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 Early smoke detection of forest fires based on SVM image segmentation

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 150-159
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Smoke Detection ◽  
Large Space ◽  
Merging Algorithm ◽  
Detection Technology

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

scholarly journals New products for a better characterisation of smoke plume and gas/aerosol dispersion from boreal eurasian forest fires: the ALANIS Smoke Plume project

2011 ◽  
Vol 8 (5) ◽  
pp. 9747-9761
Author(s):  
J.-P. Muller ◽  
V. Yershov ◽  
D. Fisher ◽  
M. Krol ◽  
W. Peters ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Trace Gases ◽  
New Products ◽  
Smoke Plume ◽  
Fire Emissions ◽  
Smoke Plumes ◽  
Aerosol Dispersion ◽  
Burnt Areas ◽  
Integrated Study

Abstract. The ALANIS (Atmosphere-LANd Integrated Study) Smoke Plume project is an on-going study funded by the ESA's Support to Science Element (STSE) dedicated to the monitoring of the fire aerosol and trace gases dispersion over Eurasia from multi-mission EO-based data, in link with the scientific issues of land-atmosphere processes in the iLEAPS community. The injection and dispersion of the smoke plumes are performed with the TM5 model from several new products (burnt areas and forest fire emissions amounts, smoke plumes injection heights) derived from the MERIS and AATSR products and from the validated IASI CO products. A first study focused on the Russian wildfire events of the summer of 2010 has shown the potential of the European missions to assess the forest fire emissions and the aerosols/gases injection and transport over Eurasia. The release of the integrated model, including the new products still under development, is planned for the summer of 2011.

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.

Eye-safe lidar measurements for detection and investigation of forest-fire smoke

2004 ◽  
Vol 13 (4) ◽  
pp. 401 ◽  
Author(s):  
Andrei B. Utkin ◽  
Armando Fernandes ◽  
Alexander Lavrov ◽  
Rui Vilar
Keyword(s):  
Laser Beam ◽  
Wildland Fire ◽  
3D Structure ◽  
Fire Detection ◽  
Layer By Layer ◽  
Topographic Map ◽  
Safety Measures ◽  
Smoke Plumes ◽  
Lidar Measurements ◽  
Fire Smoke

The problem of eye safety in lidar-assisted wildland fire detection and investigation is considered as a problem of reduction of the hazard range within which the laser beam is dangerous for direct eye exposure. The dependence of this hazard range on the lidar characteristics is examined and possible eye-safety measures discussed. The potential of one of the cheapest ways of providing eye safety, which is based on placing the lidar in an elevated position and using a 1064-nm laser beam with increased divergence, is also investigated experimentally. It is demonstrated that a lidar system operating with wider beams maintains its ability to detect smoke plumes efficiently. Providing eye-safe conditions allows scanning of the internal 3D structure of smoke plumes in the vicinity of fire plots. Examples are given as layer-by-layer smoke concentration plots on the topographic map.

scholarly journals Computer Vision-Based Wildfire Smoke Detection Using UAVs

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ehab Ur Rahman ◽  
Muhammad Asghar Khan ◽  
Fahad Algarni ◽  
Yihong Zhang ◽  
M. Irfan Uddin ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Similarity Index ◽  
Structural Similarity ◽  
Smoke Detection ◽  
Wildfire Smoke ◽  
Detection And Tracking ◽  
Fire Smoke ◽  
Feature Extractor ◽  
Different Sources

This paper presents a new methodology based on texture and color for the detection and monitoring of different sources of forest fire smoke using unmanned aerial vehicles (UAVs). A novel dataset has been gathered comprised of thin smoke and dense smoke generated from the dry leaves on the floor of the forest, which is a source of igniting forest fires. A classification task has been done by training a feature extractor to check the feasibility of the proposed dataset. A meta-architecture is trained above the feature extractor to check the dataset viability for smoke detection and tracking. Results have been obtained by implementing the proposed methodology on forest fire smoke images, smoke videos taken on a stand by the camera, and real-time UAV footages. A microaverage F1-score of 0.865 has been achieved with different test videos. An F1-score of 0.870 has been achieved on real UAV footage of wildfire smoke. The structural similarity index has been used to show some of the difficulties encountered in smoke detection, along with examples.

