Skip to main content
SpringerLink
Log in

Fuel Mapping

  • Chapter
  • First Online:
Wildland Fuel Fundamentals and Applications

  • 851 Accesses

Abstract

Fuel maps are critical tools for spatially explicit fire simulation and analysis. Many diverse techniques have been used to create spatial fuel data products including field assessment, association, remote sensing, and biophysical modeling. This chapter presents the great need for fuel maps in fire management then details how most fuel maps are created using one approach or an integration of multiple approaches.

Knowing where things are, and why, is essential to rational decision making

Jack Dangermond, ESRI

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abell CA (1937) Rate of spread and resistance to control data for Region 7 fuel types and their application to determine strength and speed of attack needed. Preliminary Report. U.S. Department of Agriculture, Forest Service, Asheville, pp 7

    Google Scholar 

  • Agee JK, Skinner CN (2005) Basic principles of forest fuel reduction treatments. For Ecol Manage 211:83–96

    Article  Google Scholar 

  • Andersen H-E, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LIDAR data. Remote Sens Environ 94(4):441–449 http://dx.doi.org/10.1016/j.rse.2004.10.013

    Article  Google Scholar 

  • Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service Intermountain Research Station, General Technical Report INT-122 Ogden, Utah, USA, pp 22

    Google Scholar 

  • Anderson M, Bourgeron P, Bryer MT, Crawford R, Engelking L, Faber-Langendoen D, Gallyoun M, Goodin K, Grossman DH, Landaal S, Metzler K, Patterson KD, Pyne M, Reid M, Sneddon L, Weakley AS (1998) International classification of ecological communities: terrestrial vegetation of the United States. The National Vegetation Classification System: list of types, vol. 2. The Nature Conservancy, Arlington, Virginia, pp 445

    Google Scholar 

  • Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role of remote sensing. For Ecol Manage 256:1239–1252

    Article  Google Scholar 

  • Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens Environ 64(3):234–253

    Article  Google Scholar 

  • Ball GL, Guertin DP (1992) Advances in fire spread simulation. In: Proceedings on the third forest service remote sensing applications conference—Protecting natural resources with remote sensing. Tucson, Arizona USA, April 9–13 1992. American Society of Photogrammery and Remote Sensing, 5410 Grosvenor lane, Bethesda, Maryland USA, pp 241–249

    Google Scholar 

  • Banks WG, Frayer HC (1966) Rate of forest fire spread and resistance to control in the fuel types of the eastern region. U.S. Department of Agriculture, Forest Service, Washington, DC. Fire Control Notes 27(2):10–13.

    Google Scholar 

  • Barrett SW, Arno SF (1991) Classifying fire regimes and defining their topographic controls in the Selway-Bitterroot Wilderness. In: 11th Conference on Fire and Forest Meteorology, Missoula, MT, 1991, pp 233–245

    Google Scholar 

  • Belfort W (1988) Controlled-scale aerial sampling photography: development and implications for multiresource inventory. J For 86(11):21–28

    Google Scholar 

  • Bergen KM, Dobson MC (1999) Integration of remotely sensed radar imagery in modeling and mapping of forest biomass and net primary production. Ecol Model 122:257–274

    Article  Google Scholar 

  • Brandis K, Jacobson C (2003) Estimation of vegetative fuel loads using Landsat TM imagery in New South Wales, Australia. Internat J Wildland Fire 12:185–194

    Article  Google Scholar 

  • Brown AA, Davis K (1973) Forest fire control and use second edition. McGraw-Hill, New York

    Google Scholar 

  • Burgan RE, Hardy CC (1994) Ground truthing a national AVHRR based vegetation fuels map. Proceedings of the 12th conference on fire and forest meteorology. Society of American Foresters, Bethesda, Maryland, pp 428–436

    Google Scholar 

  • Burgan RE, Klaver RW, Klaver JM (1998) Fuel models and fire potential from satellite and surface observations. Int J Wildland Fire 8(3):159–170

