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Prevalence and predictors of sleep and trauma symptoms in wildfire survivors

Objective This study aimed to establish the prevalence and to identify predictors of insomnia, nightmares and post-traumatic stress disorder (PTSD) in wildfire survivors. Method A total of 126 (23 males, 102 females, and 1 nonbinary individual, Mage = 52 years, SD = 14.4)…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Safety, Social Science
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: sleep disturbance, wildfire survivors, PTSD - post-traumatic stress disorder, insomnia, nightmares

Smoldering peat fire detection by time-resolved measurements of transient CO2 and CH4 emissions using a novel dual-gas optical sensor

A compact and sensitive dual-gas laser absorption sensor was developed for smoldering peat fire detection by real-time monitoring of transient CO2 and CH4 emissions from peat combustion exhaust. The sensor combines two infrared lasers to exploit CH4 and CO2 absorption lines…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Mapping, Monitoring and Inventory
Region(s): International
Keywords: absorption spectroscopy, wavelength modulation spectroscopy, smoldering combustion, peat fire, CO2 - carbon dioxide, CH4 - methane, fire detection

Regional monitoring of biomass burning using passive air sampling technique reveals the importance of MODIS unresolved fires

Field-based sampling can provide more accurate evaluation than MODIS in regional biomass burning (BB) emissions given the limitations of MODIS on unresolved fires. Polyurethane foam-based passive air samplers (PUF-PASs) are a promising tool for collecting atmospheric…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Mapping, Monitoring and Inventory
Region(s): International
Keywords: biomass burning, MODIS - Moderate Resolution Imaging Spectroradiometer, PUF-PAS - polyurethane foam-based passive air sampler, Indo-China Peninsula, levoglucosan, lignin, top-down emission estimations

A dataset for fire and smoke object detection

Fire and smoke object detection is of great significance due to the extreme destructive power of fire disasters. Most of the existing methods, whether traditional computer vision-based models with sensors or deep learning-based models have circumscribed application scenes with…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Intelligence, Models, Monitoring and Inventory
Region(s): International
Keywords: fire detection, smoke detection, dataset, object detection, DFS - Dataset for Fire and Smoke detection, deep learning

A satellite imagery smoke detection framework based on the Mahalanobis distance for early fire identification and positioning

Wildfires negatively affect the atmosphere and ecological environment. The rapid identification of smoke is helpful for early fire detection and positioning, which are significant for fire early warning, fire point tracing, and atmospheric environment monitoring. The purpose of…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Mapping, Models, Monitoring and Inventory
Region(s): International
Keywords: smoke detection, concentration, inversion, Mahalanobis distance, fire detection, fire positioning, satellite imagery

Unmanned-aircraft-system-assisted early wildfire detection with air quality sensors

Numerous hectares of land are destroyed by wildfires every year, causing harm to the environment, the economy, and the ecology. More than fifty million acres have burned in several states as a result of recent forest fires in the Western United States and Australia. According to…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Intelligence
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: UAS - Unmanned Aircraft System, air quality, sensors, fire detection, optimization

Forest fire and smoke detection using deep learning-based learning without forgetting

Background: Forests are an essential natural resource to humankind, providing a myriad of direct and indirect benefits. Natural disasters like forest fires have a major impact on global warming and the continued existence of life on Earth. Automatic identification of forest…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: forest fire, image processing, deep learning, CNN - convolution neural network, learning without forgetting, transfer learning, fire detection, satellite imagery

High-latitude fire activity of recent decades derived from microscopic charcoal and black carbon in Greenland ice cores

Warming temperatures and prolonged drought periods cause rapid changes of fire frequencies and intensities in high-latitude ecosystems. Associated smoke plumes deposit dark particles from incomplete combustion on the Greenland ice sheet that reduce albedo but also provide a…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire History, Fire Occurrence
Region(s): International
Keywords: boreal forest, Greenland, paleofire, black carbon, ice cores, microscopic charcoal analysis, biomass burning

Characterizing aerosols during forest fires over Uttarakhand region in India using multi-satellite remote sensing data

