Spatial interactions and optimal forest management on a fire-threatened landscape

Lauer, Christopher J.; Montgomery, Claire A.; Dietterich, Thomas G.

doi:10.1016/j.forpol.2017.07.006

Cited by 25 publications

(18 citation statements)

References 17 publications

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“…The knowledge of the values of size-density where the stands reach and follow the self-thinning line bridges the gap in the empiricism with which these forest systems have been usually managed. Management practices should adapt and spatially diversify the structure of the forests to make them less prone to the risk of wildfires [41]. At the forest scale, typical forest management practices, such as the management of stand density [42], through thinning and harvesting systems, can contribute to reducing wildfire risk through the decrease of fuel loads [43].…”

Section: Size-density Trajectory and Self-thinning Linementioning

confidence: 99%

Size–Density Trajectory in Regenerated Maritime Pine Stands after Fire

Enes

Lousada

Aranha

et al. 2019

Forests

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Research Highlights: This study bridges a gap of knowledge about the maximum size-density trajectory for juvenile stands of maritime pine. The continuity of the trajectory along the development stages to maturity is assured with a straightforward approach providing support to determine optimum density along all the revolution periods for the species. Background and Objectives: Forest fire is a significant threat to forests in the Mediterranean regions, but also a natural disturbance that plays a vital role in the perpetuation of forest stands. In recent decades, there has been an increase of burnt area in maritime forests in Portugal, followed by an increased interest in managing the natural and usually abundant regeneration occurring after the fires. The gap in the knowledge of growth dynamics for juvenile stages, for these forest systems, currently constrains their correct management, for forest planning, particularly in determining the optimal densities. The study aims to identify the maximum attainable density trajectory at the early stages of development of the species that could support a non-empirical definition of silvicultural prescriptions and thinning decisions, along the revolution. Materials and Methods: A representative data set collected in stands regenerated after fire supports the analysis of the maximum size-density trajectory for the species. Results: The maximum size-density trajectory for the juvenile stands deviates from the expected trajectory defined in the self-thinning line published for the species. Significant deviation occurs at the lower end of the line, indicating the need for a reevaluation of the existing self-thinning line. We propose a new self-thinning model for the species that explicitly considers the behavior of size-density for juvenile stands. The new model assures a logical continuity for the trajectory from the young stages of development to maturity. Conclusions: The proposed model based on the maximum attainable size-density trajectory provides ecological-based support to define silvicultural guidelines for management of the species.

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Section: Size-density Trajectory and Self-thinning Linementioning

confidence: 99%

Size–Density Trajectory in Regenerated Maritime Pine Stands after Fire

Enes

Lousada

Aranha

et al. 2019

Forests

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“…In our analysis, we did not account for wildfires' ability to spread between stands (Lauer, Montgomery, & Dietterich, 2017). As such, a key issue in determining the optimal harvest decision would be to extend our adaptive harvest strategy by incorporating neighboring stand effects regarding the spatial interaction of wildfires.…”

Section: Discussionmentioning

confidence: 99%

Optimal harvest strategy for even‐aged stands with price uncertainty and risk of natural disturbances

Susaeta

Gong

2019

Natural Resource Modeling

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We present a reservation price model to examine the joint impacts of natural disturbances and stumpage price uncertainty on the optimal harvesting decision for even‐aged forest stands. We consider a landowner who manages a loblolly pine stand to produce timber and amenities, under age‐dependent risk of wildfires and uncertainty in future timber prices. We show that the incorporation of risk of wildfires decreases the optimal reservation prices. The inclusion of risk of wildfires leads to lower land values and reduces the mean harvest age compared with the case of no‐risk of wildfires. Higher economic gains are obtained with the reservation price strategy compared with the deterministic rotation age model—a difference in the land value of $2,326 ha−1 (21%) between the two approaches. Recommendations for Resource Managers Our adaptive harvest strategy shows that the incorporation of risk of wildfires decreases the optimal reservation prices compared with the case of no‐risk of wildfires. Low reservation prices—a price that makes the landowner indifferent between harvesting or waiting longer—result in lower economic benefits for landowners and potential conversions of lands to nonforest use. Forest management practices oriented to reduce the effects of catastrophic disturbances, for example, creating a more complex forest structure with different stand densities, become imperative to ensure the sustainability of forestlands in the US South. Our analysis also suggests that the valuation of forestry investments should consider not only the risk of catastrophic events but also uncertainty in future timber prices. Higher appraisals of land value are obtained when timber price uncertainty is explicitly recognized, providing financial incentives for landowners to invest in forestry.

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“…For example, Penman et al (2014) used a BN to examine the relative risk reduction of using prescribed burns on the landscape versus within the 500 m interface zone adjacent to houses in the Sydney Basin, Australia. Lauer et al (2017) used approximate dynamic programming (also known as RL) to determine the optimal timing and location of fuel treatments and timber harvest for a fire-threatened landscape in Oregon, US, with the objective of maximizing wealth through timber management. Similarly, Arca et al (2015) used GA for multi-objective optimization of fuel treatments.…”

Section: Fuel Treatmentmentioning

confidence: 99%

“…From our review, a few papers used agent-based learning methods for fire management. In particular, RL was used for optimizing fuel treatments (Lauer et al 2017) or for autonomous control of aircraft for fire monitoring (Julian and Kochenderfer 2018). GAs were used for generating optimal fire lines for active fires (Homchaudhuri et al 2010) and for reducing the time for fire simulation (Cencerrado et al 2014).…”

Section: Implications For Fire Managementmentioning

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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Spatial interactions and optimal forest management on a fire-threatened landscape

Cited by 25 publications

References 17 publications

Size–Density Trajectory in Regenerated Maritime Pine Stands after Fire

Size–Density Trajectory in Regenerated Maritime Pine Stands after Fire

Optimal harvest strategy for even‐aged stands with price uncertainty and risk of natural disturbances

A review of machine learning applications in wildfire science and management

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