    Article  Google Scholar 

  • Campbell J, Weinstein D, Finney M (1995) Forest fire behavior modeling integrating GIS and BEHAVE. USDA Forest Service, Washington

    Google Scholar 

  • Chladil MA, Nunez M (1995) Assessing grassland moisture and biomass in Tasmania—The application of remote sensing and empirical models for a cloudy environment. Int J Wildland Fire 5(3):165–171

    Article  Google Scholar 

  • Chuvieco E, Salas J (1996) Mapping of spatial distribution of forest fire danger using GIS. Int J Geogr Inf Syst 10(3):333–345

    Article  Google Scholar 

  • De Vasconcelos MJP, Paul JCU, Silva S, Pereira JMC, Caetano MS, Catry FX, Oliveira TM (1998) Regional fuel mapping using a knowledge based system approach. 3rd International conference on forest fire research 14th Conference on fire and forest meteorology, November 1998, pp 2111–2123

    Google Scholar 

  • Dixon R, Shipley W, Briggs A (1985) Landsat—a tool for mapping fuel types in the boreal forest of Manitoba: a pilot study. In: Proceedings of the Pecora X symposium, 1985. American Society for Photogrammetry and Remote Sensing, Falls Church, Virginia, pp 392–393

    Google Scholar 

  • Elvidge CD (1988) Thermal infrared reflectance of dry plant materials: 2.5–20.0 micrometers. Remote Sens Environ 26:265–285

    Article  Google Scholar 

  • Erdody TL, Moskal LM (2010) Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sens Environ 114(4):725–737. http://dx.doi.org/10.1016/j.rse.2009.11.002

    Article  Google Scholar 

  • Falkowski MJ, Gessler PE, Morgan P, Hudak AT, Smith AMS (2005) Characterizing and mapping forest fire fues using ASTER imagery and remote sensing. For Ecol Manage 217:129–146

    Article  Google Scholar 

  • Finney MA (1998) FARSITE: fire area simulator—model development and evaluation. United States Department of Agriculture, Forest Service Rocky Mountain Research Station, Research Paper RMRS-RP-4. Fort Collins, CO USA, pp 47

    Google Scholar 

  • Finney MA (2005) The challenge of quantitative risk analysis for wildland fire. For Ecol Manage 211:97–108

    Article  Google Scholar 

  • Finney MA (2006) An overview of FlamMap fire modeling capabilities. In: Andrews PL, Butler BW (eds) Fuels management—how to measure success, Portland, OR, 28–30 March 2006. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Proceedings RMRS-P-41 Fort Collins, CO USA, pp 213–220

    Google Scholar 

  • Friedl MA, Michaelsen J, Davis FW, Walker H, Schimel DS (1994) Estimating grassland biomass and leaf area index using ground and satellite data. Int J Remote Sens 15(7):1401–1420

    Article  Google Scholar 

  • Gosz JR (1992) Gradient analysis of ecological change in time and space: implications for forest management. Ecol Appl 2(3):248–261

    Article  Google Scholar 

  • Grossman DH, Faber-Langendoen D, Weakley AS, Anderson M, Bourgeron P, Crawford R, Goodin K, Landaal S, Metzler K, Patterson K, Pyne M, Reid M, Sneddon L (1998) International classification of ecological communities: terrestrial vegetation of the United States Volume I. The National Vegetation Classification System: development, status, and applications., vol I. The Nature Conservancy, Arlington VA., USA

    Google Scholar 

  • Habeck JR (1976) Forest, fuels and fire in the Selway-Bitterroot Wilderness, Idaho. 14th Tall timbers fire ecology conference 14:305–353

    Google Scholar 

  • Hawkes B, Niemann O, Goodenough D, Lawson B, Thomson A, Sahle W, Fuglem P, Beck J, Bell B, Symingto P (1995) Forest fire fuel type mapping using GIS and remote sensing in British Columbia. In: Heit M, Parker HD, Shortreid A (eds) Proceedings of the symposium GIS applications in natural resources 2 – the 9th symposium on geographic information systems, Vancouver, British Columbia, Canada, pp 290–299

