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Remote Sens. 2014, 6(3), 1890-1917;

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
King’s College London, Earth and Environmental Dynamics Research Group, Department of Geography, London WC2R 2LS, UK
NERC National Centre for Earth Observation, UK
Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
Author to whom correspondence should be addressed.
Received: 27 December 2013 / Revised: 18 February 2014 / Accepted: 24 February 2014 / Published: 28 February 2014
PDF [1599 KB, uploaded 19 June 2014]


Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI’s omission rate can be attributed to a coupling between SEVIRI’s low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI’s commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring. View Full-Text
Keywords: validation; SEVIRI; geostationary active fire detection algorithm; MODIS validation; SEVIRI; geostationary active fire detection algorithm; MODIS
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Freeborn, P.H.; Wooster, M.J.; Roberts, G.; Xu, W. Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product. Remote Sens. 2014, 6, 1890-1917.

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