Wildfire Risk Research Articles

Integrated fire management in a tropical biosphere reserve: Achievements and challenges

Wildfires are among the most critical threats to tropical rainforests. In the Selva El Ocote Biosphere Reserve in the Mexican state of Chiapas, vast rainforest areas suffered destruction by fires in 1998 and 2003. However, there have been no more widespread fires, and previously burnt areas have been recovering. The present study analyses the integrated fire management processes implemented in the reserve. It includes 19 expert interviews and a survey applied to 274 inhabitants of local communities. It argues that the successful prevention of further adverse wildfire events is related to a fire management approach, which emphasizes cultural and physical fire prevention and incorporates strong participation by local communities. The communities have established internal agreements to mitigate wildfire risk. Thus, the reserve might serve as an example of how integrated and cultural fire management can be applied in practice. However, these achievements face challenges, mainly due to budget cuts, changing governmental priorities, and the crisis of the rule of law in Mexico. An event that particularly impacted fire management practices in the area in recent years was the implementation of the federal government's Sembrando Vida (“Planting Life”) program. First, it led to deforestation and the provocation of wildfires but afterwards it seems to have induced the abandonment of agricultural burning by a considerable percentage of local farmers.

Estimating fine fuel loads in Eucalypt forests using forest inventory data and a modelling approach

Eucalypt forests are vital for biodiversity and fire dynamics, with fine fuels serving as the primary ignition source and critical factors in determining fire intensity. Accurate measurement of fine fuel via destructive field methods is location and time-restricted, hindering the tracking of their dynamic variations across diverse landscapes. In response, this study utilised forest inventory data collected by the Victorian Forest Monitoring Program (VFMP) in Victoria, southeastern Australia, along with allometric models, to estimate fine fuel loads across four vertical layers (surface, near-surface, elevated, and canopy). The average total fine fuel load in Victorian Eucalypt forests was estimated at 18.45 t ha−1 (ranging from 2.23 to 39.54 t ha−1), with surface litter contributing 58.2% of this total, followed by canopy (22.1%), near-surface (13.4%), and elevated (6.6%) fine fuels. We also conducted a comprehensive investigation and found distinct variations in fine fuel loads across different bioregions, forest covers, and vegetation types. Furthermore, stepwise multiple regression was employed to develop predictive models for estimating fine fuel loads from forest structural and environmental variables. Results show that total and canopy fine fuels can be reasonably explained by forest height, density, and climate-related variables (with R2 = 0.38 and 0.51, respectively). However, the explanatory power of these models diminished when applied to elevated, near-surface, and surface fine fuels. This study underscores the intricacies of fine fuel distribution in Eucalypt forests, emphasising the necessity for comprehensive factors in fire management planning. Further research is needed to better comprehend the relationship between forest characteristics and fine fuel dynamics to enhance wildfire risk assessment and mitigation strategies.

Prescribed fire placement matters more than increasing frequency and extent in a simulated Pacific Northwest landscape

AbstractPrescribed fire has been increasingly promoted to reduce wildfire risk and restore fire‐adapted ecosystems. Yet, the complexities of forest ecosystem dynamics in response to disturbances, climate change, and drought stress, combined with myriad social and policy barriers, have inhibited widespread implementation. Using the forest succession model LANDIS‐II, we investigated the likely impacts of increasing prescribed fire frequency and extent on wildfire severity and forest carbon storage at local and landscape scales. Specifically, we ask how much prescribed fire is required to maintain carbon storage and reduce the severity and extent of wildfires under divergent climate change scenarios? We simulated four prescribed fire scenarios (no prescribed fire, business‐as‐usual, moderate increase, and large increase) in the Siskiyou Mountains of northwest California and southwest Oregon. At the local site scale, prescribed fires lowered the severity of projected wildfires and maintained approximately the same level of ecosystem carbon storage when reapplied at a ~15‐year return interval for 50‐year simulations. Increased frequency and extent of prescribed fire decreased the likelihood of aboveground carbon combustion during wildfire events. However, at the landscape scale, prescribed fire did not decrease the projected severity and extent of wildfire, even when large increases (up to 10× the current levels) of prescribed fire were simulated. Prescribed fire was most effective at reducing wildfire severity under a climate change scenario with increased temperature and precipitation and on sites with north‐facing aspects and slopes greater than 30°. Our findings suggest that placement matters more than frequency and extent to estimate the effects of prescribed fire, and that prescribed fire alone would not be sufficient to reduce the risk of wildfire and promote carbon sequestration at regional scales in the Siskiyou Mountains. To improve feasibility, we propose targeting areas of high concern or value to decrease the risk of high‐severity fire and contribute to meeting climate mitigation and adaptation goals. Our results support strategic and targeted landscape prioritization of fire treatments to reduce wildfire severity and increase the pace and scale of forest restoration in areas of social and ecological importance, highlighting the challenges of using prescribed fire to lower wildfire risk.