    Google Scholar 

  • Hessburg PF, Reynolds K, Keane RE, James K, Salter RB (2010) Evaluating wildland fire danger and prioritizing vegetation and fuels treatments. In: Pye JM, Rauscher HM, Sands Y, Lee DC, Beatty JS (eds) Advances in threat assessment and their application to forest and rangeland management, Portland, OR, 2010. Gen. Tech. Rep. PNW-GTR-802. U.S. Department of Agriculture, Forest Service, Pacific Northwest and Southern Research Stations, pp 329–353

    Google Scholar 

  • Heyerdahl EK, Brubaker LB, Agee JK (2001) Spatial controls of historical fire regimes: a multiscale example from the Interior West, USA. Ecology 82(3):660–678

    Article  Google Scholar 

  • Hornby LG (1935) Fuel type mapping in region one. J For 33(1):67–72

    Google Scholar 

  • Hornby LG (1936) Fuel type mapping, its relation to forest fire control plannig. Northwest Sci 4:3–7

    Google Scholar 

  • Huang S, Crabtree RL, Potter C, Gross P (2009) Estimating the quantity and quality of coarse woody debris in Yellowstone post-fire forest ecosystem from fusion of SAR and optical data. Remote Sens Environ 113(9):1926–1938. doi:10.1016/j.rse.2009.05.001

    Article  Google Scholar 

  • Jain A, Ravan SA, Singh RK, Das KK, Roy PS (1996) Forest fire risk modelling using remote sensing and geographic information systems. Curr Sci India 70 (10):928–933

    Google Scholar 

  • Jia GJ, Burke IC, Kaufmann MR, Goetz AFH, Kindel BC, Pu Y (2006) Estimates of forest canopy fuel attributes using hyperspectral data. For Ecol Manage 229:27–38

    Article  Google Scholar 

  • Karau EC, Keane RE (2010) Burn severity mapping using simulation modelling and satellite imagery. Int J Wildland Fire 19(6):710–724 http://dx.doi.org/10.1071/WF09018

    Article  Google Scholar 

  • Kasischke ES, French NFH, Bourgeau-Chavez LL, Ustin SL, Christensen NL (1998) Estimating the release of carbon from forest fires in Alaska using satellite remote sensing data. http://cstars.ucdavis.edu/papers/sustin/carbon/paper.html

  • Keane RE (2013) Describing wildland surface fuel loading for fire management: a review of approaches, methods and systems. Int J Wildland Fire 22(1):51–62 http://dx.doi.org/10.1071/WF11139

    Article  Google Scholar 

  • Keane RE, Reeves MC (2011) Use of expert knowledge to develop fuel maps for wildland fire management. In: Perera A, Drew A (eds) Expert knowledge and its application in landscape ecology. Island Press, New York, pp 211–228

    Google Scholar 

  • Keane RE, Kevin CR, Running SW (1996) Simulating effects of fire on northern Rocky Mountain landscapes with the ecological process model Fire-BGC. Tree Physiol 16:319–331

    Article  PubMed  Google Scholar 

  • Keane RE, McNicoll CH, Schmidt KM, Garner JL (1997) Spatially explicit ecological inventories for ecosystem management planning using gradient modeling and remote sensing. In: Proceedings of the sixth forest service remote sensing applications conference—remote sensing; people in partnership with technology. American Society of Photogrammetry and Remote Sensing. Bethesda, MD., USA, pp 135–146

    Google Scholar 

  • Keane RE, Garner JL, Schmidt KM, Long DG, Menakis JP, Finney MA (1998a) Development of input spatial data layers for the FARSITE fire growth model for the Selway-Bitterroot Wilderness complex, USA. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-3 Fort Collins, CO USA, pp 66

