97 resultados para Minneapolis (Minn.). Fire Dept.


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In fire-prone regions, wildfire influences spatial and temporal patterns of landscape heterogeneity. The likely impacts of climate change on the frequency and intensity of wildfire highlights the importance of understanding how fire-induced heterogeneity may affect different components of the biota. Here, we examine the influence of wildfire, as an agent of landscape heterogeneity, on the distribution of arboreal mammals in fire-prone forests in south-eastern Australia. First, we used a stratified design to examine the role of topography, and the relative influence of fire severity and fire history, on the occurrence of arboreal mammals 2-3 years after wildfire. Second, we investigated the influence of landscape context on the occurrence of arboreal mammals at severely burnt sites. Forested gullies supported a higher abundance of arboreal mammals than slopes. Fire severity was the strongest influence, with abundance lower at severely burnt than unburnt sites. The occurrence of mammals at severely burned sites was influenced by landscape context: abundance increased with increasing amount of unburnt and understorey-only burnt forest within a 1 km radius. These results support the hypothesis that unburnt forest and moist gullies can serve as refuges for fauna in the post-fire environment and assist recolonization of severely burned forest. They highlight the importance of spatial heterogeneity created by wildfire and the need to incorporate spatial aspects of fire regimes (e.g., creation and protection of refuges) for fire management in fire-prone landscapes.

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This study examined firefighters' sleep quantity and quality throughout multi-day wildfire suppression, and assessed the impact of sleep location, shift length, shift start time and incident severity on these variables. For 4 weeks, 40 volunteer firefighters' sleep was assessed using wrist actigraphy. Analyses revealed that the quantity of sleep obtained on fire days was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. On fire days, total sleep time was less when: (i) sleep location was in a tent or vehicle, (ii) shifts were greater than 14 h and (iii) shifts started between 05:00 and 06:00 h. This is the first empirical investigation providing objective evidence that firefighters' sleep is restricted during wildfire suppression. Furthermore, sleep location, shift length and shift start time should be targeted when designing appropriate controls to manage fatigue-related risk and preserve firefighters' health and safety during wildfire events. Practitioner Summary: During multi-day wildfire suppression, firefighters' sleep quantity was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. Furthermore, total sleep time was less when: (i) sleep occurred in a tent/vehicle, (ii) shifts were >14 h and (iii) shifts started between 05:00 and 06:00 h.

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Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.

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Fire is an integral disturbance shaping forest community dynamics over large scales. However, understanding the relationship between fire induced habitat disturbance and biodiversity remain equivocal. Ecological theories including the intermediate disturbance hypothesis (IDH) and the habitat accommodation model (HAM) offer predictive frameworks that could explain faunal responses to fire disturbances. We used an 80 year post-fire chronosequence to investigate small reptile community responses to fires in temperate forests across 74 sites. First, we evaluated if changes in species richness, abundance and evenness post-fire followed trends of prior predictions, including the IDH. Second, using competing models of fine scale habitat elements we evaluated the specific ways which fire influenced small reptiles. Third, we evaluated support for the HAM by examining compositional changes of reptile community post-fire. Relative abundance was positively correlated to age post-fire while richness and evenness showed no associations. The abundance trend was as expected based on the prior prediction of sustained population increase post-disturbance, but the trend for richness contradicted the prediction of highest diversity at intermediate levels of disturbance (according to IDH). Abundance changes were driven mainly by changes in overstorey, ground layer, and shelter, while richness and evenness did not associate with any vegetation parameter. Community composition was not strongly correlated to age since fire, thus support for the HAM was weak. Overall, in this ecosystem, frequent fire disturbances can be detrimental to small reptiles. Future studies utilizing approaches based on species traits could enhance our understanding of biodiversity patterns post-disturbance.