Coupling fire and energy in the Anthropocene: Deploying scale to analyze social vulnerability to forced electricity outages in California

Extreme events such as wildfires and winter storms result in disruptions to grid-based electricity delivery. Electricity supply disruptions are both reactive, whereby specific events cause damage to physical infrastructure, or anticipatory where electricity suppliers—namely electric utility companies—preemptively de-energize sections of an electrical grid or distribution network based on elevated potential of extreme conditions that may cause wildfire ignition. De-energization has been promoted as a strategy to mitigate risk of wildfire ignition and spread when active fires may encounter distribution/transmission lines. Provision of basic energy services such as electricity are necessary for maintaining a range of essential functions such as communication, which become critical during extreme events. In recent years, Public Safety Power Shutoffs (PSPS) have increasingly been deployed by utility companies in Western U.S states as wildfire risk increases due to combined impacts of anthropogenic climate change, fuel accumulation, and expansion of development in fire prone lands. While the PSPS policy was designed to reduce liability of utilities in igniting fires, there is a dearth of research critically analyzing how the policy affects social vulnerability for populations subjected to periods of de-energization during high-risk fire conditions. This article aims to deepen current understandings of the way scale can be deployed to illustrate the highly spatial nature of relationships coupling electricity supply outages with demographic data to advance limited knowledge on social vulnerability characteristics for specific communities subjected to PSPS. The research engages scale to compare social vulnerability to outages experienced both in Butte County, located in northern California and the state as a whole.

Assessing policy preferences for preventing and managing wildfire in Greece

Extreme temperatures and droughts have led to catastrophic wildfires across Greece over the last two decades, with 2023 experiencing the largest fire-driven evacuation in the country's history. Existing wildfire governance approaches may be outdated in the face of these new challenges. Greek residents' attitudes and willingness to pay for additional prevention and mitigation may be changing with the increased threat. Because policy design can affect public support and subsequent outcomes, we conducted an online national-level survey of Greek residents (n = 722) with an embedded discrete choice experiment, aimed at assessing preferences and willingness-to-pay for different policy alternatives to address wildfire risk. Our results suggest that, in general, Greek residents are unsatisfied with current fire policy, are willing to pay a small amount for additional fire mitigation efforts aimed to reduce burned areas, and prefer action led by the Greek Forest or Fire Services using awareness and education campaign—for example, fire prevention informative mailers or workshops.

Spatial and temporal monitoring of wildfires in Golestan province using remote sensing data

Wildfires are one of the most significant factors of ecosystem change. Knowing the wildfire regime (frequency, intensity, and distribution pattern) is essential in wildfire management. This research aims to analyze the spatiotemporal pattern of wildfires in Golestan in 2001-2021 using MODIS data, burned area product (MCD64A1). For this purpose, the annual and monthly frequency, as well as the trend of wildfires based on types of forest, pasture, and crop cover, were statistically analyzed. The local Moran pattern analysis method and kernel density function were used to analyze the spatial dynamics of wildfire. The results showed that 18,462 wildfires occurred in Golestan, the highest of which was in 2010, with 2,517 wildfires (13.8%). The lowest number of wildfires, with only 57 events (0.5%), was in 2001. Based on the local Moran model results and the kernel density function, the wildfires' extent and intensity were greater in the plains and foothills to the south and southeast of Golestan. The lowest extent and intensity of the wildfire corresponded to the eastern parts of the province. The frequency of wildfires was higher in the hot period of the year (spring and summer). However, the period of occurrence of wildfire and the peak of wildfire changes in different uses. The wildfire zones in June were wider and more intense than in other months. The frequency and spatial extent of wildfires in agricultural lands from May to July, pasture lands in July, August, and September, and forest lands in November and December were more than in other months. Weather conditions play a significant role in the occurrence of wildfire in the forest lands of Golestan. The results of this research help understand wildfire risk areas and provide a scientific basis for predicting and controlling wildfires and reducing carbon emissions related to them.

