A Mixed Methods Approach for Fuel Characterisation in Gorse (<i>Ulex europaeus</i> L.) Scrub from High-Density UAV Laser Scanning Point Clouds and Semantic Segmentation of UAV Imagery

Abstract

The classification and quantification of fuel is traditionally a labour-intensive, costly and often subjective operation, especially in hazardous vegetation types, such as gorse (Ulex europaeus L.) scrub. In this study, unmanned aerial vehicle (UAV) technologies were assessed as an alternative to traditional field methodologies for fuel characterisation. UAV laser scanning (ULS) point clouds were captured, and a variety of spatial and intensity metrics were extracted from these data. These data were used as predictor variables in models describing destructively and non-destructively sampled field measurements of total above ground biomass (TAGB) and above ground available fuel (AGAF). Multiple regression of the structural predictor variables yielded correlations of R2 = 0.89 and 0.87 for destructively sampled measurements of TAGB and AGAF, respectively, with relative root mean square error (RMSE) values of 18.6% and 11.3%, respectively. The best metrics for non-destructive field-measurements yielded correlations of R2 = 0.50 and 0.49, with RMSE values of 40% and 30.8%, for predicting TAGB and AGAF, respectively, indicating that ULS-derived structural metrics offer higher levels of precision. UAV-derived versions of the field metrics (overstory height and cover) predicted TAGB and AGAF with R2 = 0.44 and 0.41, respectively, and RMSE values of 34.5% and 21.7%, demonstrating that even simple metrics from a UAV can still generate moderate correlations. In further analyses, UAV photogrammetric data were captured and automatically processed using deep learning in order to classify vegetation into different fuel categories. The results yielded overall high levels of precision, recall and F1 score (0.83 for each), with minimum and maximum levels per class of F1 = 0.70 and 0.91. In conclusion, these ULS-derived metrics can be used to precisely estimate fuel type components and fuel load at fine spatial resolutions over moderate-sized areas, which will be useful for research, wildfire risk assessment and fuel management operations.

Keywords

• fuel load
• UAV
• lidar
• fuel classification
• deep learning
• semantic segmentation