Climate Change, Forest Fires, and Health in California

Cisneros, Ricardo; Schweizer, Don; Tarnay, Leland; Navarro, Kathleen M.; Veloz, David; Procter, C. Trent

doi:10.1007/978-3-319-61346-8_8

Cited by 9 publications

(4 citation statements)

References 96 publications

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“…These risks are magnified by the expansion of human development throughout California into forested landscapes (Hurteau et al., 2014). The policy of fire suppression, however, has exacerbated air pollution overall, whereas managed fire could mitigate human health impacts while restoring ecosystem services including improved wildlife habitat (Cisneros et al., 2017). Further, the increasing size of current high severity fires often retards post‐fire forest regeneration (Haffey et al., 2018).…”

Section: Discussionmentioning

confidence: 99%

Intermediate fire severity diversity promotes richness of forest carnivores in California

Furnas

Goldstein

Figura

2021

Diversity and Distributions

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Aim Fire can strongly influence ecosystem function, and human activities are disrupting fire activity at the global scale. Ecological theory and a growing body of literature suggest that a mixed severity fire regime promotes biodiversity in western North America. Some researchers advocate the use of pyrodiversity (i.e. heterogeneity in aspects of the fire regime such as time since fire or severity) as a conservation index to be maximized. Others caution against this approach arguing that the index oversimplifies fire–biodiversity interactions across trophic, spatial and temporal scales. We evaluated the effects of several landscape‐scale pyrodiversity indices, and their severity and time‐since‐fire components, on species richness of forest carnivores. Location Northern California, United States. Methods We gathered data on fire history and mammal occurrence from camera trap surveys at 1,451 sites across Northern California public and private forestlands during 2009–2018. We used these data to model the effects of fire severity diversity, and its components (i.e. low, moderate and high severity wildfires), on carnivore richness at short (10 years) and longer (25 years) timeframes. We repeated the modelling using a measure of time‐since‐fire diversity and its components (<10 years, 10–20 years, 20–30 years, 30–40 years, 40–100 years). We used Bayesian multispecies occupancy modelling to correct for imperfect measurement of species richness. Results We found that carnivore richness was highest at locations with intermediate fire severity diversity (0.46, 90%CI: 0.40–0.52) calculated using Simpson's Measure of Evenness (range: 0–1) for the 10‐year timeframe, and the results were almost identical yet less precise for the longer timeframe. When we separated fire severity diversity into its components, we found that carnivore richness was highest at locations where 17% (90%CI: 4–20) of the landscape had experienced low severity burns over the past decade. In contrast, we found no association between time‐since‐fire diversity and carnivore richness, however, an intermediate amount of one of the components (e.g., the total amount of fire in the past 10 years) was positively associated with carnivore richness. Our results are consistent with a mixed severity fire regime wherein there is a greater extent of low severity than high severity fire. Main conclusions Overall our results suggest that carnivores would benefit from landscapes managed for greater, but not maximal, fire severity diversity. Our results also suggest that prescribed, low severity burns may provide ecological services to wildlife not otherwise provided by silviculture in a managed forest landscape.

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Section: Discussionmentioning

confidence: 99%

Intermediate fire severity diversity promotes richness of forest carnivores in California

Furnas

Goldstein

Figura

2021

Diversity and Distributions

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“…The major causes of socio-economic losses and human casualties include traffic accidents [1,2], forest fires [3,4], and natural and geological disasters [5,6]. Among them, the ecological damage caused by forest fires is huge, irreversible and long-lasting compared to the economic damage caused to human society, even endangering the fragile global ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Wang

Peng

2022

Forests

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In recent years, frequent forest fires have plagued countries all over the world, causing serious economic damage and human casualties. Faster and more accurate detection of forest fires and timely interventions have become a research priority. With the advancement in deep learning, fully convolutional network architectures have achieved excellent results in the field of image segmentation. More researchers adopt these models to segment flames for fire monitoring, but most of the works are aimed at fires in buildings and industrial scenarios. However, there are few studies on the application of various fully convolutional models to forest fire scenarios, and comparative experiments are inadequate. In view of the above problems, on the basis of constructing the dataset with remote-sensing images of forest fires captured by unmanned aerial vehicles (UAVs) and the targeted optimization of the data enhancement process, four classical semantic segmentation models and two backbone networks are selected for modeling and testing analysis. By comparing inference results and the evaluation indicators of models such as mPA and mIoU, we can find out the models that are more suitable for forest fire segmentation scenarios. The results show that the U-Net model with Resnet50 as a backbone network has the highest segmentation accuracy of forest fires with the best comprehensive performance, and is more suitable for scenarios with high-accuracy requirements; the DeepLabV3+ model with Resnet50 is slightly less accurate than U-Net, but it can still ensure a satisfying segmentation performance with a faster running speed, which is suitable for scenarios with high real-time requirements. In contrast, FCN and PSPNet have poorer segmentation performance and, hence, are not suitable for forest fire detection scenarios.

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“…where û and σ̂ are estimates of the expected value and standard deviation, respectively, evaluated for each site and time using the model in Equation (1). The probability of departure from the norm, i.e., p = Pr are estimates of the expected value and standard deviation, respectively, evaluated for each site and time using the model in Equation (1). The probability of departure from the norm, i.e., p = Pr[r = 1] = [1 + exp(−θ)] −1 is estimated using the following logistic regression model…”

Section: Statistical Modelsmentioning

confidence: 99%

“…Recent droughts in California, in addition to the history of fire suppression in the United States, have contributed to increased wildland fire [1,2] Additions are highlighted yellow throughout the text and old citation style removed. Ecologically beneficial fire may be used to reduce these stressors [3,4].…”

Section: Introductionmentioning

confidence: 99%

Determining the Impact of Wildland Fires on Ground Level Ambient Ozone Levels in California

et al. 2020

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Wildland fire smoke is visible and detectable with remote sensing technology. Using this technology to assess ground level pollutants and the impacts to human health and exposure is more difficult. We found the presence of satellite derived smoke plumes for more than a couple of hours in the previous three days has significant impact on the chances of ground level ozone values exceeding the norm. While the magnitude of the impact will depend on characteristics of fires such as size, location, time in transport, or ozone precursors produced by the fire, we demonstrate that information on satellite derived smoke plumes together with site specific regression models provide useful information for supporting causal relationship between smoke from fire and ozone exceedances of the norm. Our results indicated that fire seasons increasing the median ozone level by 15 ppb. However, they seem to have little impact on the metric used for regulatory compliance, in particular at urban sites, except possibly during the 2008 forest fires in California.

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Climate Change, Forest Fires, and Health in California

Cited by 9 publications

References 96 publications

Intermediate fire severity diversity promotes richness of forest carnivores in California

Intermediate fire severity diversity promotes richness of forest carnivores in California

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Determining the Impact of Wildland Fires on Ground Level Ambient Ozone Levels in California

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