The complex composition of organic aerosols emitted during burning varies between Arctic and boreal peat

AbstractPeatlands in the northern hemisphere are a major carbon storage but face an increased risk of wildfires due to climate change leading to large-scale smoldering fires in boreal and Arctic peatlands. Smoldering fires release organic carbon rich particulate matter, which influences the earth’s radiative balance and can cause adverse health effects for humans. Here we characterize the molecular composition of biomass burning particulate matter generated by laboratory burning experiments of peat by electrospray ionization 21 T Fourier-transform ion cyclotron resonance mass spectrometry, revealing a highly complex mixture of aromatic and aliphatic organic compounds with abundant heteroatoms including oxygen, sulfur and up to five nitrogen atoms. Primary organosulfur species are identified in the emissions of peat-smoldering, in part also containing nitrogen. Differences are observed when comparing structural motifs as well as the chemical composition of boreal and Arctic peat burning emissions, with the latter containing compounds with more nitrogen and sulfur.

Decolonising Fire Science by Reexamining Fire Management across Contested Landscapes: A Workshop Approach

In many landscapes worldwide, fire regimes and human–fire interactions were reorganised by colonialism and continue to be shaped by neo-colonial processes. The introduction of fire suppression policies and state-centric property-rights systems across conservation areas and the intentional erasure of Indigenous governance systems and knowledge have served to decouple Indigenous fire-dependent communities from culturally mediated fire regimes and fire-adapted landscapes. This has driven a decline in anthropogenic fires while simultaneously increasing wildfire risk where Indigenous people have been excluded, resulting in widespread social–ecological vulnerabilities. Much contemporary fire research also bears colonial legacies in its epistemological traditions, in the global geographical distribution of research institutions, and the accessibility of research outputs. We report on a two-day workshop titled ‘Fire Management Across Contested Landscapes’ convened concurrently in Nairobi, Kenya, and London, UK. The workshop formed part of a series of workshops on ‘Decolonising Fire Science’ held by the Leverhulme Centre for Wildfires, Environment and Society, UK. The workshop in Nairobi invited diverse Kenyan stakeholders to engage in participatory activities that facilitate knowledge sharing, aiming to establish an inclusive working fire network. Activities included rich pictures, world café discussions, participatory art, and the co-development of a declaration to guide fire management in Kenya. Meanwhile, in London, Leverhulme Wildfires researchers explored participatory research methodologies including rich pictures and participatory video, and developed a declaration to guide more equitable research. There were opportunities throughout the workshop for participants in Nairobi and London to engage in dialogue with one another, sharing their experiences and understandings of complex fire challenges in Kenya and globally.

Blanket bog vegetation response to wildfire and drainage suggests resilience to low severity, infrequent burning

BackgroundIn 2019, a wildfire impacted an area of blanket bog and wet heath > 60 km2 in the Flow Country peatlands of northern Scotland, a site of global significance. Unusually the footprint of the wildfire included discrete areas of degraded, restored, and near-natural blanket bogs. Following the wildfire, we surveyed vegetation in 387 quadrats in burnt and unburnt areas. The study aimed to determine whether and how proximity to human-made drains and microtopography affected fire-vegetation interactions and included older wildfire sites and unburnt control sites for context.ResultsOverall, our study suggests that the 2019 Flow Country wildfire caused mostly superficial burning; except in the most degraded area, which burned more severely and where we recorded more profound impacts on the vegetation. We found higher cover of litter, which in turn led to increased localized fire damage in quadrats close to drains compared with quadrats away from the influence of drains. We also found greater fire impacts (e.g., proportions of moss burnt and Sphagnum discoloration) on hummocks, particularly where they were higher relative to the hollows. Overall, vegetation both near and away from drains largely resembled nearby unburnt sites within 20 years.ConclusionsOverall, our study suggests that the 2019 Flow Country wildfire caused mostly superficial burning, except in the most degraded areas. Vegetation communities of blanket bogs associated with conservation and restoration areas in the region appear to be largely resilient to occasional, low severity wildfires. This implies that management interventions that maintain wet conditions in peatlands have the potential to help reduce the risks of severe wildfires.

