[1]王 周.云南省林草火险空间模拟和等级评价[J].南京林业大学学报(自然科学版),2020,44(02):141-149.[doi:10.3969/j.issn.1000-2006.201808010.]
 WANG Zhou.Spatial modeling and grade evaluation of forest and grass fire danger in Yunnan Province[J].Journal of Nanjing Forestry University(Natural Science Edition),2020,44(02):141-149.[doi:10.3969/j.issn.1000-2006.201808010.]





Spatial modeling and grade evaluation of forest and grass fire danger in Yunnan Province
王 周
(国家林业和草原局西北林业调查规划设计院,陕西 西安 710048)
(Northwest Surveying, Planning and Design Institute of National Forestry and Grassland Administration, Xi’an 710048, China)
林草火险 地理加权Logistic回归(GWLR) 残差空间自相关 交叉验证 云南省
forest and grass fire danger geographically weighted Logistic regression(GWLR) spatial autocorrelation of residuals cross validation Yunnan Province
S762; X43
【Objective】Spatial modeling and grade evaluation of forest and grass fire dangers are great significance in fire prevention. In this study, a framework of spatial non-stationarity was introduced to carry out fire danger modeling in Yunnan Province. 【Method】 Data from the Moderate Resolution Imaging Spectroradiometer(MODIS), four fire-influencing factors(vegetation status, temperature, topography, and accessibility), and geographically weighted Logistic regression(GWLR)were employed to model and evaluate the forest and grass fire danger. After checking the spatio-temporal distribution of fires during 2001 and 2014, an equal number of unburned and burned points from December to April were sampled according to the designed procedure. The sampled data were divided into seven groups(one group for two years); of these, four were selected to develop the GWLR model(i.e., training and inner validation subset)and three were used to validate the developed model(i.e., independent testing subset), which created thirty five datasets. After excluding potential multicollinearity among the independent variables, the 35 adaptive Gaussian GWLR models and 35 adaptive bi-square GWLR models were developed. Cross validation was utilized to evaluate the performance of the developed GWLR models. Statistical inference was carried out based on the optimal model. 【Result】 We concluded that attention should be paid to overfitting and spatial autocorrelation of residuals in the application of GWLR to fire danger studies as they may hamper fire prevention operations. The “extreme high” danger zone accounted for 12.7%, “very high” for 18.1%, and “high” for 19.0% of the forest and grass fires in Yunnan Province from 2001 to 2014. Together, the “extreme high” and “very high” zones accounted for 80% of these fires and could be labeled as the “key area” in the danger zone. We suggest that the locations of Wenshan, Honghe, Yuxi, Chuxiong, Lijiang, and Dali, which together account for 71.5% of the “key area,” should be recognized as the most fire-prone areas and must be given priority in receiving labor, funds, and goods for forest and grass fire prevention. High temperature was an all-important forest and grass fire-influencing factor in Yunnan Province, followed by altitude, slope, dead vegetation, and fraction of green vegetation. The accessibility of roads and villages also plays a role in controlling the spread of fire. The influence of environmental factors on the presence of fire presented significant spatial variability in this study; however, these factors are unique to the Yunnan Province. 【Conclusion】 According to the significant areas of fire-influencing factors and their relative weights, Yunnan Province can be divided into six fire prevention regions and requires the formulation of different fire prevention policies for each region


[1] 萨如拉,周庆,刘鑫晔,等.1980—2015年内蒙古森林火灾的时空动态[J].南京林业大学学报(自然科学版),2019,43(2):137-143.SA R L,ZHOU Q,LIU X Y,et al.Studies on the spatial and temporal dynamics of forest fires in Inner Mongolia from 1980 to 2015[J].J Nanjing For Univ(Nat Sci Ed),2019,43(2):137-143.DOI:10.3969/j.issn.1000-2006.201806037.
[2] 杨存建,冯凉,杨洪忠,等.四川省林草火险等级评价[J].地理研究,2010,29(6):980-988.YANG C J,FENG L,YANG H Z,et al.Study of evaluation of forest and grass fire risk grade in Sichuan Province[J].Geogr Res,2010,29(6):980-988.
[3] TIAN X R, ZHAO F J, SHU L F, et al. Changes in forest fire danger for south-westhern China in the 21st century[J]. International Journal of Wildland Fire, 2014, 23(2): 185-195. DOI:10.1071/wf13014.
[4] 冯治学,陆愈实,孙艺博,等.云南电网山火灾害风险评估[J].自然灾害学报,2014,23(5):219-224.FENG Z X,LU Y S,SUN Y B,et al.Assessment of power grid risk caused by wildfire disaster in Yunnan Province[J].J Nat Disasters,2014,23(5):219-224.DOI:10.13577/j.jnd.2014.0528.
