Flame filtering and perimeter localization of wildfires using aerial thermal imagery

Airborne thermal infrared (TIR) imaging systems are being increasingly used for wild fire tactical monitoring since they show important advantages over spaceborne platforms and visible sensors while becoming much more affordable and much lighter than multispectral cameras. However, the analysis of aerial TIR images entails a number of difficulties which have thus far prevented monitoring tasks from being totally automated. One of these issues that needs to be addressed is the appearance of flame projections during the geo-correction of off-nadir images. Filtering these flames is essential in order to accurately estimate the geographical location of the fuel burning interface. Therefore, we present a methodology which allows the automatic localisation of the active fire contour free of flame projections. The actively burning area is detected in TIR georeferenced images through a combination of intensity thresholding techniques, morphological processing and active contours. Subsequently, flame projections are filtered out by the temporal frequency analysis of the appropriate contour descriptors. The proposed algorithm was tested on footages acquired during three large-scale field experimental burns. Results suggest this methodology may be suitable to automatise the acquisition of quantitative data about the fire evolution. As future work, a revision of the low-pass filter implemented for the temporal analysis (currently a median filter) was recommended. The availability of up-to-date information about the fire state would improve situational awareness during an emergency response and may be used to calibrate data-driven simulators capable of emitting short-term accurate forecasts of the subsequent fire evolution.

[1]  Francis Y. Enomoto,et al.  The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010) , 2011 .

[2]  Eulàlia Planas,et al.  Computing forest fires aerial suppression effectiveness by IR monitoring , 2011 .

[3]  E. Pastor,et al.  Short-term fire front spread prediction using inverse modelling and airborne infrared images , 2016 .

[4]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[5]  Didier Lucor,et al.  Interactive comment on “Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation” , 2014 .

[6]  Philip J. Riggan,et al.  Application of the firemappertm thermal-imaging radiometer for wildfire suppression , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[7]  Grant J. Williamson,et al.  Climate-induced variations in global wildfire danger from 1979 to 2013 , 2015, Nature Communications.

[8]  Elsa Pastor Ferrer,et al.  Automatic detection of wildfire active fronts from aerial thermal infrared images , 2015 .

[9]  Arnaud Trouvé,et al.  Towards predictive data-driven simulations of wildfire spread - Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread , 2014 .

[10]  Andrew Zisserman,et al.  Projective Geometry and Transformations of 2D , 2004 .

[11]  Francisco Manzano-Agugliaro,et al.  Methodology to obtain isochrones from large wildfires , 2014 .

[12]  M. Flannigan,et al.  Global wildland fire season severity in the 21st century , 2013 .

[13]  Juli G. Pausas Changes in Fire and Climate in the Eastern Iberian Peninsula (Mediterranean Basin) , 2004 .

[14]  Steven Verstockt,et al.  Methods and Techniques for Fire Detection: Signal, Image and Video Processing Perspectives , 2016 .

[15]  Juan Andrade-Cetto,et al.  Computing the rate of spread of linear flame fronts by thermal image processing , 2006 .

[16]  Steven Verstockt,et al.  Silhouette-based multi-sensor smoke detection , 2011, Machine Vision and Applications.

[17]  William D. Holley,et al.  Measuring radiant emissions from entire prescribed fires with ground, airborne and satellite sensors – RxCADRE 2012 , 2016 .

[18]  W. Jahn,et al.  Forecasting wind-driven wildfires using an inverse modelling approach , 2013 .

[19]  Gregory W. Walker,et al.  Evaluation and use of remotely piloted aircraft systems for operations and research – RxCADRE 2012 , 2016 .

[20]  S. A. Lewis,et al.  Remote sensing techniques to assess active fire characteristics and post-fire effects , 2006 .

[21]  Elsa Pastor,et al.  Criteria and methodology for evaluating aerial wildfire suppression , 2013 .

[22]  Gareth Roberts,et al.  Use of Handheld Thermal Imager Data for Airborne Mapping of Fire Radiative Power and Energy and Flame Front Rate of Spread , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Jonathan D. Beezley,et al.  A wildland fire model with data assimilation , 2007, Math. Comput. Simul..

[24]  Vincent G. Ambrosia,et al.  Integrating sensor data and geospatial tools to enhance real-time disaster management capabilities: Wildfire observations , 2011 .

[25]  Zoubir Hamici,et al.  Real-Time Pattern Recognition using Circular Cross-Correlation: a robot Vision System , 2006, Int. J. Robotics Autom..

[26]  R. Shakesby,et al.  Post-wildfire soil erosion in the Mediterranean: Review and future research directions , 2011 .

[27]  Lloyd L. Coulter,et al.  Measuring fire spread rates from repeat pass airborne thermal infrared imagery , 2014 .

[28]  Amir AghaKouchak,et al.  On the key role of droughts in the dynamics of summer fires in Mediterranean Europe , 2017, Scientific Reports.

[29]  M. Krawchuk,et al.  Implications of changing climate for global wildland fire , 2009 .

[30]  Vincent G. Ambrosia,et al.  Unmanned Airborne Platforms For Disaster Remote Sensing Support , 2009 .