Assessing silvopasture management as a strategy to reduce fuel loads and mitigate wildfire risk

Managing private forests for wildfire resilience is challenging due to conflicting social, economic, and ecological decisions that may result in an increase of surface fuel loads leading to greater fire risk. Due to fire suppression and a changing climate, land managers in fire-prone regions face an increasing threat of high severity fires. Thus, land managers need fuel treatment options that match their forest types and management objectives. One potential option for producers that graze livestock is silvopasture management, where livestock, forages, and overstory vegetation are carefully managed for co-benefits on the same unit of land. This study compared forest composition and structure, fuel types, and vegetative biomass between silvopasture and non-grazed managed forests in Washington, U.S. We show that silvopasture management results in reductions in grass biomass, litter, and duff depth when compared to non-grazed managed forest. These findings point to the integrated nature of silvopasture, where management of overstory composition and structure, understory vegetation, and grazing can reduce fuel loads and potential wildfire risk.

Wildfire risk assessment using deep learning in Guangdong Province, China

The severe wildfires that have ravaged Guangdong province, China, present a significant threat to the local ecosystem, socio-economics, and public health. Effective risk assessment is essential for early warning and timely prevention in wildfire management, thereby mitigating disaster losses. In this study, we compiled a dataset comprising 11,507 historical wildfire incidents in Guangdong Province spanning a decade (2011–2021) and developed a deep learning-based model to predict the likelihood of wildfire occurrence in the region. In addition to analyzing risk characteristics throughout the year, we also trained separate models for different seasons and analyzed the discrepancies in the contribution of driven factors to wildfire occurrence across seasons. Furthermore, the performance of our deep learning-based model was compared with that of traditional machine learning algorithms. The experimental results revealed that: (1) Factors such as relative humidity, temperature, NDVI, and precipitation exerted significant influence on wildfire occurrence. (2) The impact of wildfire driving factors varied across different seasons. (3) Our deep learning model outperformed traditional machine learning models, achieving a superior performance with an AUC of 0.962.

Wildfire risk assessment and mapping – an approach for Natura 2000 forest sites

Over the last ten years, Bulgaria's forest areas have been under almost year-round fire activity, severely impacting the Natura 2000 sites: Managing forests and maintaining traditional links between people and nature in mountain sites is becoming increasingly challenging. However, the National Methodology for determining the risk of forest fires (proposed in 2016) is applicable only at the regional administrative level (NUTS 3). This study is motivated by the actual need for an approach and a toolbox that can accurately assess the risk of wildfires, considering the unique characteristics of each location. An object-oriented vulnerability analysis is necessary to ensure that all parties involved in wildfire risk management can respond effectively. The main objective of this study is to suggest a flexible and scalable approach for spatial assessment and mapping of wildfire risk in geographic units relevant to natural heritage maintenance and resource management. A single register of potential fire risk factors is proposed, integrating 29 indicators into five thematic groups reflecting up-to-date data on hazards, vulnerability, and emergency response capacity. The factors are assessed and overlayed to come out with an integrated evaluation of the fire risk within a forestry subdivision - the smallest unit of forest management and inventory. The evaluation process involved geospatial technologies for analyzing the data and mapping the results. The methodology is tested on two Natura 2000 sites - BG0000496 “Rilski Manastir” (includes UNESCO’ World Heritage Site and symbol of Bulgarian identity – Rila Monastery) and BG0001011 “Osogovska Planina”, for which a conservation concern has been identified under the influence of wildfire risk. It has been confirmed that risk maps have the potential to support response teams, as demonstrated during a fire event that occurred on October 30th, 2022. The results from the study were used to identify suitable locations for the construction of fire towers and a visitor information system at BG0000496 “Rilski Manastir”.

Yearly population data at census tract level revealed that more people are now living in highly fire-prone zones in California, USA

In California (CA), the wildland-urban interface (WUI) faces escalating challenges due to surging population and real estate development. This study evaluates communities along CA’s WUI that have witnessed substantial population growth from 2010 to 2021, utilizing demographic data and the 2020 WUI boundaries by the University of Wisconsin-Madison SILVIS Lab. Employing the Mann-Kendall test, we analyze yearly population trends for each census tract along the CA WUI and assess their significance. House ownership, affordability, and wildfire risk are examined as potential drivers of this demographic shift. Our findings indicate that 12.7% of CA’s total population now resides in census tracts with significant population increases over the past decade, labeled as ‘high-growth tracts.’ The Bay Area and Southern California, encompassing 76% of all high-growth tracts in CA, witnessed the most substantial population increase along the WUI. Notably, Riverside County stands out with 29.2% of its residents (approximately 717,000 residents) located in high-growth tracts, exemplifying a significant population surge within CA’s WUI. Our analysis identifies a significant relationship between population increase in the WUI, house ownership, and affordability, where lower-priced homes come at the expense of heightened wildfire risk. However, the impact of house affordability on population growth within the WUI varies by region, playing a more prominent role in explaining population proportions in Southern California’s WUI, while in the universally low-affordability Bay Area, other motivations may drive residents to live within the WUI. Given the rapid growth and insufficient consideration of wildfire risk in the WUI, policymakers must take prompt action, ensuring adequate infrastructure and resources as more individuals relocate to areas with heightened wildfire risk.

