scholarly journals Chemical and optical properties of 2003 Siberian forest fire smoke observed at the summit of Mt. Fuji, Japan

2007 ◽  
Vol 112 (D13) ◽  
Author(s):  
Naoki Kaneyasu ◽  
Yasuhito Igarashi ◽  
Yousuke Sawa ◽  
Hiroshi Takahashi ◽  
Hideshige Takada ◽  
...  
Keyword(s):  
Optical Properties ◽  
Forest Fire ◽  
Fire Smoke

Optical properties of boreal forest fire smoke derived from Sun photometry

2002 ◽  
Vol 107 (D11) ◽  
pp. AAC 6-1-AAC 6-19 ◽  
Author(s):  
N. T. O'Neill ◽  
T. F. Eck ◽  
B. N. Holben ◽  
A. Smirnov ◽  
A. Royer ◽  
...  
Keyword(s):  
Optical Properties ◽  
Boreal Forest ◽  
Forest Fire ◽  
Fire Smoke

Observations of California forest fire aerosol in Potenza (Italy) by the multi-wavelength Raman lidar MUSA

2021 ◽  
Author(s):  
Benedetto De Rosa ◽  
Lucia Mona ◽  
Aldo Amodeo ◽  
Donato Summa
Keyword(s):  
Optical Properties ◽  
Real Time ◽  
Forest Fire ◽  
Radiative Forcing ◽  
Vertical Structure ◽  
Back Trajectory ◽  
Free Troposphere ◽  
Raman Lidar ◽  
Smoke Particles ◽  
Fire Smoke

<p>Smoke aerosols play an important role in the atmospheric chemistry in terms of direct and indirect radiative forcing. Despite this, their properties in free troposphere and stratosphere are still insufficiently studied. When the smoke reaches these altitudes can be transported over transcontinental distances. During the transport of particles important transforming processes, such as coagulation, condensation, and gas-to-particle conversion occur, thus affecting environment and climate. The optical properties of smoke plumes have been usually analyzed by ground-based radiometers and satellite. However, these techniques cannot characterize accurately the high variability of the vertical structure of smoke aerosol. Raman lidar systems  are characterized by high temporal and vertical resolutions and have demonstrated a strong capability to study long-range transport, optical properties and vertical structure of forest fire smoke. </p><p>In the 2020 California’s fire season was exceptionally catastrophic. 23<sup>rd </sup>October, the immense Sonoma fire, in few days scorched 31000 hectares. The deep convection lifted the smoke from these fires to great heights. After reaching the free troposphere and stratosphere, the forest fire smoke was transported over great distances and reached the south of Italy, as evinced by the map of biomass burning aerosol optical depth at 550 nm, provided by the Copernicus Atmosphere Monitoring Service (CAMS).</p><p>This work reports measurements carried out in the frame of the project CAMS21b by the Raman lidar system MUSA deployed at CNR-IMAA Atmospheric Observatory (CIAO) in Potenza. CAMS21b aims to design, test and set up the provisioning to CAMS of ACTRIS/EARLINET products in real time and near real time. </p><p>In the case study of 26 October 2020, from to 10:13 to 13:45 UTC, measurements of particle backscattering coefficient at 355, 532 nm and 1064 and of the particle extinction coefficient at 355 nm and 532nm, show the presence of two distinct aerosol layers. A lower one extending from 6 km to 8 km and an upper one extending from 10 km to 12 km. The back-trajectory analysis reveals that the air masses originated over California, overpassed the Atlantic sea before reaching the measurement site.</p><p>The values of the particle depolarization ratio are similar to those found in literature for smoke aerosols. In the first layer, values lower than 0.05 are indicative for small and spherical smoke particles. The moderately increased depolarization ratios in the second layer indicate the possible presence of partly coated smoke particles.</p><p>More results from this measurement effort will be reported and discussed at the Conference.</p>

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
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