International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Measurements relating fire radiative energy density and surface fuel consumption – RxCADRE 2011 and 2012

Andrew T. Hudak A F , Matthew B. Dickinson B , Benjamin C. Bright A , Robert L. Kremens C , E. Louise Loudermilk D , Joseph J. O’Brien D , Benjamin S. Hornsby D and Roger D. Ottmar E
+ Author Affiliations
- Author Affiliations

A USDA Forest Service Rocky Mountain Research Station, Forestry Sciences Laboratory, 1221 South Main Street, Moscow, ID 83843, USA.

B USDA Forest Service, Northern Research Station, 359 Main Road, Delaware, OH 43015, USA.

C Rochester Institute of Technology, Center for Imaging Science, 54 Lomb Memorial Drive, Rochester, NY 14623, USA.

D USDA Forest Service, Southern Research Station, Center for Forest Disturbance Science, 320 Green Street, Athens, GA 30602, USA.

E USDA Forest Service, Pacific Northwest Research Station, Pacific Wildland Fire Sciences Laboratory, 400 North 34th Street, Suite 201, Seattle, WA 98103, USA.

F Corresponding author. Email: ahudak@fs.fed.us

International Journal of Wildland Fire 25(1) 25-37 https://doi.org/10.1071/WF14159
Submitted: 11 September 2014  Accepted: 11 May 2015   Published: 28 July 2015

Abstract

Small-scale experiments have demonstrated that fire radiative energy is linearly related to fuel combusted but such a relationship has not been shown at the landscape level of prescribed fires. This paper presents field and remotely sensed measures of pre-fire fuel loads, consumption, fire radiative energy density (FRED) and fire radiative power flux density (FRFD), from which FRED is integrated, across forested and non-forested RxCADRE 2011 and 2012 burn blocks. Airborne longwave infrared (LWIR) image time series were calibrated to FRFD and integrated to provide FRED. Surface fuel loads measured in clip sample plots were predicted across burn blocks from airborne lidar-derived metrics. Maps of surface fuels and FRED were corrected for occlusion of the radiometric signal by the overstorey canopy in the forested blocks, and FRED maps were further corrected for temporal and spatial undersampling of FRFD. Fuel consumption predicted from FRED derived from both airborne LWIR imagery and various ground validation sensors approached a linear relationship with observed fuel consumption, which matched our expectation. These field, airborne lidar and LWIR image datasets, both before and after calibrations and corrections have been applied, will be made publicly available from a permanent archive for further analysis and to facilitate fire modelling.

Additional keywords: fire radiative energy (FRE), fire radiative power (FRP), fuel map, LiDAR, long-wave infrared (LWIR), RxCADRE.


References

Baskerville GL (1972) Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research 2, 49–53.
Use of logarithmic regression in the estimation of plant biomass.CrossRef |

Berk A, Anderson GP, Acharya PK, Hoke M, Chetwynd J, Bernstein L, Shettle EP, Matthew MW, Alder-Golden SM (2003). MODTRAN4 version 3 revision 1 user’s manual. Air Force Research Laboratory, Bedford, MA.

Boschetti L, Roy DP (2009) Strategies for the fusion of satellite fire radiative power with burned area data for fire radiative energy derivation. Journal of Geophysical Research 114, D20302
Strategies for the fusion of satellite fire radiative power with burned area data for fire radiative energy derivation.CrossRef |

Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, D’Antonio CM, DeFries RS, Doyle JC, Harrison SP, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Marston JB, Moritz MA, Prentice IC, Roos CI, Scott AC, Swetnam TW, van der Werf GR, Pyne SJ (2009) Fire in the earth system. Science 324, 481–484.
Fire in the earth system.CrossRef | 1:CAS:528:DC%2BD1MXkvVGmtb8%3D&md5=acf0f16875fe2576863ea8a4f6d043c7CAS |

Butler B, Teskey C, Jimenez D, O’Brien J, Sopko P, Wold C, Vosburgh M, Hornsby B, Loudermilk E (2015) Observations of fire intensity and fire spread rate – RxCADRE 2012 International Journal of Wildland Fire , in press.

Dickinson MB, Ellison L, Hudak AT, Ichoku C, Kremens RL, Loudermilk L, Hornsby B, O’Brien JJ, Paxton A, Schroeder W, Zajkowski T, Holley W, Bright B, Martinez O, Mauseri J (2015) Comparing ground, airborne, and satellite measurements of fire radiative power – developing methods and datasets for cross-scale validation. International Journal of Wildland Fire , in press.

