Fire Occurrence Probability Mapping of Northeast China With Binary Logistic Regression Model

Abstract

Fire occurrence probability mapping provides a detailed understanding of the spatial distribution of the fire occurrence probability and it is useful in fire management. The binary logistic regression (BLR) can combine continuous and categorical variables together in the analysis. Here we use BLR analysis to map the fire occurrence probability of Northeast China which has the largest forest area in China. Ten predictor variables including altitude (Alt), slope (Sl), aspect (As), distance to the nearest village (Dv), distance to the nearest path (Dp), distance to the nearest water bodies (Dw), land cover (LC), Fuel Moisture Content (FMC), land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) are employed and multi-temporal random sampling methodology is used to create the training subset, and then the training subset is utilized to build the fire occurrence probability spatial model. Here, a backwards stepwise procedure based on the likelihood ratio estimation is used in the model development. Assessed by the area under a relative operating characteristic (ROC) curve (AUC-area under curve) procedure, the model's fitness accuracy is 84.2%. The interpretations of the estimated coefficients show that NDVI best explain fire occurrence in the region. Evaluated by the inner testing and independent validation, better reliability and discrimination capacity of the developed spatial model can be concluded from 17 fires among the total 18 fires. Good performance suggests that the developed model is valuable to fire managers or can be directly applied to fire management in Northeast China.Index Terms-Fire danger, fire occurrence probability, logistic regression, Northeast China.

Human-caused fire occurrence modelling in perspective: a review

“…However, undetected, unreported or missing fires are a common problem in many countries, because of a lack of managerial resources and peak fire loads, differing policies on minimum reporting of fire size or fire start location in remote underpopulated regions with low values at risk (Lefort et al 2004). When field-collected fire records are unavailable, fire occurrence can be estimated from remote sensing sources such as burned area products or hotspots (Venevsky et al 2002;Vadrevu et al 2006;Maingi and Henry 2007;Chuvieco et al 2008;Garcia-Gonzalo et al 2012;Marques et al 2012;Zhang et al 2013;Li et al 2014;Bedia et al 2015;Ancog et al 2016). All models built from remote sensing data have had to consider a certain minimum fire size because of technical limitations in sensor spatial or spectral resolution, including, for example, fires .400 ha (Preisler and Westerling 2007;West et al 2016), fires .0.25 ha (Stolle et al 2003) or fires .0.1 ha (Miranda et al 2012).…”

“…Dlamini (2010) and Romero-Calcerrada et al (2008) concluded that intermediate livestock densities were associated with an increased occurrence of HCFs in Swaziland and central Spain respectively. Shrub removal by fire for pasture regeneration tends to be performed in rural areas with a lower population density than and further away from metropolitan areas Stolle et al 2003;Sitanggang et al 2013;Zhang et al 2013).…”

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

“…In the past decade, many different statistical methods have been applied to identify fire driving factors and establish fire prediction models by considering all possible environmental, topographic, climatic and infrastructure factors. These include the artificial neural network [7], the maxent algorithm [8], the autoregressive model [9], classification trees [10], global logistic regression [11][12][13][14][15][16][17][18][19], multiple linear regression and random forest [20][21][22], of which logistic regression is the most commonly used tool.…”

“…On the other hand, improved 3S technology (Remote Sensing, RS; Geographical Information System, GIS; Global Position System, GPS) enable the application of large spatial information of factors such as topography, vegetation, and climate for fire modeling [17][18][19]21,23,24], which provides a valuable contribution to the improvement of fire management and prevention strategies. However, the most commonly used methods mentioned above have not fully considered the spatial heterogeneity of the relationship between fire occurrence and its potential drivers, but have instead assumed that model parameters are valid and homogeneous for the entire study area, or assumed that the models are spatially stationary or non-spatial.…”

Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images

“…In [24] logistic regression is used to assess the risk of forest fire by combining different static and dynamic variables. In [25], 10 predictor variables, including continuous and categorical variables, were combined using the binary logistic regression for mapping fire occurrence probability.…”