Work

Estimation of Burned Fuel Volumes in Heathland Ecosystems Using Multitemporal UAV LiDAR and Superpixel Classification

Year: 2025

Type: article

Abstract: Accurate quantification of wildland fuel consumption is essential for effective fire management in Northern European heathland ecosystems, yet traditional assessment methods remain spatially limited and labour-intensive. This study combined multitemporal UAV LiDAR with SLIC superpixel-based classification to directly measure fuel consumption following a prescribed burn in a Belgian heathland. Pre- and post-fire LiDAR surveys were conducted to capture vegetation height changes. Superpixel segmentation successfully classified three vegetation types (grassland, heather and trees with understory vegetation) with 97.8% accuracy. Fuel consumption analysis revealed remarkable differences between vegetation types, with heather (mean ± SD: 0.165 ± 0.102 m) exhibiting the highest consumption compared to grass (0.089 ± 0.088 m) and tree understory vegetation (0.091 ± 0.068 m). Statistical analysis confirmed the significant differences between all vegetation types (p-value < 0.001). This methodology provides quantitative evidence for developing vegetation-specific burning protocols by demonstrating the critical importance of both pre- and post-fire remote sensing data. The approach demonstrates the effectiveness of UAV-based multitemporal LiDAR for precise fuel consumption assessment in heathland fire management.

Source: Drones

DOI: 10.3390/drones9090615