Nationally consistent mapping of wildland fuel types across Australia using satellite-derived vegetation structural data

Rakesh C. Joshi; Miguel G. Cruz; Randall J. Donohue

doi:10.1071/WF24224

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 34(5)

Nationally consistent mapping of wildland fuel types across Australia using satellite-derived vegetation structural data

Rakesh C. Joshi ^A ^* , Miguel G. Cruz

^B and Randall J. Donohue ^B

+ Author Affiliations

- Author Affiliations

^A CSIRO, Private Bag 10, Clayton South, Vic 3169, Australia.

^B CSIRO, GPO Box 1700, Canberra, ACT 2601, Australia.

^* Correspondence to: rakesh.joshi@csiro.au

International Journal of Wildland Fire 34, WF24224 https://doi.org/10.1071/WF24224

Submitted: 20 December 2024 Accepted: 7 April 2025 Published: 1 May 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Knowledge of the distribution of wildland fuels across the landscape is necessary for the appropriate application of models used to support a broad range of fire management activities.

Aims

To develop an automated and nationally consistent method that generates up-to-date spatial fuel type information across Australia.

Methods

Data from various space-borne broad-band optical, LiDAR and radar sensors were combined with land use data to generate structural descriptions of vegetation that were then converted into fuel types.

Key results

An Australian fuel type spatial layer was generated using the Bushfire Fuel Classification fuel typology. Evaluation against field measurements revealed accuracies of 89 and 71% for native forest and non-forest fuel types, respectively. This product provides a higher level of spatial and structural detail than previously obtained by other national-level fuel classification approaches in Australia.

Implications

The developed fuel type layer is made available and can be readily used in research applications. The data also have use in supporting jurisdictional-level fuel mapping for a range of fire management applications, such as fire behaviour prediction, fire danger forecasting and risk assessment.

Keywords: Bushfire fuels, Bushfire Fuel Classification, Eucalypt forest, Fuel arrangement, Fuel types, Remote sensing, Vegetation structure, Wildfire fuel map.

References

ABARES (2021) ‘Catchment Scale Land Use of Australia.’ (Australian Bureau of Agricultural and Resource Economics and Sciences) Available at https://www.agriculture.gov.au/abares/aclump [verified 15 October 2024]

ABARES (2022) ‘Australian Bureau of Agricultural and Resource Economics and Sciences, Australia’s Plantations.’ CC BY 4.0. (Australian Bureau of Agricultural and Resource Economics and Sciences: Canberra) Available at https://www.agriculture.gov.au/abares/forestsaustralia/forest-data-maps-and-tools/spatial-data/australias-plantations [verified 15 October 2024]

Abdollahi A, Yebra M (2023) Forest fuel type classification: review of remote sensing techniques, constraints and future trends. Journal of Environmental Management 342, 118315.
| Crossref | Google Scholar | PubMed |

ABS (2023) ‘Australian Bureau of Statistics. Average Floor Area of New Residential Dwellings.’ (ABS: Canberra, ACT) Available at https://www.abs.gov.au/articles/average-floor-area-new-residential-dwellings [verified 15-October-2024]

AHDAP (2022) ‘Australian Housing Data Analytics Platform.’ (A collaborative platform supported by the City Futures Research Centre, UNSW Sydney and the NCRIS-enabled Australian Research Data Commons (ARDC): CC BY 4.0) Available at https://housing-data-exchange.ahdap.org/dataset/building-footprints [verified 15 October 2024]

Albini FA (1976) Estimating wildfire behaviour and effects. General Technical Report INT-GTR-30. (USDA Forest Service, Intermountain Forestry and Range Experimental Station: Ogden, UT)

Alipour M, La Puma I, Picotte J, Shamsaei K, Rowell E, Watts A, Kosovic B, Ebrahimian H, Taciroglu E (2023) A multimodal data fusion and deep learning framework for large-scale wildfire surface fuel mapping. Fire 6(2), 36.
| Crossref | Google Scholar |

Allan GE, Southgate RI (2002) Fire regimes in the spinifex landscapes. In ‘Flammable Australia: The Fire Regimes and Biodiversity of a Continent’. (Eds RA Bradstock, JE Williams, AM Gill) pp. 145–176. (Cambridge University Press: Cambridge, UK)