Deep learning modeling of human activity affected wildfire risk by incorporating structural features: A case study in eastern China

Wildfire risk prediction is a critical component of disaster prevention and mitigation, often closely associated with local human activities in most regions. Recent studies demonstrate that employing joint modeling techniques using diverse datasets alongside Convolutional Neural Networks-Long Short-Term Memory Networks (ConvLSTM) produces favorable predictive results. However, previous research inadequately explored the different impact of factors across different categories and spatial orientations, and neglected the impact of fuels and human activities inside the samples. This study focuses on the six eastern provinces of China, utilizing a multi-source dataset comprising satellite-monitored wildfire products from 2012 to 2022, along with various factors indicating terrestrial and human activities, simulated meteorological elements and high-resolution vegetation imagery. By introducing channel and spatial attention mechanisms and visual transformer mode, this research optimizes the ConvLSTM wildfire prediction model. Results indicate a noteworthy enhancement, elevating accuracy, Kappa coefficient, and AUC of ROC curves from 91.15%, 80.87%, and 97.01% to 92.79%, 84.48%, and 97.90%, respectively. Consequently, it reinforces the accuracy by increase of the structural features within samples and quantifying the differences in the importance of different factors, which is also validated by prediction application of the samples in the entire year of 2023. Sensitivity analysis reveals that the current model is still highly dependent on the meteorological factors. Notably, the impact of structural features significantly surpasses the influence of terrain and terrestrial ecology elements, which should be considered in further models. Thus, this study has developed a methodology integrating multiple attention mechanisms and sample structural features, which could furnish high-precision daily kilometer-level wildfire risk prediction products. This method could improve the efficiency of prevention and control by improving the accuracy and narrowing the high-risk areas.

The City of Penticton’s comprehensive approach to wildfire risk reduction

From 2017 to 2023, British Columbians experienced four record-breaking wildfire seasons, resulting in reduced air quality, mass evacuations and the destruction of homes, properties and livelihoods. Wildfire risk reduction is vital to breaking the sequence of disaster that has befallen such communities as Kelowna, BC in 2003, Ft. McMurray, AB in 2016, and Lytton, BC in 2021. As the City of Penticton (‘the City’) is located in a wildfire-prone environment, its Fire Department, FireSmart Team and Emergency Program have worked closely together to facilitate a proactive and comprehensive approach towards reducing the impacts of wildfire on Penticton’s neighbourhoods, businesses and residents through a variety of wildfire mitigation initiatives. This paper discusses the City’s efforts to achieve a holistic wildfire risk management plan through alignment with the seven disciplines of FireSmart and the four pillars of emergency management, namely: the use of education; land use planning and development considerations; vegetation management; emergency planning; and cross training and interagency cooperation. The paper describes the challenges the City has faced, as well its successes, and provides recommendations to help other local authorities reduce the risk of wildfire in their communities.

Quasi Second-Order Stochastic Dominance Model for Balancing Wildfire Risks and Power Outages due to Proactive Public Safety De-Energizations

Quasi Second-Order Stochastic Dominance Model for Balancing Wildfire Risks and Power Outages due to Proactive Public Safety De-Energizations

Enhancing wildfire risk assessment through weather modeling and machine learning: a case study in Orange County, USA

Forest wildfire is a significant threat, causing extensive environmental damage and loss of life. Accurate and effective risk assessments are essential for better understanding and mitigating this risk. In this study, we propose a novel approach that combines weather modeling and machine learning for improved wildfire risk assessment. Our case study focuses on Orange County, USA, known for its high wildfire susceptibility. The methodology involves collecting historical weather and wildfire data in the region. We employ a machine learning model that integrates weather data with other factors, including vegetation and topography, to predict wildfire risk. Through machine learning techniques, we analyze the relationships among these variables and generate accurate risk predictions for Orange County. Results indicate a significant enhancement in wildfire risk assessment with this approach. The developed model provides valuable insights into the factors influencing wildfires, aiding authorities, and policymakers in implementing effective mitigation strategies. By leveraging weather modeling and machine learning, we can enhance our understanding and management of wildfire risks, safeguarding the environment and communities. This research highlights the potential of these technologies to improve wildfire mitigation efforts. The findings contribute to a proactive approach to addressing the serious threat of forest wildfires.