    Google Scholar 

  • Keane RE, Long DG, Schmidt KM, Mincemoyer SA, Garner JL (1998b) Mapping fuels for spatial fire simulations using remote sensing and biophysical modeling. Natural resource management using remote sensing and GIS: proceedings of the seventh forest service remote sensing applications conference, Nassau Bay, Texas, April 6–10, 1998. American Society for Photogrammetry and Remote Sensing c1998, Bethesda

    Google Scholar 

  • Keane RE, Mincemoyer SA, Schmidt KM, Menakis JP, Long DG, Garner JL (2000) Mapping vegetation and fuels for fire management on the Gila National Forest Complex. USDA Forest Service, Rocky Mountain Research Station General Technical Report RMRS-GTR-46-CD Fort Collins, CO USA, pp 67

    Google Scholar 

  • Keane RE, Burgan RE, Wagtendonk JV (2001) Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling. Int J Wildland Fire 10:301–319

    Article  Google Scholar 

  • Keane RE, Cecilia McNicoll, Rollins MG (2002) Integrating ecosystem sampling, gradient modeling, remote sensing, and ecosystem simulation to create spatially explicit landscape inventories. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-92 Fort Collins, CO USA, pp 61

    Google Scholar 

  • Keane RE, Frescino TL, Reeves MC, Long J (2006) Mapping wildland fuels across large regions for the LANDFIRE prototype project. In: Rollins MG, Frame C (eds) The LANDFIRE prototype project: nationally consistent and locally relevant geospatial data for wildland fire management, 2006. USDA Forest Service Rocky Mountain Research Station, General Technical Report RMRS-GTR-175 Ogden, UT USA, pp 367–396

    Google Scholar 

  • Keane RE, Holsinger L, Pratt S (2006b) Simulating historical landscape dynamics using the landscape fire succession model LANDSUM version 4.0. USDA Forest Service Rocky Mountain Research Station, General Technical Report RMRS-GTR-171CD Fort Collins, CO USA, pp 73

    Google Scholar 

  • Keane RE, Rollins M, Zhu ZL (2007) Using simulated historical time series to prioritize fuel treatments on landscapes across the United States: the LANDFIRE prototype project. Ecol Model 204(3–4):485–502. doi:10.1016/j.ecolmodel.2007.2.005

    Article  Google Scholar 

  • Keane RE, Loehman RA, Holsinger LM (2011) The FireBGCv2 landscape fire and succession model: a research simulation platform for exploring fire and vegetation dynamics. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-255 Fort Collins, CO USA, pp 137

    Google Scholar 

  • Keane R, Gray K, Bacciu V, Leirfallom S (2012a) Spatial scaling of wildland fuels for six forest and rangeland ecosystems of the northern Rocky Mountains, USA. Landsc Ecol 27(8):1213–1234. doi:10.1007/s10980-012-9773–9

    Google Scholar 

  • Keane RE, Gray K, Bacciu V (2012b) Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems. USDA Forest Service Rocky Mountain Research Station, Research Paper RMRS-RP-98 Fort Collins, Colorado USA, pp 58

    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. For Ecol Manage 305:248–263. http://dx.doi.org/10.1016/j.foreco.2013.06.001

  • Keramitsoglou I, Kontoes C, Sykioti O, Sifakis N, Xofis P (2008) Reliable, accurate, and timely forest mapping for wildfire management using ASTER and Hyperion satellite imagery. For Ecol Manage 255:3556–3562

    Article  Google Scholar 

  • Kessell SR (1976) Gradient modeling: a new approach to fire modeling and wilderness resource management. Environ Manage 1(1):39–48

    Article  Google Scholar 

  • Kourtz PH (1977) An application of Landsat digital technology to forest fire fuel mapping. In: Dube DE (ed) Fire ecology in resource mangement: a workshop. Northern Forest Research Centre, Information Report NOR-X-210 Edmonton, Alberta, pp 79–81

    Google Scholar 

  • Krasnow K (2007) Forest fuel mapping and strategic wildfire mitigation in the Montane zone of Boulder County, Colorado. Graduate Thesis, University of Colorado, Boulder, CO USA, pp 55