Anderson WR, Cruz MG, Fernandes PM, McCaw L, Vega JA, Bradstock RA, Fogarty L, Gould J, McCarthy G, Marsden-Smedley JB, Matthews S, Mattingley G, Pearce HG, van Wilgen BW (2015) A generic, empirical-based model for predicting rate of fire spread in shrublands. International Journal of Wildland Fire 24(4), 443-460.
| Crossref | Google Scholar |

Andrews PL, Bevins CD, Seli RC (2008) BehavePlus fire modeling system, version 4.0: User’s Guide. Report number RMRS-GTR-106WWW. (USDA Forest Service, Rocky Mountain Research Station: Ogden, UT, USA) Available at http://www.fs.fed.us/rm/pubs/rmrs_gtr106.pdf

Aragoneses E, García M, Salis M, Ribeiro LM, Chuvieco E (2023) Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system. Earth System Science Data 15(3), 1287-1315.
| Crossref | Google Scholar |

Bowman DMJS, Murphy BP, Boer MM, Bradstock RA, Cary GJ, Cochrane MA, Fensham RJ, Krawchuk MA, Price OF, Williams RJ (2013) Forest fire management, climate change, and the risk of catastrophic carbon losses. Frontiers in Ecology and the Environment 11(2), 66-67.
| Crossref | Google Scholar |

Bradstock RA (2010) A biogeographic model of fire regimes in Australia: current and future implications. Global Ecology and Biogeography 19(2), 145-158.
| Crossref | Google Scholar |

Burns P, Hakkenberg CR, Goetz SJ (2024) Multi-resolution gridded maps of vegetation structure from GEDI. Scientific Data 11(1), 881.
| Crossref | Google Scholar | PubMed |

Burrows N, Gill M, Sharples J (2018) Development and validation of a model for predicting fire behaviour in spinifex grasslands of arid Australia. International Journal of Wildland Fire 27(4), 271-279.
| Crossref | Google Scholar |

Burrows N, Wills A, Densmore V (2023) Fuel weight and understorey hazard dynamics in mature karri (Eucalyptus diversicolor) forests in southwest Western Australia. Australian Forestry 86(2), 68-81.
| Crossref | Google Scholar |

Carnahan JA (1990) ‘Australia: Present Vegetation (map).’ (ANU Botany Department, ASaLI Group: Canberra, ACT, Australia)

Cawson JG, Duff TJ, Swan MH, Penman TD (2018) Wildfire in wet sclerophyll forests: the interplay between disturbances and fuel dynamics. Ecosphere 9(5), e02211.
| Crossref | Google Scholar |

Chandler C, Cheney P, Thomas P, Trabaud L, Williams D (1983) ‘Fire in Forestry, Volume I: Forest Fire Behaviour and Effects.’ (John Wiley and Sons: New York, NY, USA)

Cheney NP, Gould JS, Catchpole WR (1998) Prediction of fire spread in grasslands. International Journal of Wildland Fire 8(1), 1-13.
| Crossref | Google Scholar |

Cheney NP, Gould JS, McCaw WL, Anderson WR (2012) Predicting fire behaviour in dry eucalypt forest in southern Australia. Forest Ecology and Management 280, 120-131.
| Crossref | Google Scholar |

Cheney P, Sullivan A (2008) ‘Grassfires, Fuel, Weather and Fire Behaviour.’ 2nd edn. (CSIRO Publishing: Melbourne, Vic, Australia)

Chrysafis I, Damianidis C, Giannakopoulos V, Mitsopoulos I, Dokas IM, Mallinis G (2023) Vegetation fuel mapping at regional scale using Sentinel-1, Sentinel-2, and DEM derivatives—the case of the region of East Macedonia and Thrace, Greece. Remote Sensing 15(4), 1015.
| Crossref | Google Scholar |

Chuvieco E, Riaño D, Van Wagtendok J, Morsdof F (2003) Fuel loads and fuel type mapping. In ‘Wildland Fire Danger Estimation and Mapping. The Role of Remote Sensing Data’. (Ed. E Chuvieco) pp. 119–142. (World Scientific Publishing: Singapore)

