Human-caused fire occurrence modelling in perspective: a review

Abstract

Abstract. The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.

Links Between Carbon Monoxide and Climate Indices for the Southern Hemisphere and Tropical Fire Regions

“…While ignition of fires in the present era is predominantly due to humans (Bowman et al, 2011;Costafreda-Aumedes et al, 2017), fire intensity and burned area are related to the amount, type, and dryness of available fuel, which respond to climate-driven water availability combined with ecosystem responses (van der Werf et al, 2008). Consequently, the magnitude of emissions from biomass burning, such as for CO, is connected to climate variability.…”

Modeling the Influence of Eucalypt Plantation on Wildfire Occurrence in the Brazilian Savanna Biome

“…Nonetheless, fire is also a side-effect arising from the accidental and unintentional ignitions or arson fires [6,14]. Wildfire events are connected to the expansion of urban interfaces and increasing accessibility into the wildlands [15][16][17]. The proximity to those features increases the probability of a fire to occur [15].…”

“…Understanding the role of driving factors of fire ignitions and predicting where wildfires are most likely to start is essential to design strategies for wildfire impact mitigation or to identify regions at risk [24]. In this context, wildfire occurrences have been extensively investigated, with researchers attempting to identify which environmental and socioeconomic factors foster fire occurrence, using broadly approaches and goals, such as fire prevention, supporting strategies and policies for fire, forest or land management [16]. Wildfire occurrence is considered one of the main components of wildfire risk assessment [25].…”

“…Nevertheless, the increase of computer power and the availability of detailed spatial datasets promoted the use of complex techniques, such as classification and regression trees, artificial neural networks, support vector machines and other machine learning algorithms. These robust methods have been introduced as alternatives to traditional statistical methods, especially when dealing with large datasets, non-linear relationships, and variables that are highly correlated or not normally distributed [16]. Wildfire occurrences have also been analyzed with spatial-temporal points processes statistical tools to model wildfire spatial-likelihood.…”

“…Wildfire occurrences have also been analyzed with spatial-temporal points processes statistical tools to model wildfire spatial-likelihood. The spatial point processes framework has the advantage to deal with micro-geographic data and to model, parametrically or not, the spatial-temporal trend and the interaction structure between the points [16,[32][33][34][35]. Random Forest (RF) has been extensively used to model wildfire occurrence for large datasets.…”