Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Biodiversidade Brasileira |
Texto Completo: | https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1036 |
Resumo: | The science of fire spread modelling has traditionally focused on providing predictive tools using empirically derived rate of spread calculations. However, fire spread prediction is difficult and whilst it can be a helpful tool for emergency management support the effective use of predictive rate of spread models are generally limited to relatively small spatiotemporal scales in data-rich environments. It is argued that this approach has particular value in the context of in the vast fire prone landscapes of tropical Northern Australia where biodiversity, cultural and carbon abatement considerations are more significant drives than emergency management. This is applied through a novel participatory modelling and gamification approach aimed at building shared understandings of complex fire behaviour. Described is the development of a Stochastic Cellular Automata fire behaviour simulation through a participatory modelling process that makes explicit the key variables affecting fire behaviour at a landscape scale in Northern Australia. This work aims to fill a gap in fire behaviour modelling tools by focusing on learning and participatory planning outcomes though fire behaviour simulation. A range of operational applications of this approach, within indigenous and non-indigenous communities of Northern Australia, are described and the value of predictive vrs explanatory environmental modelling for understanding fire behaviour assessed. |
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Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community EngagementFire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community EngagementThe science of fire spread modelling has traditionally focused on providing predictive tools using empirically derived rate of spread calculations. However, fire spread prediction is difficult and whilst it can be a helpful tool for emergency management support the effective use of predictive rate of spread models are generally limited to relatively small spatiotemporal scales in data-rich environments. It is argued that this approach has particular value in the context of in the vast fire prone landscapes of tropical Northern Australia where biodiversity, cultural and carbon abatement considerations are more significant drives than emergency management. This is applied through a novel participatory modelling and gamification approach aimed at building shared understandings of complex fire behaviour. Described is the development of a Stochastic Cellular Automata fire behaviour simulation through a participatory modelling process that makes explicit the key variables affecting fire behaviour at a landscape scale in Northern Australia. This work aims to fill a gap in fire behaviour modelling tools by focusing on learning and participatory planning outcomes though fire behaviour simulation. A range of operational applications of this approach, within indigenous and non-indigenous communities of Northern Australia, are described and the value of predictive vrs explanatory environmental modelling for understanding fire behaviour assessed.The science of fire spread modelling has traditionally focused on providing predictive tools using empirically derived rate of spread calculations. However, fire spread prediction is difficult and whilst it can be a helpful tool for emergency management support the effective use of predictive rate of spread models are generally limited to relatively small spatiotemporal scales in data-rich environments. It is argued that this approach has particular value in the context of in the vast fire prone landscapes of tropical Northern Australia where biodiversity, cultural and carbon abatement considerations are more significant drives than emergency management. This is applied through a novel participatory modelling and gamification approach aimed at building shared understandings of complex fire behaviour. Described is the development of a Stochastic Cellular Automata fire behaviour simulation through a participatory modelling process that makes explicit the key variables affecting fire behaviour at a landscape scale in Northern Australia. This work aims to fill a gap in fire behaviour modelling tools by focusing on learning and participatory planning outcomes though fire behaviour simulation. A range of operational applications of this approach, within indigenous and non-indigenous communities of Northern Australia, are described and the value of predictive vrs explanatory environmental modelling for understanding fire behaviour assessed.Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio)2019-11-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistaeletronica.icmbio.gov.br/BioBR/article/view/103610.37002/biodiversidadebrasileira.v9i1.1036Biodiversidade Brasileira ; v. 9 n. 1 (2019): Wildfire Conference: Resumos; 118Biodiversidade Brasileira ; Vol. 9 No. 1 (2019): Wildfire Conference: Resumos; 118Biodiversidade Brasileira ; Vol. 9 Núm. 1 (2019): Wildfire Conference: Resumos; 1182236-288610.37002/biodiversidadebrasileira.v9i1reponame:Biodiversidade Brasileirainstname:Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)instacron:ICMBIOenghttps://revistaeletronica.icmbio.gov.br/BioBR/article/view/1036/763Copyright (c) 2021 Biodiversidade Brasileira - BioBrasilhttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessFisher, Rohan2023-05-09T12:56:02Zoai:revistaeletronica.icmbio.gov.br:article/1036Revistahttps://revistaeletronica.icmbio.gov.br/BioBRPUBhttps://revistaeletronica.icmbio.gov.br/BioBR/oaifernanda.oliveto@icmbio.gov.br || katia.ribeiro@icmbio.gov.br2236-28862236-2886opendoar:2023-05-09T12:56:02Biodiversidade Brasileira - Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO)false |
dc.title.none.fl_str_mv |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement |
title |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement |
spellingShingle |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement Fisher, Rohan |
title_short |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement |
title_full |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement |
title_fullStr |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement |
title_full_unstemmed |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement |
title_sort |
Fire Behaviour Simulation; a Gamification Approach Supporting Complex System Learning and, Fire Management Planning and Community Engagement |
author |
Fisher, Rohan |
author_facet |
Fisher, Rohan |
author_role |
author |
dc.contributor.author.fl_str_mv |
Fisher, Rohan |
description |
The science of fire spread modelling has traditionally focused on providing predictive tools using empirically derived rate of spread calculations. However, fire spread prediction is difficult and whilst it can be a helpful tool for emergency management support the effective use of predictive rate of spread models are generally limited to relatively small spatiotemporal scales in data-rich environments. It is argued that this approach has particular value in the context of in the vast fire prone landscapes of tropical Northern Australia where biodiversity, cultural and carbon abatement considerations are more significant drives than emergency management. This is applied through a novel participatory modelling and gamification approach aimed at building shared understandings of complex fire behaviour. Described is the development of a Stochastic Cellular Automata fire behaviour simulation through a participatory modelling process that makes explicit the key variables affecting fire behaviour at a landscape scale in Northern Australia. This work aims to fill a gap in fire behaviour modelling tools by focusing on learning and participatory planning outcomes though fire behaviour simulation. A range of operational applications of this approach, within indigenous and non-indigenous communities of Northern Australia, are described and the value of predictive vrs explanatory environmental modelling for understanding fire behaviour assessed. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-11-15 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1036 10.37002/biodiversidadebrasileira.v9i1.1036 |
url |
https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1036 |
identifier_str_mv |
10.37002/biodiversidadebrasileira.v9i1.1036 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistaeletronica.icmbio.gov.br/BioBR/article/view/1036/763 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Biodiversidade Brasileira - BioBrasil https://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Biodiversidade Brasileira - BioBrasil https://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) |
publisher.none.fl_str_mv |
Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) |
dc.source.none.fl_str_mv |
Biodiversidade Brasileira ; v. 9 n. 1 (2019): Wildfire Conference: Resumos; 118 Biodiversidade Brasileira ; Vol. 9 No. 1 (2019): Wildfire Conference: Resumos; 118 Biodiversidade Brasileira ; Vol. 9 Núm. 1 (2019): Wildfire Conference: Resumos; 118 2236-2886 10.37002/biodiversidadebrasileira.v9i1 reponame:Biodiversidade Brasileira instname:Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) instacron:ICMBIO |
instname_str |
Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) |
instacron_str |
ICMBIO |
institution |
ICMBIO |
reponame_str |
Biodiversidade Brasileira |
collection |
Biodiversidade Brasileira |
repository.name.fl_str_mv |
Biodiversidade Brasileira - Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) |
repository.mail.fl_str_mv |
fernanda.oliveto@icmbio.gov.br || katia.ribeiro@icmbio.gov.br |
_version_ |
1797042391460347904 |