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Open AccessArticle

Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection

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Department of Electrical Engineering & French South African Institute of Technology, Tshwane University of Technology, Private Bag X680, Pretoria 0001, South Africa
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Faculty of Engineering, the Built Environment and Technology, Nelson Mandela University, P.O. Box 77000, Port Elizabeth 6031, South Africa
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École Supérieure d’ Ingénieurs en Électrotechnique et Électronique, Cité Descartes, BP 99, Noisy-le-Grand, 93162 Paris, France
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1661; https://doi.org/10.3390/rs12101661
Received: 26 March 2020 / Revised: 26 April 2020 / Accepted: 5 May 2020 / Published: 21 May 2020
Detection of an active fire in an image scene relies on an accurate estimation of the background temperature of the scene, which must be compared to the observed temperature, to decide on the presence of fire. The expected background temperature of a pixel is commonly derived based on spatial-contextual information that can overestimate the background temperature of a fire pixel and therefore results in the omission of a fire event. This paper proposes a method that assimilates brightness temperatures acquired from the Geostationary Earth Orbit (GEO) sensor MSG-SEVIRI into a Diurnal Temperature Cycle (DTC) model. The expected brightness temperatures are observational forecasts derived using the ensemble forecasting approach. The threshold on the difference between the observed and expected temperatures is derived under a Constant False Alarm Rate (CFAR) framework. The detection results are assessed against a reference dataset comprised of MODIS MOD14/MYD14 and EUMETSAT FIR products, and the performance is presented in terms of user’s and producer’s accuracies, and Precision-Recall and Receiver Operating Characteristic (ROC) graphs. The method has a high detection rate when the data assimilation is implemented with an Ensemble Kalman Filter (EnKF) and a Sampling Importance Resampling (SIR) particle filter, while the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) has comparatively lower detection and false alarm rates according to the reference dataset. Consideration of the diurnal variation in the background temperature enables the proposed method to detect even low-power fires. View Full-Text
Keywords: remote sensing data; active fire detection; background temperature of a fire pixel; Diurnal Temperature Cycle (DTC); data assimilation; ensemble forecasting; change detection; METEOSAT Second Generation (MSG) remote sensing data; active fire detection; background temperature of a fire pixel; Diurnal Temperature Cycle (DTC); data assimilation; ensemble forecasting; change detection; METEOSAT Second Generation (MSG)
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MDPI and ACS Style

Udahemuka, G.; van Wyk, B.J.; Hamam, Y. Characterization of Background Temperature Dynamics of a Multitemporal Satellite Scene through Data Assimilation for Wildfire Detection. Remote Sens. 2020, 12, 1661.

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