Freeborn PH, Wooster MJ, Roberts G (2011) Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa. Remote Sensing of Environment 115, 475–489.
Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa.CrossRef |

Hall SA, Burke IC, Box DO, Kaufmann MR, Stoker JM (2005) Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forest. Forest Ecology and Management 208, 189–209.
Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forest.CrossRef |

Hiers JK, O’Brien JJ, Mitchell RJ, Grego JM, Loudermilk EL (2009) The wildland fuel cell concept: an approach to characterize fine-scale variation in fuels and fire in frequently burned longleaf pine forests. International Journal of Wildland Fire 18, 315–325.
The wildland fuel cell concept: an approach to characterize fine-scale variation in fuels and fire in frequently burned longleaf pine forests.CrossRef |

Hudak AT, Crookston NL, Evans JS, Falkowski MJ, Smith AMS, Gessler P, Morgan P (2006) Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data. Canadian Journal of Remote Sensing 32, 126–138.
Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data.CrossRef |

Kim YZ, Yang ZQ, Cohen WB, Pflugmacher D, Lauver CL, Vankat JL (2009) Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment 113, 2499–2510.
Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data.CrossRef |

Kremens RL, Dickinson MB, Bova AS (2012) Radiant flux density, energy density, and fuel consumption in mixed-oak forest surface fires. International Journal of Wildland Fire 21, 722–730.
Radiant flux density, energy density, and fuel consumption in mixed-oak forest surface fires.CrossRef |

Kumar SS, Roy DP, Boschetti L, Kremens R (2011) Exploiting the power law distribution properties of satellite fire radiative power retrievals – a method to estimate fire radiative energy and biomass burned from sparse satellite observations. Journal of Geophysical Research 116, D19303
Exploiting the power law distribution properties of satellite fire radiative power retrievals – a method to estimate fire radiative energy and biomass burned from sparse satellite observations.CrossRef |

Lavorel S, Flannigan MD, Lambin EF, Scholes MC (2007) Vulnerability of land systems to fire: interactions among humans, climate, the atmosphere, and ecosystems. Mitigation and Adaptation Strategies for Global Change 12, 33–53.
Vulnerability of land systems to fire: interactions among humans, climate, the atmosphere, and ecosystems.CrossRef |

Lewis SA, Hudak AT, Lentile LB, Ottmar RD, Cronan JB, Hood SM, Robichaud PR, Morgan P (2011) Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA. International Journal of Wildland Fire 20, 255–271.
Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA.CrossRef |

Loudermilk EL, Hiers JK, O’Brien JJ, Mitchell RJ, Singhania A, Fernandez JC, Cropper WP, Slatton KC (2009) Ground-based LIDAR: a novel approach to quantify fine-scale fuelbed characteristics. International Journal of Wildland Fire 18, 676–685.
Ground-based LIDAR: a novel approach to quantify fine-scale fuelbed characteristics.CrossRef |

Loudermilk EL, O’Brien JJ, Mitchell RJ, Cropper WP, Hiers JK, Grunwald S, Grego J, Fernandez-Diaz JC (2012) Linking complex forest fuel structure and fire behaviour at fine scales. International Journal of Wildland Fire 21, 882–893.
Linking complex forest fuel structure and fire behaviour at fine scales.CrossRef |

McGaughey RJ (2014) FUSION/LDV: Software for LIDAR data analysis and visualization, Version 3.42. USDA Forest Service, Pacific Northwest Research Station (Seattle, WA)

McKeown D, Cockburn J, Faulring J, Kremens RL, Morse D, Rhody H, Richardson M (2004) Wildfire airborne sensor program (WASP): a new wildland fire detection and mapping system. In ‘Remote Sensing for Field Users: Proceedings of the Tenth Forest Service Remote Sensing Applications Conference. (CD-ROM) (Bethesda, MD: American Society of Photogrammetry and Remote Sensing).

O’Brien JJ, Loudermilk L, Hornsby B, Hiers K, Ottmar R (2015) High resolution infrared thermography as a tool for capturing fire behavior in wildland fires. International Journal of Wildland Fire , in press.

Ottmar RD, Hudak AT, Prichard SJ, Wright CS, Restaino J, Vihnanek RE (2015a) Pre- and postfire surface fuel and cover measurements from experimental and operational prescribed fires in longleaf pine ecosystems of the southeast United States – RxCADRE. International Journal of Wildland Fire , in press.

