Use of airborne LiDAR to predict fine dead fuel load in Mediterranean forest stands of Southern Europe

Abstract

Mediterranean forests are increasingly threatened by wildfires, with fuel load playing a crucial role in fire dynamics and behaviors. Accurate fuel load determination contributes substantially to the wildfire monitoring, management, and prevention. This study aimed to evaluate the effectiveness of airborne Light Detection and Ranging (LiDAR) data in estimating fine dead fuel load, focusing on the development of models using LiDAR-derived metrics to predict various categories of fine dead fuel load. The estimation of fine dead fuel load was performed by the integration of field data and airborne LiDAR data by applying multiple linear regression analysis. Model performance was evaluated by the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). Through multiple linear regression models, the study explored the relationship between LiDAR-derived height and canopy cover metrics and different types of fine dead fuel load (1-h, 10-h, 100-h fuel loads, and litter). The accuracy of these models varied, with litter prediction showing the highest accuracy (R2 = 0.569, nRMSE = 0.158). In contrast, the 1-h fuel load prediction was the least accurate (R2 = 0.521, nRMSE = 0.168). The analysis highlighted the significance of specific LiDAR metrics in predicting different fuel loads, revealing a strong correlation between the vertical structure of vegetation and the accumulation of fine dead fuels. The findings demonstrate the potential of airborne LiDAR data in accurately estimating fine dead fuel loads in Mediterranean forests. This capability is significant for enhancing wildfire management, including risk assessment and mitigation. The study underscores the relevance of LiDAR in environmental monitoring and forest management, particularly in regions prone to wildfires.