Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires
The wildland-urban interface (WUI) is the spatial manifestation of human communities coupled with vegetated ecosystems. Spatial delineation of the WUI is important for wildfire policy and management, but is typically defined according to spatial relationships between housing development and wildland...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2020-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ab9be5 |
| _version_ | 1797747841966276608 |
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| author | Alejandro Miranda Jaime Carrasco Mauro González Cristobal Pais Antonio Lara Adison Altamirano Andrés Weintraub Alexandra D Syphard |
| author_facet | Alejandro Miranda Jaime Carrasco Mauro González Cristobal Pais Antonio Lara Adison Altamirano Andrés Weintraub Alexandra D Syphard |
| author_sort | Alejandro Miranda |
| collection | DOAJ |
| description | The wildland-urban interface (WUI) is the spatial manifestation of human communities coupled with vegetated ecosystems. Spatial delineation of the WUI is important for wildfire policy and management, but is typically defined according to spatial relationships between housing development and wildland vegetation without explicit consideration of fire risk. A fire risk-based definition of WUI can enable a better distribution of management investment so as to maximize social return. We present a novel methodological approach to delineate the WUI based on a fire risk assessment. The approach establishes a geographical framework to model fire risk via machine learning and generate multi-scale, variable-specific spatial thresholds for translating fire probabilities into mapped output. To determine whether fire-based WUI mapping better captures the spatial congruence of houses and wildfires than conventional methods, we compared national and subnational fire-based WUI maps for Chile to WUI maps generated only with housing and vegetation thresholds. The two mapping approaches exhibited broadly similar spatial patterns, the WUI definitions covering almost the same area and containing similar proportions of the housing units in the area under study (17.1% vs. 17.9%), but the fire-based WUI accounted for 13.8% more spatial congruence of fires and people (47.1% vs. 33.2% of ignitions). Substantial regional variability was found in fire risk drivers and the corresponding spatial mapping thresholds, suggesting there are benefits to developing different WUI maps for different scales of application. We conclude that a dynamic, multi-scale, fire-based WUI mapping approach should provide more targeted and effective support for decision making than conventional approaches. |
| first_indexed | 2024-03-12T15:56:24Z |
| format | Article |
| id | doaj.art-1f9ae9dade09462ca6c25aaabd99b91c |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:56:24Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-1f9ae9dade09462ca6c25aaabd99b91c2023-08-09T14:52:03ZengIOP PublishingEnvironmental Research Letters1748-93262020-01-0115909406910.1088/1748-9326/ab9be5Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfiresAlejandro Miranda0Jaime Carrasco1Mauro González2Cristobal Pais3Antonio Lara4Adison Altamirano5https://orcid.org/0000-0002-9638-7486Andrés Weintraub6Alexandra D Syphard7Center for Climate and Resilience Research (CR2), University of Chile , Santiago, Chile; Landscape Ecology and Conservation Lab, Facultad de Ciencias Agropecuarias y Forestales, Universidad de La Frontera , Temuco, Chile; Author to whom any correspondence should be addressed.Industrial Engineering Department, University of Chile , Santiago, Chile; Complex Engineering System Institute - ISCI , Santiago, ChileCenter for Climate and Resilience Research (CR2), University of Chile , Santiago, Chile; Instituto de Conservación, Biodiversidad y Territorio, Facultad de Ciencias Forestales y Recursos Naturales, Universidad Austral de Chile , Valdivia, ChileIndustrial Engineering and Operations Research Department, University of California, Berkeley , 94720 CA, United States of AmericaCenter for Climate and Resilience Research (CR2), University of Chile , Santiago, Chile; Instituto de Conservación, Biodiversidad y Territorio, Facultad de Ciencias Forestales y Recursos Naturales, Universidad Austral de Chile , Valdivia, Chile; Fundación Centro de los Bosques Nativos FORECOS , Valdivia, ChileLandscape Ecology and Conservation Lab, Facultad de Ciencias Agropecuarias y Forestales, Universidad de La Frontera , Temuco, Chile; Butamallin Research Center for Global Change, Facultad de Ciencias Agropecuarias y Forestales, Universidad de La Frontera , Temuco, ChileIndustrial Engineering Department, University of Chile , Santiago, Chile; Complex Engineering System Institute - ISCI , Santiago, ChileSage Insurance Holdings, LLC , La Mesa, CA, United States of AmericaThe wildland-urban interface (WUI) is the spatial manifestation of human communities coupled with vegetated ecosystems. Spatial delineation of the WUI is important for wildfire policy and management, but is typically defined according to spatial relationships between housing development and wildland vegetation without explicit consideration of fire risk. A fire risk-based definition of WUI can enable a better distribution of management investment so as to maximize social return. We present a novel methodological approach to delineate the WUI based on a fire risk assessment. The approach establishes a geographical framework to model fire risk via machine learning and generate multi-scale, variable-specific spatial thresholds for translating fire probabilities into mapped output. To determine whether fire-based WUI mapping better captures the spatial congruence of houses and wildfires than conventional methods, we compared national and subnational fire-based WUI maps for Chile to WUI maps generated only with housing and vegetation thresholds. The two mapping approaches exhibited broadly similar spatial patterns, the WUI definitions covering almost the same area and containing similar proportions of the housing units in the area under study (17.1% vs. 17.9%), but the fire-based WUI accounted for 13.8% more spatial congruence of fires and people (47.1% vs. 33.2% of ignitions). Substantial regional variability was found in fire risk drivers and the corresponding spatial mapping thresholds, suggesting there are benefits to developing different WUI maps for different scales of application. We conclude that a dynamic, multi-scale, fire-based WUI mapping approach should provide more targeted and effective support for decision making than conventional approaches.https://doi.org/10.1088/1748-9326/ab9be5rural-urban interfacemachine learningartificial intelligencelandscape planningfire ignitionsChile |
| spellingShingle | Alejandro Miranda Jaime Carrasco Mauro González Cristobal Pais Antonio Lara Adison Altamirano Andrés Weintraub Alexandra D Syphard Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires Environmental Research Letters rural-urban interface machine learning artificial intelligence landscape planning fire ignitions Chile |
| title | Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires |
| title_full | Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires |
| title_fullStr | Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires |
| title_full_unstemmed | Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires |
| title_short | Evidence-based mapping of the wildland-urban interface to better identify human communities threatened by wildfires |
| title_sort | evidence based mapping of the wildland urban interface to better identify human communities threatened by wildfires |
| topic | rural-urban interface machine learning artificial intelligence landscape planning fire ignitions Chile |
| url | https://doi.org/10.1088/1748-9326/ab9be5 |
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