scholarly journals Design of Work System for Reducing Pollution and Forest Fire Smoke

2021 ◽  
Vol 1125 (1) ◽  
pp. 012107
Author(s):  
D Riandadari ◽  
S Gunawan
Keyword(s):  
Forest Fire ◽  
Work System ◽  
Fire Smoke

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

Observations of boreal forest fire smoke in the stratosphere by POAM III, SAGE II, and lidar in 1998

2000 ◽  
Vol 27 (9) ◽  
pp. 1407-1410 ◽  
Author(s):  
Michael Fromm ◽  
Jerome Alfred ◽  
Karl Hoppel ◽  
John Hornstein ◽  
Richard Bevilacqua ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Forest Fire ◽  
Fire Smoke

scholarly journals Transport of Canadian forest fire smoke over the UK as observed by lidar

2018 ◽  
Author(s):  
Geraint Vaughan ◽  
Adam P. Draude ◽  
Hugo M. A. Ricketts ◽  
David M. Schultz ◽  
Mariana Adam ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Western Canada ◽  
North West ◽  
Fire Smoke ◽  
Range Transport ◽  
Lidar Ratio ◽  
The Uk ◽  
Lidar Observations ◽  
Aerosol Properties

Abstract. Layers of aerosol at heights between 2 and 11 km were observed with Raman lidars in the UK between 23 and 31 May 2016. A network of such lidars, supported by ceilometer observations, is used to map the extent of the aerosol and its optical properties. Spaceborne lidar profiles show that the aerosol originated from forest fires over Western Canada around 17 May, and indeed the aerosol properties – weak depolarisation and a lidar ratio at 355 nm in the range 35–65 sr – were consistent with long-range transport of forest fire smoke. The event was unusual in its persistence – the smoke plume was drawn into an atmospheric block that kept it above North-west Europe for nine days. Lidar observations show how the smoke layers became optically thinner during this period, but the lidar ratio and aerosol depolarisation showed little change.

scholarly journals Forest Fire Smoke Recognition Based on Multiple Feature Fusion

2018 ◽  
Vol 435 ◽  
pp. 012006 ◽  
Author(s):  
Chang Lu ◽  
Mingqi Lu ◽  
Xiaobo Lu ◽  
Min Cai ◽  
Xiaoqiang Feng
Keyword(s):  
Forest Fire ◽  
Feature Fusion ◽  
Multiple Feature ◽  
Fire Smoke ◽  
Multiple Feature Fusion ◽  
Smoke Recognition

scholarly journals Optimization of a Method for the Detection of Biomass-Burning Relevant VOCs in Urban Areas Using Thermal Desorption Gas Chromatography Mass Spectrometry

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 276 ◽  
Author(s):  
Boggarapu Praphulla Chandra ◽  
Crystal D. McClure ◽  
JoAnne Mulligan ◽  
Daniel A. Jaffe
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Thermal Desorption ◽  
Forest Fire ◽  
Biomass Burning ◽  
Urban Areas ◽  
Gas Chromatography Mass Spectrometry ◽  
Specific Chemical ◽  
Chromatography Mass Spectrometry ◽  
Fire Smoke

Forest fire smoke influence in urban areas is relatively easy to detect at high concentrations but more challenging to detect at low concentrations. In this study, we present a simplified method that can reliably quantify smoke tracers in an urban environment at relatively low cost and complexity. For this purpose, we used dual-bed thermal desorption tubes with an auto-sampler to collect continuous samples of volatile organic compounds (VOCs). We present the validation and evaluation of this approach using thermal desorption gas chromatography mass spectrometry (TD-GC-MS) to detect VOCs at ppt to ppb concentrations. To evaluate the method, we tested stability during storage, interferences (e.g., water and O3), and reproducibility for reactive and short-lived VOCs such as acetonitrile (a specific chemical tracer for biomass burning), acetone, n-pentane, isopentane, benzene, toluene, furan, acrolein, 2-butanone, 2,3-butanedione, methacrolein, 2,5- dimethylfuran, and furfural. The results demonstrate that these VOCs can be quantified reproducibly with a total uncertainty of ≤30% between the collection and analysis, and with storage times of up to 15 days. Calibration experiments performed over a dynamic range of 10–150 ng loaded on to each thermal desorption tube at different relative humidity showed excellent linearity (r2 ≥ 0.90). We utilized this method during the summer 2019 National Oceanic and Atmospheric Administration (NOAA) Fire Influence on Regional to Global Environments Experiment–Air Quality (FIREX-AQ) intensive experiment at the Boise ground site. The results of this field study demonstrate the method’s applicability for ambient VOC speciation to identify forest fire smoke in urban areas.

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