    Google Scholar 

  • Krivtsov V, Vigy O, Legg C, Curt T, Rigolot E, Lecomte I, Jappiot M, Lampin-Maillet C, Fernandes P, Pezzatti GB (2009) Fuel modelling in terrestrial ecosystems: an overview in the context of the development of an object-orientated database for wild fire analysis. Ecol Model 220(21):2915–2926

    Article  Google Scholar 

  • Lanorte A, Lasaponara R, Murgante B, Gervasi O, Iglesias A, Taniar D, Apduhan B (2011) On the use of satellite remote sensing data to characterize and map fuel types. In: Computational science and its applications—ICCSA 2011, vol 6783. Lecture Notes in Computer Science. Springer Berlin/Heidelberg, pp 344–353. doi:10.1007/978-3-642-21887-3_27

    Google Scholar 

  • Lasaponara R, Lanorte A (2007a) On the capability of satellite VHR Quickbird data for fuel type characterization in fragmented landscapes. Ecol Model 204:79–84

    Google Scholar 

  • Lasaponara R, Lanorte A (2007b) Remotely sensed characterization of forest fuel types by using satellite ASTER data. Int J Appl Earth Obs Geoinf 9(3):225–234. http://dx.doi.org/10.1016/j.jag.2006.08.001

  • Leenhouts B (1998) Assessment of biomass burning in the conterminous United States. Conserv Ecol 2(1):1–23

    Google Scholar 

  • Lenihan JM, Daly C, Bachelet D, Neilson RP (1998) Simulating broad scale fire severity in a dynamic global vegetation model. Northwest Sci 72:91–103

    Google Scholar 

  • Loveland TR, Merchant JM, Ohlen DO, Brown JF (1991) Development of a land-cover characteristics database for the conterminous U.S. Photogramm Eng Remote Sens 57(11):1453–1463

    Google Scholar 

  • Loveland TR, Ohlen DO, Brown JF, Reed BC, Merchant JW (1993) Prototype 1990 conterminous United States land cover characteristics data set EROS Data Center, US Geological Survey, CD-ROM 9307 Souix Falls, SD USA

    Google Scholar 

  • McKenzie D, Raymond CL, Kellogg L-KB, Norheim RA, Andreu A, Bayard AC, Kopper KE, Elman E (2007) Mapping fuels at multiple scales: landscape application of the fuel characteristic classification system. Can J For Res 37:2421–2437. doi:10.1139/x07-056

    Article  Google Scholar 

  • Menakis JP, Keane RE., Long DG (2000) Mapping ecological attributes using an integrated vegetation classification system approach. J Sustain For 11(1–2):245–265

    Article  Google Scholar 

  • Merrill EH, Bramble-Brodahl MK, Marrs RW, Boyce MS (1993) Estimation of green herbaceous phytomass from Landsat MSS data in Yellowstone National Park. J Rang Manage 46:151–157

    Article  Google Scholar 

  • Miller JD, Danzer SR, Watts JM, Stone S, Yool SR (2003) Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem. J Environ Manage 68(3):239–252

    Article  PubMed  Google Scholar 

  • Millington AC, Critchley RW, Douglas TD, Ryan P (1994) Estimating woody biomass in sub-Saharan Africa. The World Bank, Washington, DC

    Google Scholar 

  • Morris WG (1970) Photo inventory of fine logging slash. Photogramm Eng Remote Sens 36:1252–1256

    Google Scholar 

  • Muraro SJ (1970) Slash fuel inventories from 70 mm low-level photography. Canadian Forest Service, Information Report 1268, Ottawa, Ontario, Canada, pp 63

    Google Scholar 

  • Mutlu M, Popescu SC, Stripling C, Spencer T (2008) Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Remote Sens Environ 112(1):274–285. http://dx.doi.org/10.1016/j.rse.2007.5.005