[5] 王秋华,舒立福,李世友.云南主要针叶林可燃物类型划分及特征[J].林业资源管理,2011(2):48-53.WANG Q H,SHU L F,LI S Y.Fuel types and characteristics in main coniferous forest in Yunnan Province[J].For Resour Manag,2011(2):48-53.DOI:10.3969/j.issn.1002-6622.2011.02.010.
[6] 王秋华,舒立福,李世友.云南松林燃烧过程中飞火的研究[J].中国安全生产科学技术,2011,7(1):48-53.WANG Q H,SHU L F,LI S Y.Study on spotting of Pinus yunnanensis forest during burning[J].J Saf Sci Technol,2011,7(1):48-53.DOI:10.3969/j.issn.1673-193X.2011.01.010.
[7] 王秋华, 徐盛基, 李世友, 等. 云南松林飞火形成的火环境研究[J]. 浙江农林大学学报, 2013, 30(2): 263-268. WANG Q H, XU S J, LI S Y, et al. Fire environment of spot fires in a Pinus yunnanensis forest[J]. Journal of Zhejiang A&F University, 2013, 30(2): 263-268.
[8] 田晓瑞, 赵凤君, 舒立福, 等. 西南林区卫星监测热点及森林火险天气指数分析[J]. 林业科学研究, 2010, 23(4): 523-529. TIAN X R, ZHAO F J, SHU L F, et al. Hotspots from satellite monitoring and forest fire weather index analysis for southwest China[J]. Forest Research, 2010, 23(4): 523-529.
[9] 田晓瑞, 舒立福, 赵凤君, 等. 未来情景下西南地区森林火险变化[J]. 林业科学, 2012, 48(1): 121-125. TIAN X R, SHU L F, ZHAO F J, et al. Forest fire danger changes for southwest China under future scenarios[J]. Scientia Silvae Sinicae, 2012, 48(1): 121-125.
[10] CHEN F, NIU S K, TONG X J, et al. The impact of precipitation regimes on forest fires in Yunnan Province, southwest China[J]. The Scientific World Journal, 2014(2014): 1-9. DOI:10.1155/2014/326782.
[11] 陈锋,林向东,牛树奎,等.气候变化对云南省森林火灾的影响[J].北京林业大学学报,2012,34(6):7-15.CHEN F,LIN X D,NIU S K,et al.Influence of climate change on forest fire in Yunnan Province,southwestern China[J].J Beijing For Univ,2012,34(6):7-15.DOI:10.13332/j.1000-1522.2012.06.010.
[12] YEBRA M,DENNISON P E,CHUVIECO E,et al.A global review of remote sensing of live fuel moisture content for fire danger assessment:Moving towards operational products[J].Remote Sens Environ,2013,136:455-468.DOI:10.1016/j.rse.2013.05.029.
[13] ZHANG H J,HAN X Y,DAI S.Fire occurrence probability mapping of northeast China with binary logistic regression model[J].IEEE J Sel Top Appl Earth Observations Remote Sensing,2013,6(1):121-127.DOI:10.1109/jstars.2012.2236680.
[14] ZHANG H J,QI P C,GUO G M.Improvement of fire danger modelling with geographically weighted Logistic model[J].Int J Wildland Fire,2014,23(8):1130.DOI:10.1071/wf13195.
[15] 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.WANG Z,JIN W Z.Fire danger modeling with geographically weighted Poisson model in Henan Province[J].J Nanjing For Univ(Nat Sci Ed),2015,39(5):93-98.DOI:10.3969/j.issn.1000-2006.2015.05.015.
[16] RIZOPOULOS D,MAX K H,KJELL J.Applied predictive modeling[J].Biometrics,2018,74(1):383.DOI:10.1111/biom.12855.
[17] 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.ZHANG H J.Spatial analysis of fire-influencing factors in Henan Province[J].Prog Geogr,2014,33(7):958-968.DOI:10.11820/dlkxjz.2014.07.011.
[18] WU W,ZHANG L J.Comparison of spatial and non-spatial logistic regression models for modeling the occurrence of cloud cover in north-eastern Puer to Rico[J].Appl Geogr,2013,37:52-62.DOI:10.1016/j.apgeog.2012.10.012.
[19] MARTíNEZ-FERNáNDEZ J,CHUVIECO E,KOUTSIAS N.Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression[J].Nat Hazards Earth Syst Sci,2013,13(2):311-327.DOI:10.5194/nhess-13-311-2013.
[20] RODRIGUES M,DE LA RIVA J,FOTHERINGHAM S.Modeling the spatial variation of the explanatory factors of human: caused wildfires in Spain using geographically weighted logistic regression[J].Appl Geogr,2014,48:52-63.DOI:10.1016/j.apgeog.2014.01.011.
