the spatial distribution of forest fires and forest fire-influencing factors, using geographic information system technology. A forest fire risk zoning study was conducted at a large temporal scale and a provincial spatial scale. The resulting logistic model of forest fire risk, built with spatial sampling, showed a good fit (p < 0.05) between the distribution of forest fires and forest fire impact factors. The relative operating characteristic value was 0.757, and a probability distribution map for forest fire was developed, using layer computing. The forest fire area of Shanxi Province was divided into zones of zero, low, moderate, high and extremely high fire risk. The influences of altitude (GC), land-use type (LT), land surface temperature (LST), normalized difference vegetation index (NDVI) and global vegetation moisture index (GVMI) on fire events presented significant spatial variability, whereas the influences of slope and distance to the nearest path exhibited insignificant spatial variability in Shanxi Province. The influences of NDVI and LST on fire events were significant throughout Shanxi Province, whereas the influences of GC, LT and GVMI were only significant locally. Seven fire-prevention regions were delineated, based on the fire-influencing factors. Different fire-prevention policies and emphases should be taken into consideration for each of the seven fire-prone regions.