Chuvieco E, Aguado I, Salas J, García M, Yebra M, Oliva P (2020) Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports 6(2), 81-96.
| Crossref | Google Scholar |

Clark DB, Mercado LM, Sitch S, Jones CD, Gedney N, Best MJ, Pryor M, Rooney GG, Essery RLH, Blyth E, Boucher O, Harding RJ, Huntingford C, Cox PM (2011) The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics. Geoscientific Model Development 4(3), 701-722.
| Crossref | Google Scholar |

Clifford V, Pearce H, Anderson S (2010) ‘Guide to New Zealand Fuels.’ (Scion, Rural Fires Research Group, NZ Forest Research Institute Limited)

Cohen JD, Deeming JE (1985) ‘The National Fire Danger Rating System: Basic Equations.’ (Pacific Southwest Forest and Range Experiment Station, USDA Forest Serervice: Berkeley, CA, USA)

Cruz MG, Alexander ME, Fernandes PAM (2008) Development of a model system to predict wildfire behaviour in pine plantations. Australian Forestry 71(2), 113-121.
| Crossref | Google Scholar |

Cruz MG, McCaw WL, Anderson WR, Gould JS (2013) Fire behaviour modelling in semi-arid mallee-heath shrublands of southern Australia. Environmental Modelling & Software 40, 21-34.
| Crossref | Google Scholar |

Cruz MG, Gould JS, Alexander MA, Sullivan AL, McCaw WL, Matthews S (2015a) A guide to Rate of Fire Spread Models for Australian Vegetation, 125. (CSIRO Land and Water Flagship, Canberra, ACT, and AFAC: Melbourne, Australia)

Cruz MG, Gould JS, Alexander ME, Sullivan AL, McCaw WL, Matthews S (2015b) Empirical-based models for predicting head-fire rate of spread in Australian fuel types. Australian Forestry 78, 118-158.
| Crossref | Google Scholar |

Cruz M, Gould J, Hollis J, McCaw W (2018) A hierarchical classification of wildland fire fuels for Australian vegetation types. Fire 1(1), 13.
| Crossref | Google Scholar |

Cruz MG, Cheney NP, Gould JS, McCaw WL, Kilinc M, Sullivan AL (2022) An empirical-based model for predicting the forward spread rate of wildfires in eucalypt forests. International Journal of Wildland Fire 31(1), 81-95.
| Crossref | Google Scholar |

Davey SM, Sarre A (2020) Editorial: the 2019/20 Black Summer bushfires. Australian Forestry 83(2), 47-51.
| Crossref | Google Scholar |

DCCEEW (2020) ‘Australia – present major vegetation groups – NVIS version 6.0 (Albers 100 m analysis product).’ (Department of Climate Change, Energy, the Environment and Water) Available at https://www.dcceew.gov.au/environment/land/native-vegetation/national-vegetation-information-system/data-products [verified 15 October 2024]

Deeming JE, Lancaster JW, Fosberg MA, Furman RW, Schroeder MJ (1974) National fire danger rating system. Report number RM-84, USDA Forest Service. (Rocky Mountain Forest and Range Experiment Station: Fort Collins, CO, USA)

Dimitrakopoulos AP (2002) Mediterranean fuel models and potential fire behaviour in Greece. International Journal of Wildland Fire 11(2), 127-130.
| Crossref | Google Scholar |

Domingo D, de la Riva J, Lamelas MT, García-Martín A, Ibarra P, Echeverría M, Hoffrén R (2020) Fuel type classification using airborne laser scanning and Sentinel 2 data in Mediterranean forest affected by wildfires. Remote Sensing 12(21), 3660.
| Crossref | Google Scholar |

Donohue R (2021) ‘Testing the accuracy of Gramina, a model for monitoring Australia’s pasture and grassland biomass.’ CSIRO internal report. (CSIRO: Canberra, ACT, Australia) 10.4225/08/5a3953b0a4d5c

Donohue RJ, Renzullo LJ (2025) An assessment of the accuracy of satellite-derived woody and grass foliage cover estimates for Australia. Australian Journal of Botany 73(2), BT24060.
| Crossref | Google Scholar |

Donohue RJ, Mokany K, McVicar TR, O’Grady AP (2022) Identifying management-driven dynamics in vegetation cover: applying the Compere framework to Cooper Creek, Australia. Ecosphere 13(3), e4006.
| Crossref | Google Scholar |