Ottmar RD, Hiers JK, Clements CB, Butler B, Dickinson MB, Potter B, O’Brien JJ, Hudak AT, Rowell EM, Zajkowski TJ (2015b) Datasets for fire model development and evaluation—the RxCADRE project. International Journal of Wildland Fire , in press.

R Core Team (2014) ‘R: a Language and Environment for Statistical Computing.’ (R Foundation for Statistical Computing: Vienna, Austria) Available at http://www.r-project.org [Verified 11 April 2015]

Reid AM, Robertson KM (2012) Energy content of common fuels in upland pine savannas of the south-eastern US and their application to fire behavior modelling. International Journal of Wildland Fire 21, 591–595.
Energy content of common fuels in upland pine savannas of the south-eastern US and their application to fire behavior modelling.CrossRef |

Riggan PJ, Tissell RG, Lockwood RN, Brass JA, Pereira JAR, Miranda HS, Miranda AC, Campos T, Higgins RG (2004) Remote measurement of energy and carbon flux from wildfires in Brazil. Ecological Applications 14, 855–872.
Remote measurement of energy and carbon flux from wildfires in Brazil.CrossRef |

Roberts G, Wooster MJ (2008) Fire detection and fire characterization over Africa using Meteosat SEVIRI. IEEE Transactions on Geoscience and Remote Sensing 46, 1200–1218.
Fire detection and fire characterization over Africa using Meteosat SEVIRI.CrossRef |

Roberts G, Wooster MJ, Freeborn P, Xu W (2011) Integration of geostationary FRP and polar orbiter burned area datasets for an enhanced biomass burning inventory. Remote Sensing of Environment 115, 2047–2061.
Integration of geostationary FRP and polar orbiter burned area datasets for an enhanced biomass burning inventory.CrossRef |

Robinson AP, Duursma RA, Marshall JD (2005) A regression-based equivalence test for model validation: shifting the burden of proof. Tree Physiology 25, 903–913.
A regression-based equivalence test for model validation: shifting the burden of proof.CrossRef | 15870057PubMed |

Rowell EM, Seielstad CA (2015) Developing and validating fuel height models for the RxCADRE experiments. International Journal of Wildland Fire , in press.

Schroeder W, Oliva P, Giglio L, Csiszar I (2014) The new VIIRS 375 m active fire detection data product: algorithm description and initial assessment. Remote Sensing of Environment 143, 85–96.
The new VIIRS 375 m active fire detection data product: algorithm description and initial assessment.CrossRef |

Seielstad CA, Queen L (2003) Using airborne laser altimetry to determine fuel models for estimating fire behavior. Journal of Forestry 101, 10–15.

Seiler W, Crutzen PJ (1980) Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Climatic Change 2, 207–247.
Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning.CrossRef | 1:CAS:528:DyaL3cXlsFelt7c%3D&md5=a1573b188f0425514f06e074b7aab43cCAS |

Smith AMS, Tinkham WT, Roy DP, Boschetti L, Kremens RL, Kumar SS, Sparks AM, Falkowski MJ (2013) Quantification of fuel moisture effects on biomass consumed derived from fire radiative energy retrievals. Geophysical Research Letters 40, 6298–6302.
Quantification of fuel moisture effects on biomass consumed derived from fire radiative energy retrievals.CrossRef |

Trigg SN, Roy DP (2007) A focus group study of factors that promote and constrain the use of satellite derived fire products by resource managers in southern Africa. Journal of Environmental Management 82, 95–110.
A focus group study of factors that promote and constrain the use of satellite derived fire products by resource managers in southern Africa.CrossRef | 1:STN:280:DC%2BD28nltFalsw%3D%3D&md5=7b02fc9066a84f624b83f9ddd3245d78CAS | 16677754PubMed |

Wooster MJ, Roberts G, Perry GLW, Kaufman YJ (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. Journal of Geophysical Research, D, Atmospheres 110, D24311
Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release.CrossRef |

Wooster MJ, Roberts G, Smith AMS, Johnston J, Freeborn P, Amici S, Hudak AT (2013) Thermal remote sensing of active fires and biomass burning events. In ‘Thermal infrared remote sensing: sensors, methods, applications’ (Eds C Kuenzer, S Dech) pp. 347–390. (Springer: Dordrecht, Germany)



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