[21] FEUILLET T,COQUIN J,MERCIER D,et al.Focusing on the spatial non-stationarity of landslide predisposing factors in northern Iceland[J].Prog Phys Geogr:Earth Environ,2014,38(3):354-377.DOI:10.1177/0309133314528944.
[22] ROY D P,BOSCHETTI L,JUSTICE C O,et al.The collection 5 MODIS burned area product:global evaluation by comparison with the MODIS active fire product[J].Remote Sens Environ,2008,112(9):3690-3707.DOI:10.1016/j.rse.2008.05.013.
[23] BISQUERT M,SáNCHEZ J,CASELLES V.Modeling fire danger in Galicia and Asturias(Spain)from MODIS images[J].Remote Sens,2014,6(1):540-554.DOI:10.3390/rs6010540.
[24] 范阔,谢士琴,陈玥璐,等.河北省围场县森林火险区划研究[J].西北林学院学报,2018,33(4):162-166.FAN K,XIE S Q,CHEN Y L,et al.Forest fire risk zone mapping in Weichang County of Hebei Province[J].J Northwest For Univ,2018,33(4):162-166.DOI:10.3969/j.issn.1001-7461.2018.04.27.
[25] WANG L T,ZHOU Y,ZHOU W Q,et al.Fire danger assessment with remote sensing:a case study in northern China[J].Nat Hazards,2013,65(1):819-834.DOI:10.1007/s11069-012-0391-2.
[26] CECCATO P,GOBRON N,FLASSE S,et al.Designing a spectral index to estimate vegetation water content from remote sensing data:part 1[J].Remote Sens Environ,2002,82(2/3):188-197.DOI:10.1016/s0034-4257(02)00037-8.
[27] WHEELER D C.Diagnostic tools and a remedial method for collinearity in geographically weighted regression[J].Environ Plan A,2007,39(10):2464-2481.DOI:10.1068/a38325.
[28] WHEELER D,TIEFELSDORF M.Multicollinearity and correlation among local regression coefficients in geographically weighted regression[J].J Geograph Syst,2005,7(2):161-187.DOI:10.1007/s10109-005-0155-6.
[29] 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].J Hist Geogr,2012,38(4):372-386.DOI:10.1016/j.jhg.2012.01.018.
[30] KAREGOWDA A G,JAYARAM M A,MANJUNATH A S.Combining Akaike’s information criterion(AIC)and the golden-section search technique to find optimal numbers of K-nearest neighbors[J].Int J Comput Appl,2010,2(1):80-87.DOI:10.5120/609-859.
[31] HURVICH C M,SIMONOFF J S,TSAI C L.Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion[J].J Royal Stat Soc:Ser B Stat Methodol,1998,60(2):271-293.DOI:10.1111/1467-9868.00125.
[32] PILZ J,SP?CK G.Why do we need and how should we implement Bayesian Kriging methods[J].Stoch Environ Res Risk Assess,2008,22(5):621-632.DOI:10.1007/s00477-007-0165-7.
[33] DORMANN C F,MCPHERSON J M,ARAJO M B,et al.Methods to account for spatial autocorrelation in the analysis of species distributional data:a review[J].Ecography,2007,30(5):609-628.DOI:10.1111/j.2007.0906-7590.05171.x.
[34] PEARCE J,FERRIER S.Evaluating the predictive performance of habitat models developed using logistic regression[J].Ecol Model,2000,133(3):225-245.DOI:10.1016/s0304-3800(00)00322-7.
[35] LE REST K,PINAUD D,BRETAGNOLLE V.Accounting for spatial autocorrelation from model selection to statistical inference:application to a national survey of a diurnal raptor[J].Ecol Informatics,2013,14:17-24.DOI:10.1016/j.ecoinf.2012.11.008.
[36] CROMLEY R G,HANINK D M.Visualizing robust geographically weighted parameter estimates[J].Cartogr Geogr Inf Sci,2014,41(1):100-110.DOI:10.1080/15230406.2013.831205.
[37] KOUTSIAS N,MARTíNEZ-FERNáNDEZ J,ALLG?WER B.Do factors causing wildfires vary in space?evidence from geographically weighted regression[J].Giscience Remote Sens,2010,47(2):221-240.DOI:10.2747/1548-1603.47.2.221.
[38] GUO L,MA Z H,ZHANG L J.Comparison of bandwidth selection in application of geographically weighted regression:a case study[J].Can J For Res,2008,38(9):2526-2534.DOI:10.1139/x08-091.


收稿日期:2018-08-04 修回日期:2019-03-20基金项目:国家重点研发计划(2016YFD0600203)。 第一作者:王周(xbygis@163.com),高级工程师,ORCID(0000-0001-6094-7288)。
更新日期/Last Update: 2019-03-25