Assessing spatial scale effects in multi-sensor fire fuel mapping in the heterogeneous landscapes of Tasmania

Effective fire-risk management in Tasmania requires vegetation maps that capture both broad fuel patterns and small, highly flammable gorse (Ulex europaeus) infestations. Yet gorse often occurs in fragmented patches that disappear in coarser land-cover products, raising uncertainty about how much fuel information is lost when regional maps are produced at moderate or coarse resolution. To clarify these scale effects we compare Object-Based Image Analysis-Random Forest fuel mapping at 0.5, 3 and 10 m in a heterogeneous Tasmanian agricultural landscape using fused optical, LiDAR and SAR features. Beyond accuracy at each scale, we quantify how classes merge, disappear, or persist between resolutions using transfer matrices and analyse how large a gorse patch must be to remain detectable at coarser scales. F1 scores are consistently high across scales (76%-99%), yet class-level behaviour differs substantially. The 3 m model achieves the highest gorse classification performance while maintaining geometric coherence of these shrub patches. When transferred from 0.5 m, 76% of fine-scale gorse area remains represented at 3 m, compared to only 36.8% at 10 m. Detection probability at 3 m increases monotonically with patch size, whereas at 10 m even large patches (10,000-30,000 m) are detected in only 60% of the cases. These results demonstrate that high within-scale accuracy does not guarantee cross-scale persistence of fine-grained fuels. 3 m resolution provides optimal scale–patch alignment for regional fuel-zone delineation in Tasmania, whereas sub-metre imagery is required for explicit identification of individual gorse infestations. Overall, the results confirm that spatial aggregation disproportionately affects narrow and fragmented vegetation types. Resolution choice is therefore not merely a technical setting, but a decisive factor in whether hazardous fine fuels remain visible in regional fuel assessments.