Erdody TL, Moskal LM (2010) Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment 114(4), 725-737.
| Crossref | Google Scholar |

Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The shuttle radar topography mission. Reviews of Geophysics 45(2), RG2004.
| Crossref | Google Scholar |

Fernandes P, Gonçalves H, Loureiro C, Fernandes M, Costa T, Cruz M, Botelho H (2009) Modelos de combustível florestal para Portugal. In ‘6th Congr. Florest. Nac’. (Soc. Port. Ciências Florestais: Lisboa, Portugal) [In Portuguese]

Finney MA (2004) FARSITE: Fire Area Simulator–model development and evaluation. Report number RMRS-RP-4 Revised. (U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Ogden, UT)

Finney MA (2006) An overview of FlamMap fire modeling capabilities. In ‘Proceedings of Fuels Management-How to Measure Success: Conference Proceedings’, 28–30 March 2006, Portland, OR. Proceedings RMRS-P-41. Vol. 41. (Comps PL Andrews, BW Butler) pp. 213–220. (US Department of Agriculture, Forest Service, Rocky Mountain Research Station: Fort Collins, CO)

Forestry Canada Fire Danger Group (1992) Development and structure of the Canadian Forest Fire Behavior Prediction System. Report number Information Report ST-X-3. (Forestry Canada, Science and Sustainable Development Directorate, SaSD Forestry Canada: Ottawa, Canada)

Furlaud JM, Prior LD, Williamson GJ, Bowman DMJS (2021a) Bioclimatic drivers of fire severity across the Australian geographical range of giant Eucalyptus forests. Journal of Ecology 109(6), 2514-2536.
| Crossref | Google Scholar |

Furlaud JM, Prior LD, Williamson GJ, Bowman DMJS (2021b) Fire risk and severity decline with stand development in Tasmanian giant Eucalyptus forest. Forest Ecology and Management 502, 119724.
| Crossref | Google Scholar |

García M, Riaño D, Chuvieco E, Salas J, Danson F M (2011) Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules. Remote Sensing of Environment 115(6), 1369-1379.
| Crossref | Google Scholar |

Gibson R, Danaher T, Hehir W, Collins L (2020) A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest. Remote Sensing of Environment 240, 111702.
| Crossref | Google Scholar |

Giglio L, Justice C, Boschetti L, Roy D (2021) ‘MODIS/terra+aqua burned area monthly L3 global 500m SIN grid V061.’ (NASA EOSDIS Land Processes DAAC: Washington, DC, USA). Available at https://lpdaac.usgs.gov/products/mcd64a1v006/

Gill AM, Stephens SL, Cary GJ (2013) The worldwide ‘wildfire’ problem. Ecological Applications 23(2), 438-454.
| Crossref | Google Scholar | PubMed |

Guerschman JP, Hill MJ (2018) Calibration and validation of the Australian fractional cover product for MODIS collection 6. Remote Sensing Letters 9(7), 696-705.
| Crossref | Google Scholar |

Haverd V, Smith B, Nieradzik L, Briggs PR, Woodgate W, Trudinger CM, Canadell JG, Cuntz M (2018) A new version of the CABLE land surface model (subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geoscientific Model Development 11(7), 2995-3026.
| Crossref | Google Scholar |

Hengl T, Walsh MG, Sanderman J, Wheeler I, Harrison SP, Prentice IC (2018) Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential. PeerJ 6, e5457.
| Crossref | Google Scholar | PubMed |

Hollis JJ, Gould JS, Cruz MG, McCaw WL (2015) Framework for an Australian fuel classification to support bushfire management. Australian Forestry 78(1), 1-17.
| Crossref | Google Scholar |

Hollis JJ, Matthews S, Anderson WR, Cruz MG, Fox-Hughes P, Grootemaat S, Kenny BJ, Sauvage S (2024a) A framework for defining fire danger to support fire management operations in Australia. International Journal of Wildland Fire 33(3), WF23141.
| Crossref | Google Scholar |

Hollis JJ, Matthews S, Fox-Hughes P, Grootemaat S, Heemstra S, Kenny BJ, Sauvage S (2024b) Introduction to the Australian Fire Danger Rating System. International Journal of Wildland Fire 33(3), WF23140.
| Crossref | Google Scholar |

