Vegetation characteristics significantly influence the impact of wildfires on individual building structures, and these effects can be systematically analyzed using heat transfer modelling software. Close-range light detection and ranging (LiDAR) data obtained from uncrewed aerial systems (UASs) capture detailed vegetation morphology; however, the integration of dense vegetation and merged canopies into three-dimensional (3D) models for fire modelling software poses significant challenges. This study proposes a method for integrating the UAS–LiDAR-derived geometric features of vegetation components—such as bark, wooden core, and foliage—into heat transfer models. The data were collected from the natural woodland surrounding an elevated building in Samford, Queensland, Australia. Aboveground biomass (AGB) was estimated for 21 trees utilizing three 3D tree reconstruction tools, with validation against biomass allometric equations (BAEs) derived from field measurements. The most accurate reconstruction tool produced a tree mesh utilized for modelling vegetation geometry. A proof of concept was established with Eucalyptus siderophloia, incorporating vegetation data into heat transfer models. This non-destructive framework leverages available technologies to create reliable 3D tree reconstructions of complex vegetation in wildland–urban interfaces (WUIs). It facilitates realistic wildfire risk assessments by providing accurate heat flux estimations, which are critical for evaluating building safety during fire events, while addressing the limitations associated with direct measurements.

The increasing frequency and intensity of wildfires pose severe risks to ecosystems, infrastructure, and human safety. In wildland–urban interface (WUI) areas, nearby vegetation strongly influences building ignition risk through flame contact and radiant heat exposure. However, limited research has leveraged Unmanned Aerial Systems (UAS) remote sensing (RS) to capture species-specific vegetation geometry and predict thermal responses during ignition events This study proposes a two-stage framework integrating UAS-based multispectral (MS) imagery, LiDAR data, and Fire Dynamics Simulator (FDS) modeling to estimate the maximum temperature (T) and heat flux (HF) of outdoor vegetation, focusing on Syzygium smithii (Lilly Pilly). The study data was collected at a plant nursery at Queensland, Australia. A total of 72 commercially available outdoor vegetation samples were classified into 11 classes based on pixel counts. In the first stage, ensemble learning and watershed segmentation were employed to segment target vegetation patches. Vegetation UAS-LiDAR point cloud delineation was performed using Raycloudtools, then projected onto a 2D raster to generate instance ID maps. The delineated point clouds associated with the target vegetation were filtered using georeferenced vegetation patches. In the second stage, cone-shaped synthetic models of Lilly Pilly were simulated in FDS, and the resulting data from the sensor grid placed near the vegetation in the simulation environment were used to train an XGBoost model to predict T and HF based on vegetation height (H) and crown diameter (D). The point cloud delineation successfully extracted all Lilly Pilly vegetation within the test region. The thermal response prediction model demonstrated high accuracy, achieving an RMSE of 0.0547 °C and R² of 0.9971 for T, and an RMSE of 0.1372 kW/m² with an R² of 0.9933 for HF. This study demonstrates the framework’s feasibility using a single vegetation species under controlled ignition simulation conditions and establishes a scalable foundation for extending its applicability to diverse vegetation types and environmental conditions.