Confirmed rise in average surface temperature and consequent prolonged dry days in tropical Himalayan foothills (tarai region) favors frequent wildfire event which make susceptible to the local forest vegetation and ecology. Recent improvement in spatio-temporal resolution of…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence
Region(s): International
Keywords: India, forest fires, aerosols, NBRT - Normalized Burn Ratio Thermal, MODIS - Moderate Resolution Imaging Spectroradiometer, CALIPSO, remote sensing

Relationship between circum-Arctic atmospheric wave patterns and large-scale wildfires in boreal summer

Long-term assessment of severe wildfires and associated air pollution and related climate patterns in and around the Arctic is essential for assessing healthy human life status. To examine the relationships, we analyzed the National Aeronautics and Space Administration (NASA)…
Person:
Year: 2021
Type: Document
Source: FRAMES
Topic(s): Climate, Emissions and Smoke, Fire Effects, Fire Occurrence, Safety
Region(s): Alaska, International
Keywords: wildfire, aerosol, PM - particulate matter, PM2.5, Arctic, fire climate patterns, atmospheric circulation, particulate organic matter, air pollution

A high-precision forest fire smoke detection approach based on ARGNet

The occurrence of forest fires can lead to ecological damage, property loss, and human casualties. Current forest fire smoke detection methods do not sufficiently consider the characteristics of smoke with high transparency and no clear edges and have low detection accuracy,…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: smoke detection, fire detection, adjacent layer composite network, global optimal nonmaximum suppression, UAV-IoT, recursive feature pyramid with deconvolution and dilated convolution

Analysis of wildfires in the mid and high latitudes using a multi-dataset approach: a case study in California and Krasnoyarsk Krai

In this study, we investigate the emissions from wildfires in the mid latitude (California) and high latitude (Krasnoyarsk Krai) during the periods of 16–17 August 2020 and 28 July 2019, respectively. Wildfires are unique in themselves as they are driven by various factors such…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Mapping, Monitoring and Inventory
Region(s): California, International
Keywords: aerosols, biomass burning, remote sensing, satellites, CO - carbon monoxide, black carbon, air pollutants, CALIPSO, TROPOMI - TROPOspheric Monitoring Instrument

Scalable fire and smoke segmentation from aerial images using convolutional neural networks and Quad-Tree search

Autonomous systems can help firefighting operations by detecting and locating the fire spot from surveillance images and videos. Similar to many other areas of computer vision, Convolutional Neural Networks (CNNs) have achieved state-of-the-art results for fire and smoke…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Mapping, Monitoring and Inventory
Region(s): International
Keywords: smoke detection, fire detection, aerial imagery, wildfire, CNN - convolution neural network, quad-tree

An efficient and lightweight detection model for forest smoke recognition

Massive wildfires have become more frequent, seriously threatening the Earth’s ecosystems and human societies. Recognizing smoke from forest fires is critical to extinguishing them at an early stage. However, edge devices have low computational accuracy and suboptimal real-time…
Person:
Year: 2024
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Models
Region(s): International
Keywords: China, YOLOv8, lightweight model, smoke detection, SimAmazonia, BiFPN - bidirectional feature pyramid network

Current and future patterns of global wildfire based on deep neural networks

Global climate change and extreme weather has a profound impact on wildfire, and it is of great importance to explore wildfire patterns in the context of global climate change for wildfire prevention and management. In this paper, a wildfire spatial prediction model based on…
Person:
Year: 2024
Type: Document
Source: FRAMES
Topic(s): Climate, Emissions and Smoke, Fire Behavior, Fire Occurrence, Fire Prevention, Models, Weather
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: fire management, GFED - Global Fire Emissions Database, convolutional neural network, area burned

Escalating carbon emissions from North American boreal forest wildfires and the climate mitigation potential of fire management

Wildfires in boreal forests release large quantities of greenhouse gases to the atmosphere, exacerbating climate change. Here, we characterize the magnitude of recent and projected gross and net boreal North American wildfire carbon dioxide emissions, evaluate fire management as…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Climate, Emissions and Smoke, Fire Effects, Fire Occurrence, Fire Prevention, Hazard and Risk, Planning
Region(s): Alaska, International
Keywords: Canada, boreal North America, Alaska wildfires, carbon emissions, climate change

Harnessing deep learning for forecasting fire-burning locations and unveiling emissions

