Please use this identifier to cite or link to this item: https://hdl.handle.net/10316.2/34282
DC FieldValueLanguage
dc.contributor.authorRios, Oriol
dc.contributor.authorPastor, Elsa
dc.contributor.authorTarragó, Diana
dc.contributor.authorRein, Guillermo
dc.contributor.authorPlanas, Eulàlia
dc.date.accessioned2014-10-23T16:20:56Z
dc.date.accessioned2020-09-09T21:31:12Z-
dc.date.available2014-10-23T16:20:56Z
dc.date.available2020-09-09T21:31:12Z-
dc.date.issued2014-
dc.identifier.isbn978-989-26-0884-6
dc.identifier.urihttps://hdl.handle.net/10316.2/34282-
dc.description.abstractA key factor in decision-making process during a wildfire incident is counting on the forecast of how the fire is likely to behave in different fuels, weather conditions and terrain. Wildfire models and simulators attempt to assist fire responders in gaining understanding of the fire behaviour. The main hurdle to overcome when applying such technologies at operational level is the lack of a complete model that describes wildfire governing physics and the trade-off between accuracy and computing time. A forecasting prediction must be delivered within a positive lead time and current physical models are far beyond this requirement. Inverse modelling and data assimilation techniques offer a great potential of operational applicability in wildfires, coupling fire monitoring and fire behaviour forecast at real time. With this approach, a better description of the processes simulated by the fire behaviour models can be achieved when adding real-state information of the system, since discrepancies between simulated fire behaviour variables and observed variables are minimized. The use of this approach accelerates fire simulations without loss of forecast accuracy. In this paper we explore the adaptation to real fire scenarios of a synthetic-data-based inverse modelling structure for fire behaviour forecast. Improvements are investigated to extrapolate the already existing algorithm to real data assimilation from IR aerial monitoring. The technique explores elliptical Huygens expansion coupled with simple -yet effective- semi-empirical wildfire models. The algorithm assimilates fire fronts positions extracted from airborne thermal imaging and additional available data as wind speed and direction or fuel characteristics. The invariants -set of governing parameters that are mutually independent and constant for a significant amount of time- are resolved by means of forward model and linear tangent minimization. The technique has been adapted to be employed in large-scale mallee-heath shrubland fires experiments conducted in South Australia in 2008. Fires were filmed with a helicopter transported TIR camera. The IR images were processed to obtain the position of the fire perimeter at a maximum frequency of one isochrone every 10 seconds. The algorithm shows great capability to simulate fire fronts observations and opens the door to keep developing a fully automatic data assimilation algorithm with forecasting capacity.eng
dc.language.isoeng-
dc.publisherImprensa da Universidade de Coimbrapor
dc.relation.ispartofhttp://hdl.handle.net/10316.2/34013por
dc.rightsopen access-
dc.subjectInverse modellingeng
dc.subjectData assimilationeng
dc.subjectInfrared imageryeng
dc.subjectShrubland fire behavioureng
dc.titleShort term forecasting of large scale wind-driven wildfires using thermal imaging and inverse modelling techniquespor
dc.typebookPartpor
uc.publication.firstPage949-
uc.publication.lastPage960-
uc.publication.locationCoimbrapor
dc.identifier.doi10.14195/978-989-26-0884-6_103-
uc.publication.sectionChapter 3 - Fire Managementpor
uc.publication.digCollectionPBpor
uc.publication.orderno103-
uc.publication.areaCiências da Engenharia e Tecnologiaspor
uc.publication.bookTitleAdvances in forest fire research-
uc.publication.parentItemId53868-
uc.itemId70262-
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Advances in forest fire research
Files in This Item:
File SizeFormat 
978-989-26-0884-6_103.pdf1.96 MBAdobe PDFView/Open
Show simple item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.