U.S. fires became larger, more frequent, and more widespread in the 2000s

Science Advances, Год журнала: 2022, Номер 8(11)

Опубликована: Март 16, 2022

Recent fires have fueled concerns that regional and global warming trends are leading to more extreme burning. We found compelling evidence average fire events in regions of the United States up four times size, triple frequency, widespread 2000s than previous two decades. Moreover, most also larger, common, likely co-occur with other fires. This documented shift burning patterns across country aligns palpable change dynamics noted by media, public, fire-fighting officials.

Язык: Английский

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Warming weakens the night-time barrier to global fire

Nature, Год журнала: 2022, Номер 602(7897), С. 442 - 448

Опубликована: Фев. 16, 2022

Язык: Английский

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A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

Information Fusion, Год журнала: 2024, Номер 108, С. 102369 - 102369

Опубликована: Март 22, 2024

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses. These losses underscored urgent need to improve public knowledge and advance existing techniques in wildfire management. Recently, use Artificial Intelligence (AI) wildfires, propelled by integration Unmanned Aerial Vehicles (UAVs) deep learning models, has created an unprecedented momentum implement develop more effective Although survey papers explored learning-based approaches wildfire, drone disaster management, risk assessment, a comprehensive review emphasizing application AI-enabled UAV systems investigating role methods throughout overall workflow multi-stage including pre-fire (e.g., vision-based vegetation fuel measurement), active-fire fire growth modeling), post-fire tasks evacuation planning) is notably lacking. This synthesizes integrates state-of-the-science reviews research at nexus observations modeling, AI, UAVs - topics forefront advances elucidating AI performing monitoring actuation from pre-fire, through stage, To this aim, we provide extensive analysis remote sensing with particular focus on advancements, device specifications, sensor technologies relevant We also examine management approaches, monitoring, prevention strategies, well planning, damage operation strategies. Additionally, summarize wide range computer vision emphasis Machine Learning (ML), Reinforcement (RL), Deep (DL) algorithms for classification, segmentation, detection, tasks. Ultimately, underscore substantial advancement modeling cutting-edge UAV-based data, providing novel insights enhanced predictive capabilities understand dynamic behavior.

Язык: Английский

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In the Line of Fire: Consequences of Human-Ignited Wildfires to Homes in the U.S. (1992–2015)

Fire, Год журнала: 2020, Номер 3(3), С. 50 - 50

Опубликована: Сен. 7, 2020

With climate-driven increases in wildfires the western U.S., it is imperative to understand how risk homes also changing nationwide. Here, we quantify number of threatened, suppression costs, and ignition sources for 1.6 million United States (U.S.; 1992–2015). Human-caused accounted 97% residential threatened (within 1 km a wildfire) nearly third costs. This study illustrates wildland-urban interface (WUI), which accounts only small portion U.S. land area (10%), acts as major source fires, almost exclusively human-started. Cumulatively (1992–2015), just over one were within human-caused wildfire perimeters WUI, where communities are built flammable vegetation. An additional 58.8 kilometer across 24-year record. On an annual basis WUI (1999–2014), average 2.5 (2.2–2.8 million, 95% confidence interval) by human-started perimeter up 1-km away). The grew 32 from 1990–2015. convergence warmer, drier conditions greater development into landscapes leaving many vulnerable wildfires. These areas high priority policy management efforts that aim reduce human ignitions promote resilience future particularly this record expected continue grow coming years.

Язык: Английский

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Global Wildfire Susceptibility Mapping Based on Machine Learning Models

Forests, Год журнала: 2022, Номер 13(7), С. 1050 - 1050

Опубликована: Июль 3, 2022

Wildfires are a major natural hazard that lead to deforestation, carbon emissions, and loss of human animal lives every year. Effective predictions wildfire occurrence burned areas essential forest management firefighting. In this paper we apply various machine learning (ML) methods on 0.25° monthly resolution global dataset wildfires. We test the prediction accuracies four different fire classifiers: random (RF), eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP) neural network, logistic regression. Our best ML model predicts with over 90% accuracy, compared approximately 70% using then train regression models predict size obtain an MAE score 3.13 km2, 7.48 km2 linear To our knowledge, is first study be conducted in such dataset. use developed shed light influence factors areas. suggest building upon these results create ML-based weather indices.