    Article  Google Scholar 

  • New Jersey Department of Conservation and Development U.S. Department of Agriculture FS (1942) Forest fuel types of New Jersey. New Jersey Department of Conservation and Development, Trenton, NJ

    Google Scholar 

  • Ohmann JL, Spies TA (1998) Regional gradient analysis and spatial pattern of woody plant communities of Oregon forests. Ecol Monogr 68(2):151–182

    Article  Google Scholar 

  • Omernik JM (1987) Ecoregions of the conterminous United States. Annu Assoc Am Geogr 77(1):118–125

    Article  Google Scholar 

  • Oswald BP, Fancher JT, Kulhavy DL, Reeves HC (1999) Classifying fuels with aerial photography in East Texas. Int J Wildland Fire 9(2):109–113

    Article  Google Scholar 

  • Ottmar RD, Vihnanek R, Alvarado E (1994) Forest health assessment: air quality tradeoffs. In: Proceedings of the 12th international conference on fire and forest meterology, Jekyll Island, Georgia, USA, October 26–28, 1993. Society of American Foresters, Bethesda, MD, USA, pp 47–62

    Google Scholar 

  • Pala S, Taylor D, Holder G (1990) Integrating satellite-derived forest fuel data into fire management decision support models. In: Proceedings, second national GIS conference, Ottawa, Canada, March 5–8, 1990

    Google Scholar 

  • Peterson SH, Franklin J, Roberts DA, van Wagtendonk JW (2012) Mapping fuels in Yosemite National Park. Can J For Res 43(1):7–17. doi:10.1139/cjfr-2012-0213

    Article  Google Scholar 

  • Pierce AD, Farris CA, Taylor AH (2012) Use of random forests for modeling and mapping forest canopy fuels for fire behavior analysis in Lassen Volcanic National Park, California, USA. For Ecol Manage 279:77–89. doi:http://dx.doi.org/10.1016/j.foreco.2012.05.010

    Article  Google Scholar 

  • Quigley TM, Graham RT, Haynes RW (1996) An integrated scientific assessment for ecosystem management in the Interior Columbia River Basin and portions of the Klamath and Great Basins. US Department of Agriculture, Forest Service, Pacific Northwest Research Station, PNW-GTR-382 Portland, OR., USA, pp 126

    Google Scholar 

  • Reeves MC, Ryan KC, Rollins MC, Thompson TG (2009) Spatial fuel data products of the LANDFIRE project. Int J Wildland Fire 18:250–267

    Article  Google Scholar 

  • Reinhardt E, Lutes D, Scott J (2006) FuelCalc: a method for estimating fuel characteristics. In: Andrews PL, Butler BW (eds) Fuels management—how to measure success, Portland, OR, 2006a. I U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Proceedings RMRS-P-41 Fort Collins, CO, pp 273–287

    Google Scholar 

  • Riaño D, Meier E, Allgöwer B, Chuvieco E, Ustin SL (2003) Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sens Environ 86(2):177–186 http://dx.doi.org/10.1016/S0034-4257(03)00098-1

    Article  Google Scholar 

  • Rignot E, Way J, Williams C, Viereck L (1994) Radar estimates of aboveground biomass in boreal forests of interior Alaska. IEEE Trans Geosci Remote Sens 32(5):1117–1124

    Article  Google Scholar 

  • Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18(3):235–249. doi:10.1071/WF08088

    Article  Google Scholar 

  • Rollins MG, Keane RE, Parsons R (2004) Mapping ecological attributes using gradient analysis and remote sensing. Ecol Appl 14(1):75–95

    Article  Google Scholar 

  • Root RR, Wagtendonk J (1999) Hyperspectral analysis of multi-temporal Landsat TM data for mapping fuels in Yosemite National Park. In: Annual ASPRS Conference, Portland, OR., USA, May 17–21, 1999. Society of American Photogrammeters, Bethesda, MD., USA, pp 399–408

    Google Scholar 

  • Root RR, Stitt SCF, Nyquist MO, Waggoner GS, Agee JK (1985) Vegetation and fire fuel models mapping of North Cascades National Park. In: The tenth William T. Pecora remote sensing symposium, Fort Collins, CO, 1985, pp 287–294

