Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile

<p>Wildfire risk is latent in Chilean metropolitan areas characterized by the strong presence of wildland–urban interfaces (WUIs). The Concepción metropolitan area (CMA) constitutes one of the most representative samples of that dynamic. The wildfire risk in the CMA was addressed by establishi...

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Autores principales:	E. Jaque Castillo, A. Fernández, R. Fuentes Robles, C. G. Ojeda
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/f68e2c70b9b74d65a7ff8235ec9e7266
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spelling	oai:doaj.org-article:f68e2c70b9b74d65a7ff8235ec9e72662021-12-03T11:50:15ZData-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile10.5194/nhess-21-3663-20211561-86331684-9981https://doaj.org/article/f68e2c70b9b74d65a7ff8235ec9e72662021-12-01T00:00:00Zhttps://nhess.copernicus.org/articles/21/3663/2021/nhess-21-3663-2021.pdfhttps://doaj.org/toc/1561-8633https://doaj.org/toc/1684-9981<p>Wildfire risk is latent in Chilean metropolitan areas characterized by the strong presence of wildland–urban interfaces (WUIs). The Concepción metropolitan area (CMA) constitutes one of the most representative samples of that dynamic. The wildfire risk in the CMA was addressed by establishing a model of five categories (near zero, low, moderate, high, and very high) that represent discernible thresholds in fire occurrence, using geospatial data and satellite images describing anthropic–biophysical factors that trigger fires. Those were used to deliver a model of fire hazard using machine learning algorithms, including principal component analysis and Kohonen self-organizing maps in two experimental scenarios: only native forest and only forestry plantation. The model was validated using fire hotspots obtained from the forestry government organization. The results indicated that 12.3 % of the CMA's surface area has a high and very high risk of a forest fire, 29.4 % has a moderate risk, and 58.3 % has a low and very low risk. Lastly, the observed main drivers that have deepened this risk were discussed: first, the evident proximity between the increasing urban areas with exotic forestry plantations and, second, climate change that threatens triggering more severe and large wildfires because of human activities.</p>E. Jaque CastilloA. FernándezR. Fuentes RoblesC. G. OjedaCopernicus PublicationsarticleEnvironmental technology. Sanitary engineeringTD1-1066Geography. Anthropology. RecreationGEnvironmental sciencesGE1-350GeologyQE1-996.5ENNatural Hazards and Earth System Sciences, Vol 21, Pp 3663-3678 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Geology QE1-996.5
spellingShingle	Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Geology QE1-996.5 E. Jaque Castillo A. Fernández R. Fuentes Robles C. G. Ojeda Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile
description	<p>Wildfire risk is latent in Chilean metropolitan areas characterized by the strong presence of wildland–urban interfaces (WUIs). The Concepción metropolitan area (CMA) constitutes one of the most representative samples of that dynamic. The wildfire risk in the CMA was addressed by establishing a model of five categories (near zero, low, moderate, high, and very high) that represent discernible thresholds in fire occurrence, using geospatial data and satellite images describing anthropic–biophysical factors that trigger fires. Those were used to deliver a model of fire hazard using machine learning algorithms, including principal component analysis and Kohonen self-organizing maps in two experimental scenarios: only native forest and only forestry plantation. The model was validated using fire hotspots obtained from the forestry government organization. The results indicated that 12.3 % of the CMA's surface area has a high and very high risk of a forest fire, 29.4 % has a moderate risk, and 58.3 % has a low and very low risk. Lastly, the observed main drivers that have deepened this risk were discussed: first, the evident proximity between the increasing urban areas with exotic forestry plantations and, second, climate change that threatens triggering more severe and large wildfires because of human activities.</p>
format	article
author	E. Jaque Castillo A. Fernández R. Fuentes Robles C. G. Ojeda
author_facet	E. Jaque Castillo A. Fernández R. Fuentes Robles C. G. Ojeda
author_sort	E. Jaque Castillo
title	Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile
title_short	Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile
title_full	Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile
title_fullStr	Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile
title_full_unstemmed	Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile
title_sort	data-based wildfire risk model for mediterranean ecosystems – case study of the concepción metropolitan area in central chile
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/f68e2c70b9b74d65a7ff8235ec9e7266
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Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile

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