ICONA (1990) ‘Clave fotográfica para la identificación de modelos de combustible, Defensa contra incendios forestales.’ (Ministerio de Agricultura, Pesca y Alimentación, Instituto para la Conservación de la Naturaleza: Madrid, Spain) [In Spanish]

Jolly WM, Freeborn PH, Bradshaw LS, Wallace J, Brittain S (2024) Modernizing the US National Fire Danger Rating System (version 4): simplified fuel models and improved live and dead fuel moisture calculations. Environmental Modelling & Software 181, 106181.
| Crossref | Google Scholar |

Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal 58(4), 233-248.
| Crossref | Google Scholar |

Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Knyazikhin Y, Running SW, Nemani RR, Zhengming W, Huete AR, Leeuwen Wv, Wolfe RE, Giglio L, Muller J, Lewis P, Barnsley MJ (1998) The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing 36(4), 1228-1249.
| Crossref | Google Scholar |

Keane RE (2015) ‘Wildland Fuel Fundamentals and Applications.’ (Springer: New York, NY, USA)

Keane RE, Burgan R, van Wagtendonk J (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire 10(4), 301-319.
| Crossref | Google Scholar |

Keane RE, Herynk JM, Toney C, Urbanski SP, Lutes DC, Ottmar RD (2013) Evaluating the performance and mapping of three fuel classification systems using forest inventory and analysis surface fuel measurements. Forest Ecology and Management 305, 248-263.
| Crossref | Google Scholar |

Kenny BJ, Matthews S, Sauvage S, Grootemaat S, Hollis JJ, Fox-Hughes P (2024) Australian Fire Danger Rating System: implementing fire behaviour calculations to forecast fire danger in a research prototype. International Journal of Wildland Fire 33(4), WF23142.
| Crossref | Google Scholar |

Kessell SR (1976) Wildland inventories and fire modeling by gradient analysis in Glacier National Park. In ‘Proceedings of the Tall Timbers Fire EcologyConference and Fire and Land Management Symposium 14’. pp. 115–162. (Tall Timbers Research Station: Missoula, MT)

Krasnow K, Schoennagel T, Veblen TT (2009) Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder County, Colorado, USA. Forest Ecology and Management 257(7), 1603-1612.
| Crossref | Google Scholar |

Lang N, Jetz W, Schindler K, Wegner JD (2023) A high-resolution canopy height model of the Earth. Nature Ecology & Evolution 7(11), 1778-1789.
| Crossref | Google Scholar | PubMed |

López-De-Castro M, Herráez D, Asensio M, Pagnini G (2022) A high-resolution fuel type mapping procedure based on satellite imagery and neural networks: updating fuel maps for wildfire simulators. Remote Sensing Applications: Society and Environment 27, 100810.
| Crossref | Google Scholar |

Loudermilk EL, O’Brien JJ, Goodrick SL, Linn RR, Skowronski NS, Hiers JK (2022) Vegetation’s influence on fire behavior goes beyond just being fuel. Fire Ecology 18(1), 9.
| Crossref | Google Scholar |

Malone B, Searle R (2022) Soil and Landscape Grid National Soil Attribute Maps - Sand (3″ resolution) - Release 2. 2.0. Terrestrial Ecosystem Research Network [Dataset]. 10.25919/rjmy-pa10.

Marino E, Ranz P, Tomé JL, Noriega MÁ, Esteban J, Madrigal J (2016) Generation of high-resolution fuel model maps from discrete airborne laser scanner and Landsat-8 OLI: a low-cost and highly updated methodology for large areas. Remote Sensing of Environment 187, 267-280.
| Crossref | Google Scholar |

Marsden-Smedley JB, Catchpole WR (1995) Fire modelling in Tasmanian buttongrass moorlands I. Fuel characteristics. International Journal of Wildland Fire 5(4), 203-214.
| Crossref | Google Scholar |

Marselis SM, Yebra M, Jovanovic T, van Dijk AIJM (2016) Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification. Environmental Modelling & Software 82, 142-151.
| Crossref | Google Scholar |