    Google Scholar 

  • Saatchi S, Halligan K, Despain DG, Crabtree RL (2007) Estimation of forest fuel load from radar remote sensing. Geosci Remote Sens, IEEE Trans 45(6):1726–1740. doi:10.1109/tgrs.2006.887002

    Google Scholar 

  • Salas J, Chuvieco E (1994) Geographic information systems for wildland fire risk mapping. Wildfire 3(2):7–13

    Google Scholar 

  • Schmidt KM, Menakis JP, Hardy CC, Hann WJ, Bunnell DL (2002) Development of coarse-scale spatial data for wildland fire and fuel management. USDA Forest Service Rocky Mountain Research Station, General Technical Report RMRS-GTR-87 Fort Collins, CO, USA, pp 41

    Google Scholar 

  • Scott J, Burgan RE (2005) A new set of standard fire behavior fuel models for use with Rothermel’s surface fire spread model. USDA Forest Service Rocky Mountain Research Station, General Technical Report RMRS-GTR-153. Fort Collins, CO USA, pp 66

    Google Scholar 

  • Sikkink PG, Keane RE (2008) A comparison of five sampling techniques to estimate surface fuel loading in montane forests. Int J Wildland Fire 17(3):363–379. doi:Doi 10.1071/Wf07003

    Article  Google Scholar 

  • Skidmore AK (1989) A comparison of techniques for calculating gradient and aspect from a gridded digital elevation model. Int J Geogr Info Sys 3(4):323–334

    Article  Google Scholar 

  • Thornton PE, Law BE, Gholz HL, Clark KL, Falge E, Ellsworth DS, Goldstein AH, Monson RK, Hollinger DY, Falk M, Chen J, Sparks JP (2002) Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agric For Meteorol 113:185–222

    Article  Google Scholar 

  • Varga TA, Asner GP (2008) Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park. Ecol Appl 18(3):613–623. doi:10.1890/07-1280.1

    Article  PubMed  Google Scholar 

  • Vasconcelos JJPd, Paul JCU, Silva S, Pereira JMC, Caetono MS, Catry FX, Oliveira TM (1998) Regional fuel mapping using a knowledge based system approach. In: Proceedings: III international conference on forest fire research and 14th conference on fire and forest meterology, vol. II, 1998. pp 2111–2123

    Google Scholar 

  • Verbyla DL (1995) Satellite remote sensing of natural resources. Lewis Publishers, CRC Press Valley Stream, New York

    Google Scholar 

  • Vogelmann JE, Kost JR, Tolk B, Howard S, Short K, Xuexia C, Chengquan H, Pabst K, Rollins MG (2011) Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and Ancillary data. Selected topics in applied earth observations and remote sensing. IEEE J 4(2):252–264. doi:10.1109/jstars.2010.2044478

    Google Scholar 

  • Willis JM (1985) Applications of Landsat imagery for fire fuels mapping projects covering large geographic areas. In: Proceedings of the Pecora X symposium, 1985. American Society for Photogrammetry and Remote Sensing, Falls Church, Virginia, USA, pp 394–395

    Google Scholar 

  • Wilson BA, Ow CFY, 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. Glob Ecol Biogeogr Lett 4:33–39

    Article  Google Scholar 

  • Xiao-rui T, McRae D, Li-fu S, Ming-yu W (2005) Fuel classification and mapping from satellite imagines. J For Res 16(4):311–316. doi:10.1007/bf02858198

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Missoula Fire Sciences Laboratory, USDA Forest Service Rocky Mountain Research Station, Missoula, Montana, USA

    Robert E. Keane

Authors
  1. Robert E. Keane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert E. Keane .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Keane, R. (2015). Fuel Mapping. In: Wildland Fuel Fundamentals and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-09015-3_9

Download citation

Publish with us

Policies and ethics

Search

Navigation