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Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates [Corrigendum]Author(s): C. Klauberg; A. T. Hudak; B. C. Bright; L. Boschetti; M. B. Dickinson; R. L. Kremens; C. A. Silva
Source: International Journal of Wildland Fire. 27: 228-240.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionFire radiative energy density (FRED, J m-2) integrated from fire radiative power density (FRPD, W m-2) observations of landscape-level fires can present an undersampling problem when collected from fixed-wing aircraft. In the present study, the aircraft made multiple passes over the fire at ~3 min intervals, thus failing to observe most of the FRPD emitted as the flame front spread. We integrated the sparse FRPD time series to obtain pixel-level FRED estimates, and subsequently applied ordinary kriging (OK) and Gaussian conditional simulation (GCS) to interpolate across data voids caused by the undersampling. We compared FRED interpolated via OK and GCS with FRED estimated independently from ground measurements of biomass consumed from five prescribed burns at Eglin Air Force Base, Florida, USA. In four of five burns considered where undersampling prevailed, OK and GCS effectively interpolated FRED estimates across the data voids, improving the spatial distribution of FRED across the burning event and its overall mean. In a fifth burn, the burning characteristics were such that undersampling did not present a problem needing to be fixed. We also determined where burning and FRPD sampling characteristics merited applying OK and CGS only to the highest FRED estimates to interpolate more accurate FRED maps.
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CitationKlauberg, C.; Hudak, A. T.; Bright, B. C.; Boschetti, L.; Dickinson, M. B.; Kremens, R. L.; Silva, C. A. 2018. Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates [Corrigendum]. International Journal of Wildland Fire. 27: 228-240.
Keywordsfire behavior, fire modeling, fire modeling, fire radiative energy, remote sensing, RxCADRE
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