Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China

Abstract

Forest fires are among the major natural disasters that destroy the balance of forest ecosystems. The construction of a forest fire prediction model to investigate the driving mechanism of fire drivers on forest fires can help reveal the mechanism of forest fire occurrence and its risk, and thus contribute to the prevention and control of forest fires. However, previous studies on the mechanisms of forest fire drivers have not considered the effect of differences in spatial scale of action of forest fire drivers on the predicted effect. Therefore, the present study proposes a spatial prediction model of forest fires that considers the spatial scale effect of forest fire drivers to predict forest fire risk. First, based on historical forest fire data and geographic environmental data in the Yunnan Province, geographically weighted logistic regression (GWLR) was used to determine the forest fire drivers and to estimate the probability of forest fire occurrence at locations where fire observations are absent. Then, multi-scale geographically weighted regression (MGWR) was used to explore the spatial scales of action of different drivers on forest fires. The results show that meteorological factors such as relative humidity, air temperature, air pressure, sunshine hours, daily precipitation, wind speed, topographic factors such as elevation, slope, and aspect, anthropogenic factors such as population density and road network, as well as vegetation type, were significantly correlated with forest fires; thus, they are identified as important factors influencing occurrence of forest fires in the Yunnan Province. The MGWR model regression results show that the role of different forest fire drivers on forest fire occurrence has spatial scale differences. The spatial scale of drivers such as altitude, aspect, wind speed, temperature, slope, and distance from the road to the fire point was larger and their spatial influence was relatively stable, with spatial heterogeneity having less influence on the model evaluation results. The spatial scale of drivers such as relative humidity, sunshine, air pressure, precipitation, population density, and vegetation type were smaller, and spatial heterogeneity had a more obvious influence on the model evaluation results. This study provides a reference for selecting drivers and evaluating their spatial scale effects to construct predictive regional forest fire models.

Comparing machine learning algorithms to predict vegetation fire detections in Pakistan

“…Our analysis incorporates a detailed evaluation of meteorological variables such as average annual daily high temperature, annual average relative humidity, total annual precipitation, and average annual wind speed. We also considered broader climatic factors, topological features, and different vegetation types as significant determinants of fire risk (Li et al 2022). In this study, we selected nine variables for analyzing forest and crop fires and seven for other types of vegetation based on their demonstrated association with fire occurrences and their statistical significance in preliminary models.…”

“…Analysis of multicollinearity involves assessing variance inflation factors (VIF) and tolerance levels (TOL), which are commonly utilized to evaluate the relationships among independent variables. It is widely acknowledged that a TOL value below 0.1 and a VIF value exceeding 10 indicate the presence of multicollinearity (Bui et al 2019;Li et al 2022). These thresholds suggest that multicollinearity could significantly impact the reliability of regression and classification model estimates.…”

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

“…Since the geographical distribution of forest fires and their influencing factors is highly heterogeneous in space, the relationship between them has significant spatial instability [69]. Therefore, in future work, we will consider adding a geographically weighted regression model for comparative studies, which incorporates spatial location information in the regression parameters and is capable of conducting the spatial analysis of the influencing factors and spatial prediction of forest fires.…”

Assessment of Burn Severity and Monitoring of the Wildfire Recovery Process in Mongolia

“…Other issues and different phenomena occur in different areas within different natural zones around the world. We collected and reviewed a few studies from different study areas, including Siberia, Russia [23,25,26], Indonesia [27], Canada [28,29], Australia [18,[30][31][32], Spain [33], Portugal [13], the Mediterranean [7,[34][35][36][37], Turkey [1,4], Greece [2,3], China [10,[38][39][40][41], California and Alaska [42][43][44], the US [45][46][47][48][49], Peru [14], Iran [50], Bolivia [51], the Amazon of Brazil [52] and India [53]. The wildfire studies from each country had their own characteristics.…”