Matthews S, Fox-Hughes P, Grootemaat S, Hollis JJ, Kenny BJ, Sauvage S (2019) ‘Australian Fire Danger Rating System: research prototype.’ (NSW Rural Fire Service: Lidcombe, NSW)

McCaw WL (2011) Characteristics of jarrah (Eucalyptus marginata) forest at FORESTCHECK monitoring sites in south-west Western Australia: stand structure, litter, woody debris, soil and foliar nutrients. Australian Forestry 74(4), 254-265.
| Crossref | Google Scholar |

Mihajlovski B, Fernandes PM, Pereira JMC, Guerra-Hernández J (2023) Comparing forest understory fuel classification in Portugal using discrete airborne laser scanning data and satellite multi-source remote sensing data. Fire 6(9), 327.
| Crossref | Google Scholar |

Monedero S, Ramirez J, Cardil A (2019) Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: a mobile app for wildland fire prediction. Ecological Modelling 392, 103-107.
| Crossref | Google Scholar |

Moritz MA, Batllori E, Bradstock RA, Gill AM, Handmer J, Hessburg PF, Leonard J, McCaffrey S, Odion DC, Schoennagel T, Syphard AD (2014) Learning to coexist with wildfire. Nature 515(7525), 58-66.
| Crossref | Google Scholar | PubMed |

Nikparvar B, Thill J-C (2021) Machine learning of spatial data. ISPRS International Journal of Geo-Information 10(9), 600.
| Crossref | Google Scholar |

Phelps N, Beverly JL (2022) Classification of forest fuels in selected fire-prone ecosystems of Alberta, Canada—implications for crown fire behaviour prediction and fuel management. Annals of Forest Science 79(1), 40.
| Crossref | Google Scholar |

Plucinski MP, Sullivan AL, Rucinski CJ, Prakash M (2017) Improving the reliability and utility of operational bushfire behaviour predictions in Australian vegetation. Environmental Modelling & Software 91, 1-12.
| Crossref | Google Scholar |

Potapov P, Li X, Hernandez-Serna A, Tyukavina A, Hansen MC, Kommareddy A, Pickens A, Turubanova S, Tang H, Silva CE, Armston J, Dubayah R, Blair JB, Hofton M (2021) Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment 253, 112165.
| Crossref | Google Scholar |

Pyne SJ (1984) ‘Introduction to Wildland Fire. Fire Management in the United States.’ (John Wiley & Sons: New York, NY, USA)

Raymond OL, Retter AJ (2014) Surface Geology of Australia 1:1,000,000 Scale, 2010 Edition. (Atlas of Living Australia) Available at https://spatial.ala.org.au/layers, https://researchdata.edu.au/surface-geology-australia-2010-edition [verified 22 October 2024]

Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. International Journal of Wildland Fire 18(3), 235-249.
| Crossref | Google Scholar |

Rollins MG, Keane RE, Parsons RA (2004) Mapping fuels and fire regimes using remote sensing, ecosystem simulation, and gradient modeling. Ecological Applications 14(1), 75-95.
| Crossref | Google Scholar |

Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Research Paper INT-115. (USDA Forest Service: Ogden, UT, USA)

Scarth P, Armston J, Lucas R, Bunting P (2019) A structural classification of Australian vegetation using ICESat/GLAS, ALOS PALSAR, and Landsat sensor data. Remote Sensing 11(2), 147.
| Crossref | Google Scholar |

Schlund M, Wenzel A, Camarretta N, Stiegler C, Erasmi S (2023) Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM-X supported by GEDI data. Methods in Ecology and Evolution 14(7), 1639-1656.
| Crossref | Google Scholar |

Scott JH, Burgan RE (2005) Standard fire behavior fuel models: A comprehensive set for use with Rothermel’s surface fire spread model. Report number RMRS-GTR-153 (USDA Forest Service, Rocky Mountain Forest and Range Experiment Station: Fort Collins, CO, USA) Available at https://www.fs.fed.us/rm/pubs/rmrs_gtr153.pdf

Sharples JJ, Cary GJ, Fox-Hughes P, Mooney S, Evans JP, Fletcher M-S, Fromm M, Grierson PF, McRae R, Baker P (2016) Natural hazards in Australia: extreme bushfire. Climatic Change 139, 85-99.
| Crossref | Google Scholar |

