A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis

Abstract

Increased demand for sustainable timber products has resulted in large investments in agroforestry in Australia, with plantations growing various Pinus species, selected to suit a plantation’s environment. Juvenile Pinus species have a low fire tolerance. With Australia’s history of wildfires and the likelihood of climate change exacerbating that risk, the potential for a total loss of invested capital is high unless cost-effective targeted risk minimisation is part of forest management plans. Based on the belief that the understory profiles within the juvenile plantations are a major factor determining fuel hazard risks, an accurate assessment of these profiles is required to effectively mitigate those risks. At present, assessment protocols are largely reliant on ground-based observations, which are labour-intensive, time consuming, and expensive. This research project investigates the effectiveness of using geospatial analysis of drone-derived photographic data collected in the commercial pine plantations of south-eastern Queensland as a cost-saving alternative to current fuel hazard risk assessment practices. Understory composition was determined using the supervised classification of orthomosaic images together with derivations of canopy height models (CHMs). The CHMs were subjected to marker-controlled watershed segmentation (MCWS) analysis, isolating and removing the plantation pine trees, enabling the quantification of understory fuel profiles. The method used proved highly applicable to immature forest environments with minimal canopy closure, but became less reliable for close canopied older plantations.

Code and data availability

The supplied blocks describe drone imagery, field quadrat measurements, and an ArcGIS/Agisoft/ENVI analysis workflow, but contain no public phenotype/trait dataset, no deposited images or sensor inputs, and no author analysis code, scripts, workflows, or trained models with availability language or public URLs. All URL

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