Point cloud density approach to characterize and estimate shrub fuel load in the mediterranean environments using mobile laser scanning

• Novel local density metric from MLS-SLAM clouds to estimate Mediterranean shrub fuel load. • Refined mesh volumes via point-cloud density to overcome segmentation limits in dense shrubby. • Prediction models (linear, polynomial, KNN, SVM) perform best for finer diameter fuel classes. • Field sampling supported training & validations with promising results but requires broader tests. 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.