A comprehensive spatial-temporal analysis of driving factors of human-caused wildfires in Spain using Geographically Weighted Logistic Regression

Abstract

Over the last decades, authorities responsible on forest fire have encouraged research on fire triggering factors, recognizing this as a critical point to achieve a greater understanding of fire occurrence patterns and improve preventive measures. The key objectives of this study are to investigate and analyze spatial-temporal changes in the contribution of wildfire drivers in Spain, and provide deeper insights into the influence of fire features: cause, season and size. We explored several subsets of fire occurrence combining cause (negligence/accident and arson), season (summer-spring and winter-fall) and size (<1 Ha, 1-100 Ha and >100 Ha). The analysis is carried out fitting Geographically Weighted Logistic Regression models in two separate time periods (1988-1992, soon after Spain joined the European Union; and 2006-2010, after several decades of forest management). Our results suggest that human factors are losing performance with climate factors taking over, which may be ultimately related to the success in recent prevention policies. In addition, we found strong differences in the performance of occurrence models across subsets, thus models based on long-term historical fire records might led to misleading conclusions. Overall, fire management should move towards differential prevention measurements and recommendations due to the observed variability in drivers' behavior over time and space, paying special attention to winter fires.

Driving Factors of Land Change in China’s Loess Plateau: Quantification Using Geographically Weighted Regression and Management Implications

“…Typically, only global-level statistical approaches were applied, under the assumption that relationships between explanatory factors and dependent variables are constant (homogenous) over space [24]. However, many socioeconomic drivers are known to exhibit spatial non-stationarity and to have distinct temporal signatures [25,26]. Brunsdon et al (1996) proposed Geographically Weighted Regression (GWR) as a method to explore spatial heterogeneity [27].…”

Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications

“…Those trends might be explained by the anomalies in the trajectory of the North Atlantic Jet [101], which could be related to a decrease of the cloudy atmosphere or which could provoke more severe droughts in some critical months of the year for the growth of vegetation. Other factors could be land degradation and insect plagues, in addition to the high rates of land abandonment and fragmentation, severe overgrazing, and the increase in wildfire occurrence in recent decades [109][110][111]. Furthermore, in this area, other studies suggest that the Fagus sylvatica species in the northern zones of Spain are less resistant to climate change than those located in the southern regions of Spain [112].…”

Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)

“…Recently, anomalies in the trajectory of the North Atlantic jet stream [109] were found that could be related to a decrease in the cloudy atmosphere or that could provoke more severe droughts in some critical months of the year for the growth of vegetation. Other authors have reported that Cordillera Cantábrica suffered a land degradation with high rates of land abandoned and fragmented, a severe overgrazing, and an increase in wildfire occurrence during the past few decades [110][111][112]. Other studies suggest that the Fagus sylvatica species in northern zones of Spain is less resistant to climatic change than those located in southern areas of Spain [113].…”