Wildfire response of forest species from multispectral LiDAR data. A deep learning approach with synthetic data

Abstract

Forests play a crucial role as the lungs and life-support system of our planet, harbouring 80% of the Earth's biodiversity. However, we are witnessing an average loss of 480 ha of forest every hour because of destructive wildfires spreading across the globe. To effectively mitigate the threat of wildfires, it is crucial to devise precise and dependable approaches for forecasting fire dynamics and formulating efficient fire management strategies, such as the utilisation of fuel models.The objective of this study was to enhance forest fuel classification that considers only structural information, such as the Prometheus model, by integrating data on the fire responses of various tree species and other vegetation elements, such as ground litter and shrubs. This distinction can be achieved using multispectral (MS) Light Detection and Ranging (LiDAR) data in mixed forests. The methodology involves a novel approach in semantic classifications of forests by generating synthetic data with semantic labels regarding fire responses and reflectance information at different spectral bands, as a real MS scanner device would detect. Forests, which are highly intricate environments, present challenges in accurately classifying point clouds. To address this complexity, a deep learning (DL) model for semantic classification was trained on synthetic point clouds in different formats to achieve the best performance when leveraging MS data.Forest plots in the study region were scanned using different Terrestrial Laser Scanning sensors at wavelengths of 905 and 1550 nm. Subsequently, an interpolation process was applied to generate the MS point clouds of each plot, and the trained DL model was applied to classify them. These classifications surpassed the average thresholds of 90% and 75% for accuracy and intersection over union, respectively, resulting in a more precise categorisation of fuel models based on the distinct responses of forest elements to fire. The results of this study reveal the potential of MS LiDAR data and DL classification models for improving fuel model retrieval in forest ecosystems and enhancing wildfire management efforts.