Decision Support for Landscapes with High Fire Hazard and Competing Values at Risk: The Upper Wenatchee Pilot Project

Background: Climate change is a strong contributing factor in the lengthening and intensification of wildfire seasons, with warmer and often drier conditions associated with increasingly severe impacts. Land managers are faced with challenging decisions about how to manage forests, minimize risk of extreme wildfire, and balance competing values at risk, including communities, habitat, air quality, surface drinking water, recreation, and infrastructure. Aims: We propose that land managers use decision analytic frameworks to complement existing decision support systems such as the Interagency Fuel Treatment Decision Support System. Methods: We apply this approach to a fire-prone landscape in eastern Washington State under two proposed landscape treatment alternatives. Through stakeholder engagement, a quantitative wildfire risk assessment, and translating results into probabilistic descriptions of wildfire occurrence (burn probability) and intensity (conditional flame length), we construct a decision tree to explicitly evaluate tradeoffs of treatment alternative outcomes. Key Results: We find that while there are slightly more effective localized benefits for treatments involving thinning and prescribed burning, neither of the UWPP’s proposed alternatives are more likely to meaningfully minimize the risk of wildfire impacts at the landscape level. Conclusions: This case study demonstrates that a quantitatively informed decision analytic framework can improve land managers’ ability to effectively and explicitly evaluate tradeoffs between treatment alternatives.

Mitigating drought mortality by incorporating topography into variable forest thinning strategies

Abstract Drought-induced productivity reductions and tree mortality have been increasing in recent decades in forests around the globe. Developing adaptation strategies hinges on an adequate understanding of the mechanisms governing the drought vulnerability of forest stands. Prescribed reduction in stand density has been used as a management tool to reduce water stress and wildfire risk, but the processes that modulate fine-scale variations in plant water supply and water demand are largely missing in ecosystem models. We used an ecohydrological model that couples plant hydraulics with groundwater hydrology to examine how within-stand variations in tree spatial arrangements and topography might mitigate forest vulnerability to drought at individual-tree and stand scales. Our results demonstrated thinning generally ameliorated plant hydraulic stress and improved carbon and water fluxes of the remaining trees, although the effectiveness varied by climate and topography. Variable thinning that adjusted thinning intensity based on topography-mediated water availability achieved higher stand productivity and lower mortality risk, compared to evenly-spaced thinning at comparable intensities. The results from numerical experiments provided mechanistic evidence that topography mediates the effectiveness of thinning and highlighted the need for an explicit consideration of within-stand heterogeneity in trees and abiotic environments when designing forest thinning to mitigate drought impacts.

A Density Management Diagram for Pitch Pine to Illustrate Tradeoffs between Carbon and Wildfire Risk

Abstract Pitch pine (Pinus rigida Mill.) can be found across a broad range in eastern North America but assumes local dominance only on poor soils in the northeastern United States. Contemporary management goals in the Northeast for areas dominated by pitch pine are focused on noncommercial benefits of forests, such as carbon density, reduced wildfire risk, habitat for rare species, and water provisioning. We present a density management diagram that empirically articulates the size-density limits of even-aged pitch pine stands. Included in the diagram are wildfire risk and carbon density, which are inversely related for most stand sizes. Maximum possible aboveground live tree carbon begins to decline at a quadratic mean diameter greater than 9 in., while crown fire risk remains high along the size-density limit until a quadratic mean diameter above 12 in. is achieved. Study Implications: Modern silvicultural tools that illustrate forest stand conditions have not been developed for pitch pine, but this species occurs in a region with much public attention on forests. We develop and present a density management diagram to show the interplay of different social goals for the forest and how they relate to the maximum size-density relationship. Pitch pine stands with high levels of aboveground live carbon are at high risk of crown fire, particularly in the smaller size classes.