The identification and assessment of areas at risk of forest fire using fuzzy methodology

Soto, Miguel Castillo

doi:10.1016/j.apgeog.2012.07.001

Cited by 43 publications

(17 citation statements)

References 28 publications

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“…The trigger is a local condition that causes a risk to occur if the area is susceptible to that risk, while the susceptibility is assessed based on predisposing factors, stable over time. The proximity to road and pathway networks and to urban and recreation areas are most frequently mentioned as predisposing human factors for wildfire [37][38][39][40][41][42][43][44]. As regards geo-environmental variables, those related to vegetation type and topography are the most significant drivers, especially in Mediterranean-type regions [45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

et al. 2020

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Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

et al. 2020

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“…This was particularly evident for wine production because the occurrence of fire in this area was largely explained by human-induced variables, with a high probability of fire affecting areas close to the city and roads where suitable areas for wine production are located. These results were consistent with other studies modeling the occurrence of fire in Central Chile (Castillo 2012, Altamirano et al 2013) and highlight the importance of fire as a major driver of change in the spatial patterns and overall supply of the selected ecosystem services.…”

Section: Discussionsupporting

confidence: 81%

“…The projected effects of climate change along with the increasing human population density and associated expansion of the road network is predicted to lead to an increase the prevalence of fires in the study region. These interactions were included in the ecosystem service assessments (Castillo 2012, Altamirano et al 2013). scenarios were implemented with and without fire probability.…”

Section: The Scenario Storylinesmentioning

confidence: 99%

Planning for the conservation and management of ecosystem services in Central Chile

Martínez‐Harms¹

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The ecosystem services concept emphasizes the benefits societies obtain from ecosystems.These benefits include the production of food and clean water, the regulation of floods, provision for recreation and scenic beauty, a connection to place, and inspiration. Ecosystem services have become popular within both scientific and policy circles as a means to document the value humans place on ecosystems, evaluating these benefits from nature and using the resulting knowledge to inform landuse management decisions. The integration of ecosystem services into decision-making, nevertheless, remains challenging. The research that comprises this thesis provides a comprehensive framework for strengthening the dialogue between ecosystem services assessment and decision-making. My case studies focus on critical challenges in the translation and transformation of ecosystem services as a concept, to a component that improves environmental decision-making.Numerous assessments have quantified, mapped, and valued ecosystem services. However, much of the literature fails to clarify how the information gathered in such assessments can be used to inform decisions that will affect ecosystem services. I propose a framework for making decisions for ecosystem services adapted from decision science and synthesize the degree to which the peerreviewed ecosystem services literature has captured these steps. I find that the ecosystem services literature has not covered all the steps of a formal decision-making process. For the ecosystem service paradigm to gain traction in science and policy arenas, future ecosystem service assessments should have clearly articulated objectives and meet user-related measures for ecosystem services, seek to evaluate the consequences of scenario alternatives, and facilitate closer engagement between scientists and stakeholders.There is a paucity of studies of ecosystem services assessments under global change, particularly in the developing countries of Latin America. I demonstrate a rapid assessment of the impacts of global change on key ecosystem services in the data-sparse region of Central Chile. This approach translates expert-derived qualitative scenario storylines into quantitative spatial predictions of the combined effects of climate change, urbanization and fire events on the future provision of carbon storage, wine production, and scenic beauty for the year 2050. I find that the cumulative impacts of climate change and urbanization are likely to place considerable pressure on ecosystem services in Central Chile by mid-century revealing the need for stronger planning regulations to manage land-use change.In environmental decision-making, there is increasing attention towards equity of access to ecosystem services, and in conservation implementation. In central Chile, distribution of ecosystem 2 services must be considered within the context of substantial economic and social inequity. I develop a user-related measure to assess the accessibility of the population to the protected area network. ...

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“…Causas atribuibles a esta situación pueden estar relacionadas con la falta de información sufi ciente y confi able . En Chile, recientemente algunos estudios han evaluado áreas de riesgo, propagación y daño potencial de incendios forestales (Muñoz et al, 2007;Castillo, 2012;Castillo et al, 2012), además del monitoreo de combustibles (INFOR, 2008). También fue desarrollado un sistema para predecir la ocurrencia y propagación de incendios forestales (Castillo, 1997).…”

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Influencia de la heterogeneidad del paisaje en la ocurrencia de incendios forestales en Chile Central

Altamirano

Salas

Yaitul

et al. 2013

Rev. geogr. Norte Gd.

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RESUMENA pesar de la reconocida importancia de los incendios forestales, la investigación sobre modelación de ocurrencia de incendios es escasa en el Hemisferio Sur, y en particular en Chile. Se estudió el efecto de la heterogeneidad del paisaje en la ocurrencia de incendios forestales en Chile Central. Se ajustó un modelo que incluye variables climáticas, topográfi cas, de actividad humana y cobertura del suelo. La probabilidad de ocurrencia aumentó con una mayor temperatura mínima y estacionalidad de la precipitación, y disminuyó con una mayor distancia a las ciudades. El modelo clasifi có correctamente un 65% de los datos de validación. Las predicciones sugieren que un 38% del área de estudio tiene muy alta probabilidad de ocurrencia de incendios, la cual aumenta bajo un simulado escenario futuro. El modelo predictivo debería ser utilizado como una herramienta para orientar aspectos urgentes de protección y prevención, lo cual debe continuar siendo discutido y evaluado.Palabras clave: Clima, disturbios antrópicos, patrones de paisaje, regresión logísti-ca, sensores remotos. ABSTRACTForest fi res are recognized as a serious problem. Despite its importance, research into modelling of forest fi re occurrence is lacking for the southern hemisphere, in particular for Chile. We investigated how landscape heterogeneity affects the probability of the occurrence of forest fi res in Central Chile. We fi tted a logistic regression model which included climatic, topographic, human-related and land-cover variables. Estimated probabilities of forest fi re occurrence increased positively

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The identification and assessment of areas at risk of forest fire using fuzzy methodology

Cited by 43 publications

References 28 publications

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

Planning for the conservation and management of ecosystem services in Central Chile

Influencia de la heterogeneidad del paisaje en la ocurrencia de incendios forestales en Chile Central

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