Time Series of Potential US Wildland Fire Smoke Exposures

Vargo, Jason

doi:10.3389/fpubh.2020.00126

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…With this screening distance, we are not necessarily using a plume's full extent, but rather following along the plume centerline for a distance coinciding with surface smoke concentrations decaying below a preset value. While Vargo [87] avoided the same shortcoming of using the HMS plumes for estimating exposure by adding aerosol optical depth information to the analysis to add smoke concentration information, such an approach is not amenable for scenario-based projection aspects of the current study, where the use of the gaussian plume model allows flexibility in adjusting the screening distance based on simulated changes in fuel conditions [87].…”

Section: Discussionmentioning

confidence: 99%

Mapping Firescapes for Wild and Prescribed Fire Management: A Landscape Classification Approach

Gould,

Pomara,

Nepal

et al. 2023

Land

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Risks associated with severe wildfire are growing in forest landscapes due to interactions among climate change, fuel accumulation from fire suppression, an expanding wildland–urban interface, and additional factors. People, infrastructure, ecosystem services, and forest health all face varying degrees of risk. The spatial distributions of the many social and ecological factors that influence wildfire, its impacts, and management responses are an important landscape-level context for managing risks and fostering resilient lands and communities. Decision-support tools that integrate these varied distributions can provide a holistic and readily interpreted characterization of landscapes, helping fire management decision making be appropriate, efficient, and effective. Firescapes—landscape types defined in relation to fire, its drivers, and its effects as a socioecological system—fill this role, providing a way to organize and interpret spatial variation along multiple relevant dimensions. We describe a quantitative approach for classifying and mapping firescapes for decision support, using the southeastern United States as a case study. We worked with regional partners to compile relevant large-scale datasets and identify 73 variables for analysis. We used factor analysis to reduce the data to eight factors with intuitive interpretations relevant to fire dynamics, fire history, forest characteristics, climate, conservation and ecosystem service values, social and ecological landscape properties, and social vulnerabilities. We then used cluster analysis on the factors to generate quantitative landscape classes, which we interpreted as nine distinctive firescape classes. The firescapes provide a broad-scale socioecological information context for wildfire risk management and planning. The analytical approach can accommodate different data types at a variety of scales, incorporate new monitoring data as they are available, and can be used under data-driven scenarios to assess possible consequences of future change. The resulting firescape maps can provide decision support to forest managers, planners, and other stakeholders, informing appropriate strategies to manage fire and associated risks, build community and forest resilience to fire, and improve conservation outcomes.

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Section: Discussionmentioning

confidence: 99%

Mapping Firescapes for Wild and Prescribed Fire Management: A Landscape Classification Approach

Gould,

Pomara,

Nepal

et al. 2023

Land

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“…Also, wildfire smoke is defined based on satellite imagery, therefore exposure misclassification is possible in this context since it may also capture smoke higher in the atmosphere. Nonetheless, validation of the HMS smoke product with ground-level monitors has been shown correlation with PM 2.5 concentrations [43]. Additionally, we only focused on cumulative mortality as the outcome of interest.…”

Section: Discussionmentioning

confidence: 99%

“…These estimates were revised and modified by trained analysts to improve accuracy. Aerosol Optical Depth information was collected from the Geostationary Operational Environmental Satellite Aerosol and Smoke Product and smoke plumes were categorized by NOAA's Office of Satellite and Product Operations as light, medium, or heavy categories, corresponding to PM 2.5 concentrations of 0-10, 10-21, and 22+ µg m 3 , respectively [43]. Satellite imagery for the border region was obtained from the National Aeronautics and Space Administration Worldview visualization tool [44].…”

Section: Data Sourcesmentioning

confidence: 99%

The potential impact of wildfire smoke on COVID-19 cumulative deaths in the San Diego-Tijuana border region

Schwarz

Aguilera

Quiñones³

et al. 2023

Environ. Res.: Health

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2020 broke records for the most active fire year on the West Coast, resulting in the worst air quality observed in decades. Concurrently, the public health threat of COVID-19 caused over 1 million deaths in the United States (US) and Mexico in 2020 and 2021. Due to the effect of air pollution on respiratory diseases, wildfire-specific particulate matter is a hypothesized driver of COVID-19 severity and death. Capitalizing on wildfire smoke that hit the San Diego-Tijuana border region in September 2020, we applied synthetic control methods to explore its differential role in affecting COVID-19 mortality on both sides of the border. Daily data on COVID-19 cumulative deaths for US Counties was obtained from the CDC COVID tracker and data for Mexican Municipalities was obtained from the Mexican Secretary of Health. Counties and Municipalities with wildfire smoke exposure were identified using the NOAA hazard mapping Smoke Product; a day where 90% of the area covered by smoke was considered exposed for the main analyses. Unexposed Counties/Municipalities were considered as potential controls. The San Diego-Tijuana border region was covered by dense smoke by September 7th; 707 COVID-19 deaths had occurred in San Diego and 1367 in Tijuana. While a slight increase in cumulative mortality was observed in San Diego, no change was found in Tijuana; neither estimate indicated a strong precise effect of wildfire smoke on COVID-19 mortality. We hope this study will serve as an illustration of how border contexts can be used to investigate differential vulnerability to wildfire smoke for infectious diseases. Examining the interactive effect of COVID-19 and smoke can help in recognizing the implications of these dual health risks which will be increasingly important as wildfires become more frequent and severe in the context of climate change.

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“…A reliable smoke alarm is essential for preventing fire-related losses. Rapidly spreading wildfires, exacerbated by climate change, can have far-reaching consequences on human communities, ecosystems, and economies, if not detected or extinguished quickly [ 1 , 2 ]. Smoke and flame detection are both applicable to wildfire monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

Mukhiddinov

Abdusalomov

2022

Sensors

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Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network’s backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset.

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Time Series of Potential US Wildland Fire Smoke Exposures

Cited by 8 publications

References 12 publications

Mapping Firescapes for Wild and Prescribed Fire Management: A Landscape Classification Approach

Mapping Firescapes for Wild and Prescribed Fire Management: A Landscape Classification Approach

The potential impact of wildfire smoke on COVID-19 cumulative deaths in the San Diego-Tijuana border region

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

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