Язык: Английский

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Elevated fires during COVID-19 lockdown and the vulnerability of protected areas

Nature Sustainability, Год журнала: 2022, Номер 5(7), С. 603 - 609

Опубликована: Май 5, 2022

Язык: Английский

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Global Warming Reshapes European Pyroregions

Earth s Future, Год журнала: 2023, Номер 11(5)

Опубликована: Май 1, 2023

Abstract Wildland fire is expected to increase in response global warming, yet little known about future changes regimes Europe. Here, we developed a pyrogeography based on statistical models better understand how warming reshapes across the continent. We identified five large‐scale pyroregions with different levels of area burned, frequency, intensity, length period, size distribution, and seasonality. All other things being equal, was found alter distribution these pyroregions, an expansion most prone ranging respectively from 50% 130% under 2° 4°C scenarios. Our estimates indicate strong amplification parts southern Europe subsequent shift toward new regimes, implying substantial socio‐ecological impacts absence mitigation or adaptation measures.

Язык: Английский

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Deep graphical regression for jointly moderate and extreme Australian wildfires

Spatial Statistics, Год журнала: 2024, Номер 59, С. 100811 - 100811

Опубликована: Янв. 17, 2024

Recent wildfires in Australia have led to considerable economic loss and property destruction, there is increasing concern that climate change may exacerbate their intensity, duration, frequency. Hazard quantification for extreme an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, recovery efforts. However, although are typically the most impactful, both small moderate fires can still be devastating local communities ecosystems. Therefore, imperative develop robust statistical methods reliably model full distribution spread. We do so a novel dataset Australian from 1999 2019, analyse monthly spread over areas approximately corresponding Statistical Areas Level 1 2 (SA1/SA2) regions. Given complex nature ignition spread, we exploit recent advances deep learning value theory construct parametric regression using graph convolutional neural networks extended generalised Pareto which allows us observed on irregular spatial domain. highlight efficacy our newly proposed perform hazard assessment population-dense communities, namely Tasmania, Sydney, Melbourne, Perth.

Язык: Английский

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The fastest-growing and most destructive fires in the US (2001 to 2020)

Science, Год журнала: 2024, Номер 386(6720), С. 425 - 431

Опубликована: Окт. 24, 2024

The most destructive and deadly wildfires in US history were also fast. Using satellite data, we analyzed the daily growth rates of more than 60,000 fires from 2001 to 2020 across contiguous US. Nearly half ecoregions experienced fast that grew 1620 hectares 1 day. These accounted for 78% structures destroyed 61% suppression costs ($18.9 billion). From 2020, average peak rate these doubled (+249% relative 2001) Western 3 million within 4 kilometers a fire during this period Given recent devastating wildfires, understanding is crucial improving firefighting strategies community preparedness.

Язык: Английский

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Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima

Ecological Applications, Год журнала: 2019, Номер 29(6)

Опубликована: Апрель 13, 2019

Abstract Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across contiguous United States, we integrate a 30‐yr record with meteorological housing data spatiotemporal Bayesian statistical models spatially varying nonlinear effects. We compared different distributions for number sizes large fires to generate posterior predictive distribution based on finite sample maxima extreme events (the largest over bounded domains). A zero‐inflated negative binomial model fire counts lognormal burned areas provided best performance. This attains 99% interval coverage 93% six year withheld set. Dryness air temperature strongly probabilities. Housing density has hump‐shaped relationship occurrence, occurring at intermediate densities. Statistically, these drivers affect chance an two ways: by altering size distributions, frequency, which influences sampling from tails distributions. conclude that recent should not be surprising, States may verge even larger extremes.

Язык: Английский

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