Climate change and human activity have increased fires in India. Fine particulate matter (PM2.5) is released into the atmosphere by stubble burning in Punjab and Haryana and forest fires in the north-eastern and central areas of the country. Accurate short-term PM2.5 estimates…
Person:
Year: 2024
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Models
Region(s): International
Keywords: fire forecasting, deep learning, air pollution, LSTM - long short-term memory, India, PM2.5, fine particulate matter

Fine and ultrafine particle emission factors and new diagnostic ratios of PAHs for peat swamp forest fires

Peatland fires are one of the major global sources of atmospheric particles. Emission factors for fine (PM1 and PM2.5) and ultrafine (PM0.1) particles and particle-bound polycyclic aromatic hydrocarbons (PAHs) from plants in the peat swamp forest (PSF), including Melaleuca…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Aquatic, Emissions and Smoke, Fire Effects, Fire Occurrence, Models
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: peat swamp forest, forest burning source, PAH - polycyclic aromatic hydrocarbons, Melaleuca cajuputi, PM2.5, PM1, PM0.1, leaf litter, PAH diagnostic ratios, particle emission factors

An improved wildfire smoke detection based on YOLOv8 and UAV images

Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Models
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: YOLOv8, UAV - unmanned aerial vehicles, smoke detection, forest fires, BiFormer, ghost shuffle convolution, remote sensing, deep learning

ForestFireDetector: expanding channel depth for fine-grained feature learning in forest fire smoke detection

Wildfire is a pressing global issue that transcends geographic boundaries. Many areas, including China, are trying to cope with the threat of wildfires and manage limited forest resources. Effective forest fire detection is crucial, given its significant implications for…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Models
Region(s): International
Keywords: China, smoke detection, YOLOv8, channel depth, fire detection, forest resources

Forest fire smoke detection based on deep learning approaches and unmanned aerial vehicle images

Wildfire poses a significant threat and is considered a severe natural disaster, which endangers forest resources, wildlife, and human livelihoods. In recent times, there has been an increase in the number of wildfire incidents, and both human involvement with nature and the…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Fire Prevention, Models
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: forest fire, smoke detection, wildfire smoke, deep learning, remote sensing, decoupled head, UAV - unmanned aerial vehicles, YOLOv7

Cross-layer extraction structure and multiscale downsampling network with bidirectional transpose FPN for fast detection of wildfire smoke

At present, the wildfire smoke detection algorithm based on YOLOv3 has problems, such as low accuracy and slow detection speed. In this article, we propose a cross-layer extraction structure and multiscale downsampling network with bidirectional transpose FPN (BCMNet) for fast…
Person:
Year: 2023
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Models
Region(s): International
Keywords: fire detection, YOLOv3, smoke detection, China

Fast forest fire smoke detection using MVMNet

Forest fires are a huge ecological hazard, and smoke is an early characteristic of forest fires. Smoke is present only in a tiny region in images that are captured in the early stages of smoke occurrence or when the smoke is far from the camera. Furthermore, smoke dispersal is…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: smoke detection, multioriented, attention mechanism, softpool-spatial pyramid pooling, mixed-NMS

Real-time detection of flame and smoke using an improved YOLOv4 network

Fire is one of the major disasters in the world, which seriously endangers the safety of life and property. Effective flame and smoke detection can provide timely warning information for firefighters. Existing flame and smoke detection algorithms are limited by processor…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Mapping, Monitoring and Inventory
Region(s): International
Keywords: fire detection, flame detection, YOLOv4, MobileNetv3, depthwise separable convolution, DSAM - depth separable attention module, BiFPN - bidirectional feature pyramid network, Light-YOLOv4

Semantic segmentation and analysis on sensitive parameters of forest fire smoke using Smoke-Unet and Landsat-8 imagery

Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in…
Person:
Year: 2022
Type: Document
Source: FRAMES
Topic(s): Emissions and Smoke, Fire Occurrence, Models
Region(s): Alaska, California, Eastern, Great Basin, Hawaii, Northern Rockies, Northwest, Rocky Mountain, Southern, Southwest, International, National
Keywords: forest fire, remote sensing, smoke segmentation, Smoke-Unet, attention mechanism, residual block, Landsat 8, band sensibility, fire detection