Mapping the daily progression of large wildland fires using MODIS active fire data
Sander Veraverbeke A B D , Fernando Sedano B C , Simon J. Hook A , James T. Randerson B , Yufang Jin B and Brendan M. Rogers B
+ Author Affiliations
- Author Affiliations
A Jet Propulsion Laboratory (NASA), California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA.
B Department of Earth System Science, University of California, 2224 Croul Hall, Irvine, CA 92697, USA.
C Department of Geograpical Sciences, University of Maryland, 2181 LeFrak Hall, College Park, MD 20742, USA.
D Corresponding author. Email: sander.veraverbeke@uci.edu
International Journal of Wildland Fire 23(5) 655-667 https://doi.org/10.1071/WF13015
Submitted: 30 January 2013 Accepted: 12 November 2013 Published: 7 March 2014
Abstract
High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were defined using variogram analysis. The optimal number of observations used to estimate a pixel’s time of burning varied between four and six among the fires studied. The median standard error from kriging ranged between 0.80 and 3.56 days and the median standard error from geolocation uncertainty was between 0.34 and 2.72 days. For nine fires in the south-western US, the accuracy of the kriging model was assessed using high spatial resolution daily fire perimeter data available from the US Forest Service. For these nine fires, we also assessed the temporal reporting accuracy of the MODIS burnt area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared with 33% for MCD45A1 and 53% for MCD64A1. Systematic application of this algorithm to wildland fires in the future may lead to new information about vegetation, climate and topographic controls on fire behaviour.
Additional keywords: carbon emissions, fire growth, fire propagation, fire spread.
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