Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Zhangwen Su,Haiqing Hu,Guangyu Wang,Aicong Zeng,Futao Guo,Mulualem Tigabu

doi:10.3390/f10050377

Abstract

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

Highlights

Fires are an important ecological factor that affect the renewal and succession of forest vegetation and cause damage to forest ecosystems [1]
Estimated coefficients of the full variables in both models show that slope, railway density, average relative humidity, and average temperature were positively correlated with the occurrence of wildfire; whereas road density, settlement areas, average precipitation, vegetation cover, and GDP were negatively correlated with wildfire occurrence (Table 2)
While the relationship between elevation and fire occurrence was negative in global negative Binomial (NB) model, it was positive in geographically weighted negative Binomial regression (GWNBR) model

Summary

Introduction

Fires are an important ecological factor that affect the renewal and succession of forest vegetation and cause damage to forest ecosystems [1]. Forests 2019, 10, 377 in the region and establishing a wildfire occurrence prediction model has become the key to local wildfire management. Many studies found that environmental factors such as meteorology, vegetation, and topography play a key role in the occurrence and spread of wildfires [5,6,7,8,9,10,11]. Changes in meteorological factors, such as temperature, relative humidity, and precipitation, influence the moisture content of the fuel, which in turn affects the ignition and spread of wildfires [9,12,13]. Topographic variation affects the composition of vegetation and the spatial distribution of fuel, forming different fire environments, directly affecting the occurrence and the spread of wildfires [14]. Human activities and socio-economic conditions, such as population density and human settlements, have a significant effect on wildfire occurrence [6]

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Methods

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Discussion

Conclusion