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Wildfires have major impacts on ecosystems globally. Fire regimes (including fire frequency, intensity, season and type of fire) influence the status of species by altering habitat suitability at the site scale, and by creating heterogeneity at the landscape scale. The relative effects of site and landscape-scale fire attributes on animal species are rarely examined together. Such knowledge is important, given that fire regimes are sensitive to changing land management practices; and that fires are predicted to become larger and more frequent in some regions as a result of climate change. Here, we tested the relative influence of elements of the fire regime (fire severity, fire history) at the site-scale, and the landscape context (extent of surrounding unburnt forest, fire heterogeneity) on the occurrence of native terrestrial mammals after severe wildfire in south-eastern Australia. We conducted surveys by using automatically triggered, infrared cameras at 80 sites in fire-prone eucalypt forests, 2-3. years post-wildfire. Thirteen native mammal species were recorded, eight of which were detected with sufficient frequency for analysis. Most species were widespread (35-90% of sites) and recorded in all fire severity classes. Fire effects at the site-level were more influential than landscape context effects arising from heterogeneity in the fire regime (e.g. extent of surrounding unburnt forest). Fire severity was the most influential of the fire-regime elements investigated, but it affected different species in different ways. This study highlights three main points relevant to conservation of terrestrial mammals after wildfire. First, spatial variation in fire severity associated with wildfire (ranging from unburned to severely burned stands) is an important contributor to the post-fire status of species. Second, post-fire environmental conditions are significant: here, rapid regeneration of vegetation following drought-breaking rains greatly influenced the suitability of post-fire habitats. Third, it is valuable to consider the effects of the fire regime at multiple scales, including both the site (forest stand) and its landscape context. Insights from short-term surveys, such as this, will be enhanced by complementary longitudinal studies, especially where they encompass environmental variation through the post-fire succession.

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Resource selection by animals influences individual fitness, the abundance of local populations, and the distribution of species. Further, the degree to which individuals select particular resources can be altered by numerous factors including competition, predation, and both natural- and human-induced environmental change. Understanding the influence of such factors on the way animals use resources can guide species conservation and management in changing environments. In this study, we investigated the effects of a prescribed fire on small-scale (microhabitat) resource selection, abundance, body condition, and movement pathways of a native Australian rodent, the bush rat (Rattus fuscipes). Using a before-after, control-impact design, we gathered data from 60 individuals fitted with spool and line tracking devices. In unburnt forest, selection of resources by bush rats was positively related to rushes, logs and complex habitat, and negatively related to ferns and litter. Fire caused selection for spreading grass, rushes, and complex habitat to increase relative to an unburnt control location. At the burnt location after the fire, rats selected patches of unburnt vegetation, and no rats were caught at a trapping site where most of the understory had been burnt. The fire also reduced bush rat abundance and body condition and caused movement pathways to become more convoluted. After the fire, some individuals moved through burnt areas but the majority of movements occurred within unburnt patches. The effects of fire on bush rat resource selection, movement, body condition, and abundance were likely driven by several linked factors including limited access to shelter and food due to the loss of understory vegetation and heightened levels of perceived predation risk. Our findings suggest the influence of prescribed fire on small mammals will depend on the resulting mosaic of burnt and unburnt patches and how well this corresponds to the resource requirements of particular species.

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Proposing efficient methods for fire protection is becoming more and more important, because a small flame of fire may cause huge problems in social safety. In this paper, an effective fire flame detection method is investigated. This fire detection method includes four main stages: in the first step, a linear transformation is applied to convert red, green, and blue (RGB) color space through a 3∗3 matrix to a new color space. In the next step, fuzzy c-mean clustering method (FCM) is used to distinguish between fire flame and non-fire flame pixels. Particle Swarm Optimization algorithm (PSO) is also utilized in the last step to decrease the error value measured by FCM after conversion. Finally, we apply Otsu threshold method to the new converted images to make a binary picture. Empirical results show the strength, accuracy and fast-response of the proposed algorithm in detecting fire flames in color images.