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In Mediterranean environments, shrub vegetation is a critical driver of wildfire dynamics, contributing substantially to overall fuel loads. However, characterizing and quantifying this component remains a significant challenge because of its 3-dimensional complexity. This study presents a density-based approach using mobile laser scanning (MLS), equipped with Simultaneous Localization and Mapping (SLAM), derived point clouds to characterize above-ground shrub dry mass. The retrieved density metric was employed as a fuel load predictor for linear, polynomial, k nearest neighbour (KNN), and support vector machine (SVM) regression models. Field campaigns provided diameter-based fuel classifications and physical parameters (e.g., dry/fresh weight, moisture) for models validation. Results highlighted stronger correlations for fine fuel classes (diameter ≤ 2.5 cm), which are more prone to fire risk, underscoring the method’s potential to enhance wildfire prevention through accurate, scalable fuel characterization in complex Mediterranean landscapes.
Arcidiaco et al. (2025) studied this question.