Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates
C. Klauberg A , A. T. Hudak A E , B. C. Bright A , L. Boschetti B , M. B. Dickinson C , R. L. Kremens D and C. A. Silva B
+ Author Affiliations
- Author Affiliations
A USDA Forest Service Rocky Mountain Station, Forestry Sciences Laboratory, 1221 South Main Street, Moscow, ID 83843, USA.
B University of Idaho, 708 South Deakin Street, Moscow, ID 83844, USA.
C USDA Forest Service, Northern Research Station, 359 Main Road, Delaware, OH 43015, USA.
D Rochester Institute of Technology, Center of Imaging Science, 54 Lomb Memorial Drive, Rochester, NY 14623, USA.
E Corresponding author. Email: ahudak@fs.fed.us
International Journal of Wildland Fire 27(4) 228-240 https://doi.org/10.1071/WF17113
Submitted: 29 July 2017 Accepted: 17 February 2018 Published: 23 April 2018
Abstract
Fire 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.
Additional keywords: fire behaviour, fire modeling, fire modelling, fire radiative energy, remote sensing, RxCADRE.
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