Sophia Hoyer , Anke Fluhrer , Florian Hellwig , Steve Harwin , Jukka Matthias Krisp , Thomas Jagdhuber
{"title":"Assessing spatial scale effects in multi-sensor fire fuel mapping in the heterogeneous landscapes of Tasmania","authors":"Sophia Hoyer , Anke Fluhrer , Florian Hellwig , Steve Harwin , Jukka Matthias Krisp , Thomas Jagdhuber","doi":"10.1016/j.rsase.2026.101996","DOIUrl":null,"url":null,"abstract":"<div><div>Effective fire-risk management in Tasmania requires vegetation maps that capture both broad fuel patterns and small, highly flammable gorse (<em>Ulex europaeus</em>) 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<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) 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.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101996"},"PeriodicalIF":4.5000,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938526001291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/4/6 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems