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

Gustave Udahemuka,Yskandar Hamam,Barend J Van Wyk

doi:10.3390/rs12101661

Abstract

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.

Highlights

Data acquired from satellite sensors are widely used in the automatic detection of active wildfires
No cloud screening is implemented, and no sunglint-contaminated sample screening is implemented
Ensemble Kalman Filter (EnKF)-CD(Ne=51) and Sampling Importance Resampling (SIR)-CD(Np=51) at 10−3.5 False Alarm Rate (FAR) achieve a true positive rate of 78.64% and 79.34%, respectively, of fire events reported in MODerate Resolution Imaging Spectroradiometer (MODIS)

Summary

Introduction

Data acquired from satellite sensors are widely used in the automatic detection of active wildfires. Since GEO fire-sensitive MIR data were available, most GEO-based fire detection techniques were conceived by adapting the pre-existing LEO methods on GEO data These techniques used the same spatial-contextual information to define the expected pixel’s background temperature during fire detection [10,11,12,13,14] or fire confirmation [15,16,17]. A spatial-contextual mechanism has its drawbacks, such as Point Spread Function (PSF) effect, spatial heterogeneity, and undetected cloud-contaminated pixels that contribute to both omission and commission errors [1,18,19] The performance of such techniques is restricted by the low spatial resolution of GEO data. Temporal information incorporates land surface characteristics, but to consider the weather and atmosphere, a combination of temporal, spatial, and spectral information must be assimilated into the background temperature model of a pixel

Methods

Results

Discussion

Conclusion