Roadside vegetation planning and conservation: New approach to prevent and mitigate wildfires based on fire ignition potential

Molina, Juan Ramón; Lora, Á.; Prades, Cristina; Silva, Francisco Rodríguez y

doi:10.1016/j.foreco.2019.04.034

Cited by 36 publications

(19 citation statements)

References 42 publications

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“…For regions of high population density, anthropogenic or structural factors are often dominant for fire susceptibility. For example, Molina et al (2019) used MaxEnt to show that distance to roads, settlements, or powerlines were the dominant factors for fire occurrence probability in the Andalusia region in southern Spain. MaxEnt has also been used for estimating spatial fire probability under different scenarios such as future projections of housing development and private land conservation (Syphard et al 2016).…”

Section: Landscape Controls On Firementioning

confidence: 99%

A review of machine learning applications in wildfire science and management

et al. 2020

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Artiﬁcial intelligence has been applied in wildﬁre science and management since the 1990s, with early applications including neural networks and expert systems. Since then the ﬁeld has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildﬁre science and management. Our overall objective is to improve awareness of ML methods among wildﬁre researchers and managers, as well as illustrate the diverse and challenging range of problems in wildﬁre science available to ML data scientists. To that end, we ﬁrst present an overview of popular ML approaches used in wildﬁre science to date, and then review the use of ML in wildﬁre science as broadly categorized into six problem domains, including: 1) fuels characterization, ﬁre detection, and mapping; 2) ﬁre weather and climate change; 3) ﬁre occurrence, susceptibility, and risk; 4) ﬁre behavior prediction; 5) ﬁre eﬀects; and 6) ﬁre management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildﬁres within a data science context. In total, we identfied 300 relevant publications up to the end of 2019, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artiﬁcial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildﬁre sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML methods to learn on their own, expertise in wildﬁre science is necessary to ensure realistic modelling of ﬁre processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildﬁre research and management communities play an active role in providing relevant, high quality, and freely available wildﬁre data for use by practitioners of ML methods.

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Section: Landscape Controls On Firementioning

confidence: 99%

A review of machine learning applications in wildfire science and management

et al. 2020

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“…However, not only carbon is interesting for forest modelers, and other regional issues such as fire spread are also important drivers for modelling. In this sense, Cardil et al (2019b) have developed models to estimate the effects of fire suppression in Quebec (eastern Canada), Clements et al (2019) have studied fire spread rates in Texas (SW USA), Ruffaut and Mouillot (2017) in SE France and Molina et al (2019) in southern Spain. Another issue that has attracted the attention of forest modelers working at regional to continental scales has been regeneration processes.…”

Section: Bringing Down the Barriers Between Scales: Still A Challenge?mentioning

confidence: 99%

Modelling Forest Ecosystems: a crossroad between scales, techniques and applications

Blanco

Améztegui

Rodríguez

2020

Ecological Modelling

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into different modelling approaches to try to understand forest systems, from continental, regional or landscape-scales to individual-base models. Similarly, forest models have been developed at all kind of complexity levels, from very complex, integrative models to simpler models focused on one specific ecophysiological process. Thus, continuous efforts by the forest modelling community have provided a wealth of research and expertise, but at the same time have generated a dispersion and lack of linkages between different modelling approaches and applications.Forest science is integrative by nature: understanding climate, soil and water processes is as important as understanding plant eco-physiology to understand how forests work. Therefore, forest science has evolved during the last centuries to understand how trees and forests grow, and which patterns and processes rule their development through time and space. Following the common scientific method, empirical reductionist approaches tried to isolate and study one ecological factor or variable at a time (Popper 1968), trying to reduce the complexity inherent to ecological processes. On the contrary, theoretical ecological models explore the ecological interactions among the components that exist in a forest, assembling the pieces of evidence generated through traditional field and lab work (Kimmins et al. 2010). In this special issue, we want to show some of the latest developments in forest modelling, understanding forests like working systems in which human intervention has played and is still playing a historical role, and will likely do so in the future.Following the sharp increase in data availability, modelling is now being applied in forest ecosystems at multiple scales, from continental and regional studies on forests and species distribution to leaf scales modelling plague and pathogen infestation in individual branches. Similarly, models are being applied to understand short-time processes such as nutrient transport in cells, to extremely long scales such as genetic drift in tree populations. From the application point of view, models are also being applied from purely ecological issues, such as coexistence and resource competition among tree species, to multidisciplinary socio-economic-ecological issues, such as design of tourist routes inside natural reserves, estimating timber flow to sawmills under climate change scenarios, or engineering concerns such as designing forest harvesting machinery with lower ecological footprint (in energy, rut formation, noise, etc.).When organizing this special issue, we (the guest editors) decided to carry out a quick overview of current trends in forest modelling. To do so, we used Clarivate's Web of Science to carry out a search in the top five journals with "Forestry" as their unique category

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“…No presente estudo também foi utilizado o teste de pseudo-significância gerando 999 permutações dos valores de atributos associados e o nível de significância de 95% (Hu et al, 2019). (Molina et al, 2019). Dessa forma, a próximidade com essas vias aumenta consideravelmente o risco de ocorrência de incêndios florestais.…”

Section: Lisa=unclassified

Zoneamento de incêndios florestais na estação ecológica Águas Emendadas, Distrito Federal (Brasil)

Camelo

Sanches

Nakagomi

2020

TER

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O aumento no número de ocorrências de incêndios florestais no bioma Cerrado é considerado uma ameaça à sua conservação. Dessa forma, as áreas protegidas por meio das Unidades de Conservação (UC) são fundamentais para a proteção dos biomas brasileiros. No entanto, mesmo essas áreas são acometidas por incêndios que comprometem suas integridades. Neste sentido, este estudo teve como objetivo elaborar o zoneamento do risco de incêndio florestal da UC Estação Ecológica Águas Emendadas por meio do método multicritério aditivo, validá-lo com as cicatrizes dos incêndios ocorridos entre os anos de 2011 a 2015 e avaliar a espacialidade do risco a incêndios florestais. Foi observado que nenhuma área da UC estudada foi classificada como zona de risco Extremo, no entanto mais de 60% da UC foi classificada em zonas de Alto ou Muito Alto risco a incêndios. A validação mostrou que 80% dos incêndios ocorreram sobre áreas com risco mediano a incêndios florestais como formações savânicas ou campestres. Foi obtido um Índice Global de Moran para o risco de incêndios florestais no valor de 0.83 indicando uma elevada autorrelação espacial, além disso, o Índice Local de Moran identificou como regiões mais críticas as áreas de interface de vegetação nativa e áreas urbanas.

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Roadside vegetation planning and conservation: New approach to prevent and mitigate wildfires based on fire ignition potential

Cited by 36 publications

References 42 publications

A review of machine learning applications in wildfire science and management

A review of machine learning applications in wildfire science and management

Modelling Forest Ecosystems: a crossroad between scales, techniques and applications

Zoneamento de incêndios florestais na estação ecológica Águas Emendadas, Distrito Federal (Brasil)

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