Small-scale Forest Fire Danger Rating Prediction Model

Forest fire is a damage which is comprehensively caused by a variety of natural and socialfactors, and its grade level is utalized to measure the mutiple characteristics of the forest fireincluding the probability of occurrence, spread rate, release energy intensity, the difficultdegree of control and the damage loss. Forest fire forecasting is a fundmental work of forestfire management, and the direction and scientificity of forest fire prevention are always decidedby the accuracy and timeliness of forest fire forecasting.The features of the existing forest fire danger rating prediction methods are: the nationalforest fire weather rating is forecasted based on the meteorological factors data on thelarge-scale, the likelihood of fires occurring is predicted by exploiting meteorological factorsand wildland fuel moisture content and the forest fire behavior is prognosised by unilaterallyconsidering the combustion characteristics of fuel on the mesoscale and the small-scale.According to statistics, the man-caused fire is one of the main reason in forest fires, and thesmall area of forest, which are always distirbuted near the human settlesments and withfrequent human activities, are the places of the high incidence of forest fires. However, it is noteffective to deduct the results of the large-scale and mesoscale forest fire danger ratingpredition to the small-scale, meanwhile the difference of natural and social environment issignificant in forest resource subcompartments which is a typical representative of thesmall-scale. Forest fire impact factors in forest resource subcompartment includingmeteorology, fuel combustion resistance and moisture content, terrain and the man-made factorare tightly related, and the absence of any one factor will locally strengthen or weaken theforest fire danger rating, thereby the accuracy of prediction of forest fire will be affected. Withthe advent of the era of big data, it is a new way for the data acquisition of various forest firesfactors on small-scale by making use of the wireless sensor networks for the internet of thingsto help improve the accuracy and timeliness of forest fire prediction. Fire environmental factors, combustible materials factors and fire sources factors weresummarized, and the influence of these three factors acting on the forest fire danger rating andthe relationship between factors were analyzed. Then, the wildland fuel classification methodwas hierarchically builded to meet the requirement of small-scale forest fire danger ratingprediction. Meanwhile, the estimation model of various fire impact factors were constructed,and then the construction method of a Small-scale Forest Fire Danger Rating PredictionModel(SFFDRPM) was proposed. Finally, Jiulong mountain district forestry experiment centerof north China in beijing city was taken as the study area, and a small-scale forest fire dangerrating prediction of which was achieved. Thus, the main results and conclusions are as follows:(1) There are a great number of forest fire impact factors, and the accuracy of the firedanger rating prediction model was essentially determined by the selection of factors. Theimpact factors on forest fire were classified, and the way of these factors impacting on theforest fire was analyzed in detail. Then, the interaction relationship between each factor wereexplored. Thus, the factors selection theoretical basis for the SFFDRPM was provided.(2) Not only are forest fire environmental factors the pregnant disaster environment, butalso the differences basis of other forest fires impact factors. The small-scale topographicalinformation extraction methods were established by the integrated application of GIS, RS andstatistical theory based on the DEM data and forest resource inventory data. At the same time,the meteorological data of ENVIS gradient stations were collected and the acquiredtopographical factors data were combined to amend the key parameters in MTCLIM(MountainMicroclimate Simulation Model), which was utalized to simulate the small mountainenvironment temperature and humidity. Meanwhile, the average wind speed in forestsubcompartment were obtained by applying the simulation algorithm of WindNinja software.Ultimately, the weather information in mountain microenvironment were simulated to providethe method basis of the fire environment factors calculation.(3) The forest wildland fuel classification method was established to provide the basis ofthe factors calculation in the SFFDRPM.Then, the combustion index calculation method in forest subcompartment was proposed based on the intrinsic properties in physical and chemicalproperties of forest wildland fuel, and the moisture content of the initial ignition of forestwildland fuel was measured to provide factor data for the SFFDRPM.(4) The tree shrub biomass estimation model, surface litter undecomposed layer loadestimation model and semi-decomposed layer load estimation model were builded based onmultiple regression analysis. Meanwhile, the typical shrub biomass estimation model wasconstructed to achieve a quick biomass estimation in a wide range of shrub and effectivelyavoid the tedious process of independent variables and models selection in shrub biomassmodeling based on Genetic Algorithm optimized Back Propagation (GA-BP). And then, theload calculation methods of tree layer, shrub layer, surface litter undecomposed layer andhalf-decomposed layer in forest subcompartment were proposed for the fuel load computingmethods reference in the SFFDRPM.(5) The moisture content models of surface litter undecomposed layer andsemi-decomposed layer were proposed by exploiting multivariate stepwise regression analysisbased on time series analysis theory for each tree specie, and the wildland fuel moisturecalculation method in forest subcompartment was constructed to achieve the fuel moisturedynamic prediction by combining with meteorological data in mountain microenvironment.(6) The construction method of the SFFDRPM was established. The small-scale forest fireimpact factors were comprehensively considered to select input variables in the SFFDRPM,and the forest fire danger indexs were calculated based on the the principal component scorefunction, which was fitting by using factor analysis method to eliminate factorsmulticollinearity and extract principal components. Then, the forest fire danger rating in eachforest subcompartment was clustered by making use of cluster analysis method. Finally, theSFFDRPM in the study area was created by applying the GA-BP.