Evaluating Lightning ‐Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain

In this study, we analyze the effects of different biophysical variables and lightning characteristics on lightning ‐caused forest wildfires in Castilla y León region (Central Spain). The presence/absence of at least one lightning‐caused fire in any 4 × 4‐km grid cell was used as a dependent variable and vegetation type and structure, terrain, climate, and lightning characteristics were used as possib le covariates. Five prediction methods were compared: a generalized linear model (GLM), a random forest model (RFM), a generalized additive model (GAM), a GAM that includes a spatial trend function (GAMs) and a spatial autoregressive model (AUREG). A GAMs with just one covariate, apart from longitud e and latitude for each observation included as a combined effect, was considered the most appropriate model in terms of both predictive ability and simplicity. According to our results, the probability of a forest being affected by a lightning‐caused fire is positively and nonlinearly associated with the percentage of coniferous woodlands in the landscape, suggesting that occurrence is more closely associated with vegetation type than with topography, climate, or lightning characteristics. The selected GAMs is intended to inform the Regional Government of Castilla y León (the fire and fuel agency in the region) regarding identification of areas at greatest risk so it can design long‐term forest fuel and fire management strategies.
Source: Risk Analysis - Category: International Medicine & Public Health Authors: Tags: Original Research Article Source Type: research