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

2018 ◽  
Vol 18 (15) ◽  
pp. 11375-11388 ◽  
Author(s):  
Geraint Vaughan ◽  
Adam P. Draude ◽  
Hugo M. A. Ricketts ◽  
David M. Schultz ◽  
Mariana Adam ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Western Europe ◽  
Geographic Area ◽  
Thin Layers ◽  
Smoke Plume ◽  
Fire Smoke ◽  
Range Transport ◽  
Lidar Ratio ◽  
The Uk

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 these lidars, supported by ceilometer observations, is used to map the extent of the aerosol and its optical properties. Space-borne lidar profiles show that the aerosol originated from forest fires over western Canada around 17 May, and indeed the aerosol properties – weak volume depolarisation (<5 %) 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-western Europe for 9 days. Lidar observations show how the smoke layers became optically thinner during this period, but the lidar ratio and aerosol depolarisation showed little change. The results demonstrate the value of a dense network of observations for tracking forest fire smoke, and show how the dispersion of smoke in the free troposphere leads to the emergence of discrete thin layers in the far field. They also show how atmospheric blocking can keep a smoke plume in the same geographic area for over a week.

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.

Feasibility of forest-fire smoke detection using lidar

2003 ◽  
Vol 12 (2) ◽  
pp. 159 ◽  
Author(s):  
Andrei B. Utkin ◽  
Armando Fernandes ◽  
Fernando Simões ◽  
Alexander Lavrov ◽  
Rui Vilar
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
High Repetition Rate ◽  
Smoke Detection ◽  
Smoke Plume ◽  
Smoke Plumes ◽  
Lidar Measurements ◽  
Fire Smoke ◽  
532 Nm

The feasibility and fundamentals of forest fire detection by smoke sensing with single-wavelength lidar are discussed with reference to results of 532-nm lidar measurements of smoke plumes from experimental forest fires in Portugal within the scope of the Gestosa 2001 project. The investigations included tracing smoke-plume evolution, estimating forest-fire alarm promptness, and smoke-plume location by azimuth rastering of the lidar optical axis. The possibility of locating a smoke plume whose source is out of line of sight and detection under extremely unfavourable visibility conditions was also demonstrated. The eye hazard problem is addressed and three possibilities of providing eye-safety conditions without loss of lidar sensitivity (namely, using a low energy-per-pulse and high repetition-rate laser, an expanded laser beam, or eye-safe radiation) are discussed.

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.

scholarly journals Analysis of a warehouse fire smoke plume over Paris with an N<sub>2</sub> Raman lidar and an optical thickness matching algorithm

2018 ◽  
Vol 11 (12) ◽  
pp. 6525-6538 ◽  
Author(s):  
Xiaoxia Shang ◽  
Patrick Chazette ◽  
Julien Totems
Keyword(s):  
Optical Thickness ◽  
Monte Carlo Algorithm ◽  
Optical Parameters ◽  
Depolarization Ratio ◽  
Raman Lidar ◽  
Smoke Plume ◽  
Paris Area ◽  
Fire Smoke ◽  
Lidar Ratio ◽  
Warehouse Fire

Abstract. A smoke plume, coming from an accidental fire in a textile warehouse in the north of Paris, covered a significant part of the Paris area on 17 April 2015 and seriously impacted the visibility over the megalopolis. This exceptional event was sampled with an automatic N2 Raman lidar, which operated 15 km south of Paris. The industrial pollution episode was concomitant with the long-range transport of dust aerosols originated from the Sahara, and with the presence of an extended stratus cloud cover. The analysis of the ground-based lidar profiles therefore required the development of an original inversion algorithm, using a top-down aerosol optical thickness matching (TDAM) approach. This study is, to the best of our knowledge, the first lidar measurement of a fresh smoke plume, emitted only a few hours after an accidental warehouse fire. Vertical profiles of the aerosol extinction coefficient, depolarization ratio, and lidar ratio are derived to optically characterize the aerosols that form the plume. We found a lidar ratio close to 50±10 sr for this fire smoke aerosol layer. The particle depolarization ratio is low, ∼1±0.1 %, suggesting the presence of either small particles or spherical hydrated aerosols in that layer. A Monte Carlo algorithm was used to assess the uncertainties on the optical parameters and to evaluate the TDAM algorithm.

scholarly journals Optical thickness matching algorithm applied to the case study of an accidental fire smoke plume over the Paris area with N<sub>2</sub>-Raman lidar

2018 ◽  
Author(s):  
Xiaoxia Shang ◽  
Patrick Chazette ◽  
Julien Totems
Keyword(s):  
Optical Thickness ◽  
Monte Carlo Algorithm ◽  
Spherical Particles ◽  
Optical Parameters ◽  
Aerosol Layer ◽  
Raman Lidar ◽  
Smoke Plume ◽  
Paris Area ◽  
Fire Smoke ◽  
Lidar Ratio

Abstract. A smoke plume, coming from an accidental fire in a textile warehouse in the north of Paris, covered a significant part of the Paris area on 17 April 2015 and seriously impacted the visibility over the megalopolis. This exceptional event was sampled with an automatic N2-Raman lidar, which operated 15 km south of Paris. The industrial pollution episode was concomitant with a long-range transport of dust aerosols raised from Sahara, and with the presence of an extended stratus cloud cover. The analysis of the ground-based lidar profiles therefore required the development of an original inversion algorithm, using a top-down aerosol optical thickness matching (TDAM) approach. This study is, to the best of our knowledge, the first lidar measurement of an accidental fire smoke plume. Vertical profiles of the aerosol extinction coefficient, depolarization and lidar ratio are derived to optically characterize the aerosols that form the plume. We found a lidar ratio close to 50 ± 10 sr for this fire smoke aerosol layer. The particle depolarization ratio is low, ~ 1 ± 0.1 %, suggesting the presence of spherical particles and therefore highly hydrated aerosols in that layer. A Monte Carlo algorithm was used to assess the uncertainties on the optical parameters, and to evaluate the TDAM algorithm.

Soft Computing Modeling of Wild Fire Risk Indices

2013 ◽  
pp. 1073-1087
Author(s):  
L. Iliadis ◽  
T. Betsidou
Keyword(s):  
Soft Computing ◽  
Forest Fire ◽  
Forest Fires ◽  
Fuzzy Inference ◽  
Research Effort ◽  
Fire Risk ◽  
Geographic Area ◽  
Burned Area ◽  
Wild Fire ◽  
Risk Indices

It is essential to find ways that can reduce the risk of devastating forest fires which have multiple negative ecological and financial consequences. This preliminary research effort focuses on the implementation of an intelligent rule based fuzzy inference system evaluating wild fire risk in the forest departments of Greece. The system uses soft computing techniques and was built in the Matlab integrated environment. The whole research is related to the wild fires in Greece during the period 1983-1997 with data coming from the general forest management service. It classifies all Greek forest departments (by assigning three labels) according to their forest fire risk due to distinct parameters. The estimation of the risk indices was done by using fuzzy triangular membership functions and Einstein fuzzy conjunction T-Norms. Moreover the system produces the profile of the forest departments located in the geographic area of “Peloponnesus.” This is a region located in the southern part of the country and it has a vast number of annual forest fire breakouts. Meteorological, topographic, and historical (total burned area and intervention time) features were considered for the determination of the risk indices. The system has shown a good performance which can be improved further if more data is gathered and used. Its main advantage is that it offers an innovative and reliable model that can be employed in any part of the world as a basis for natural disasters’ risk estimation.

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