Sismanis M, Stefanidou A, Stavrakoudis D, Gitas IZ (2023) Wildland fuel type mapping in Attica using Sentinel-2 time-series. In ‘Proceedings of 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech)’, 20–23 June 2023. pp. 1–5. (IEEE) 10.23919/SpliTech58164.2023.10193645

Slijepcevic A, Anderson WR, Matthews S, Anderson DH (2018) An analysis of the effect of aspect and vegetation type on fine fuel moisture content in eucalypt forest. International Journal of Wildland Fire 27(3), 190-202.
| Crossref | Google Scholar |

Sparrow B, Tokmakoff A, Leitch E, Guerin G, O’Neill S, Macdonald C, Lowe A, Flitton R, Saleeba T, Coish C (2021) ‘TERN Ecosystem Surveillance Monitoring Program: Photopoint Panoramic Images’, Version 1.0 edn. (Terrestrial Ecosystem Research Network) Available at https://portal.tern.org.au/metadata/TERN/fe390ffb-8f30-44e2-872d-93fa96ec8f79

Specht R (1972) Water use by perennial evergreen plant communities in Australia and Papua New Guinea. Australian Journal of Botany 20(3), 273-299.
| Crossref | Google Scholar |

Specht RL (1970) Vegetation. In ‘The Australian Environment’, 4th edn. (Ed. GW Leeper) pp. 44–67. (CSIRO - Melbourne University Press: Melbourne, Vic, Australia)

Specht RL, Specht A (1999) ‘Australian plant communities – Dynamics of structure, growth and diversity.’ (Oxford University Press: Oxford, UK)

Standards Australia (2018) ‘Standards Australia: Construction of Buildings in Bushfire-Prone Areas.’ 4th edn. (Standards Australia: Australia)

Stefanidou A, Gitas IZ, Katagis T (2022) A national fuel type mapping method improvement using sentinel-2 satellite data. Geocarto International 37(4), 1022-1042.
| Crossref | Google Scholar |

Sullivan A, McCaw L, Cruz M, Matthews S, Ellis PF (2012) Fuel, fire weather and fire behaviour in Australian Ecosystems. In ‘Flammable Australia: Fire Regimes, Biodiversity and Ecosystems in a Changing World’. (Eds RA Bradstock, AM Gill, RJ Williams) pp. 51–78. (CSIRO Publishing: Melbourne, Vic, Australia)

Syphard AD, Keeley JE, Massada AB, Brennan TJ, Radeloff VC (2012) Housing arrangement and location determine the likelihood of housing loss due to wildfire. PLoS One 7(3), e33954.
| Crossref | Google Scholar | PubMed |

Ticehurst C, Newnham G (2024) Producing annual Australia-wide vegetation height images from GEDI and Landsat data. International Journal of Remote Sensing 45(18), 6445-6469.
| Crossref | Google Scholar |

Tolhurst K (2005) Conversion of ecological vegetation classes (EVCs) to fuel types and calculation of equivalent fine fuel loads with time since fire. Victoria 1-7.
| Google Scholar |

Tolhurst K, Duff T, Chong D (2013) From ‘Wildland–Urban Interface’ to ‘Wildfire Interface Zone’ using dynamic fire modelling. In ‘Proceedings of MODSIM2013, 20th International Congress on Modelling and Simulation'. (Eds J Piantadosi, RS Anderssen, J Boland) pp. 1–6. (Modelling and Simulation Society of Australia and New Zealand: Adelaide, SA)

Wang Z, Cai H, Yang X (2024) A new method for mapping vegetation structure parameters in forested areas using GEDI data. Ecological Indicators 164, 112157.
| Crossref | Google Scholar |

Watson P (2011) ‘Fuel load dynamics in NSW vegetation – part 1: forests and grassy woodlands. Centre for Environmental Risk Management of Bushfires.’ (University of Wollongong: Wollongong, NSW, Australia)

Wilson BA, Charlotte FYO, Heathcott M, Milne D, McCaffrey TM, Ghitter G, Franklin SE (1994) Landsat MSS classification of fire fuel types in Wood Buffalo National Park, Northern Canada. Global Ecology and Biogeography Letters 4(2), 33-39.
| Crossref | Google Scholar |