scholarly journals Biomass-burning smoke heights over the Amazon observed from space

2019 ◽  
Vol 19 (3) ◽  
pp. 1685-1702 ◽  
Author(s):  
Laura Gonzalez-Alonso ◽  
Maria Val Martin ◽  
Ralph A. Kahn
Keyword(s):  
Vertical Distribution ◽  
Tropical Forest ◽  
Biomass Burning ◽  
Forest Fires ◽  
Atmospheric Stability ◽  
Free Troposphere ◽  
Smoke Plume ◽  
Smoke Plumes ◽  
Drought Conditions ◽  
The Vertical Distribution

Abstract. We characterise the vertical distribution of biomass-burning emissions across the Amazon during the biomass-burning season (July–November) with an extensive climatology of smoke plumes derived from MISR and MODIS (2005–2012) and CALIOP (2006–2012) observations. Smoke plume heights exhibit substantial variability, spanning a few hundred metres up to 6 km above the terrain. However, the majority of the smoke is located at altitudes below 2.5 km. About 60 % of smoke plumes are observed in drought years, 40 %–50 % at the peak month of the burning season (September) and 94 % over tropical forest and savanna regions, with respect to the total number of smoke plume observations. At the time of the MISR observations (10:00–11:00 LT), the highest plumes are detected over grassland fires (with an averaged maximum plume height of ∼1100 m) and the lowest plumes occur over tropical forest fires (∼800 m). A similar pattern is found later in the day (14:00–15:00 LT) with CALIOP, although at higher altitudes (2300 m grassland vs. 2000 m tropical forest), as CALIOP typically detects smoke at higher altitudes due to its later overpass time, associated with a deeper planetary boundary layer, possibly more energetic fires, and greater sensitivity to thin aerosol layers. On average, 3 %–20 % of the fires inject smoke into the free troposphere; this percentage tends to increase toward the end of the burning season (November: 15 %–40 %). We find a well-defined seasonal cycle between MISR plume heights, MODIS fire radiative power and atmospheric stability across the main biomes of the Amazon, with higher smoke plumes, more intense fires and reduced atmospheric stability conditions toward the end of the burning season. Lower smoke plume heights are detected during drought (800 m) compared to non-drought (1100 m) conditions, in particular over tropical forest and savanna fires. Drought conditions favour understory fires over tropical forest, which tend to produce smouldering combustion and low smoke injection heights. Droughts also seem to favour deeper boundary layers and the percentage of smoke plumes that reach the free troposphere is lower during these dry conditions. Consistent with previous studies, the MISR mid-visible aerosol optical depth demonstrates that smoke makes a significant contribution to the total aerosol loading over the Amazon, which in combination with lower injection heights in drought periods has important implications for air quality. This work highlights the importance of biome type, fire properties and atmospheric and drought conditions for plume dynamics and smoke loading. In addition, our study demonstrates the value of combining observations of MISR and CALIOP constraints on the vertical distribution of smoke from biomass burning over the Amazon.

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 A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1992 ◽  
Author(s):  
Zixi Xie ◽  
Weiguo Song ◽  
Rui Ba ◽  
Xiaolian Li ◽  
Long Xia
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Forest Fire ◽  
Forest Fires ◽  
Spatial Information ◽  
Fire Detection ◽  
High Temporal Resolution ◽  
Detection Accuracy ◽  
Contextual Model ◽  
Forest Fire Detection

Two of the main remote sensing data resources for forest fire detection have significant drawbacks: geostationary Earth Observation (EO) satellites have high temporal resolution but low spatial resolution, whereas Polar-orbiting systems have high spatial resolution but low temporal resolution. Therefore, the existing forest fire detection algorithms that are based on a single one of these two systems have only exploited temporal or spatial information independently. There are no approaches yet that have effectively merged spatial and temporal characteristics to detect forest fires. This paper fills this gap by presenting a spatiotemporal contextual model (STCM) that fully exploits geostationary data’s spatial and temporal dimensions based on the data from Himawari-8 Satellite. We used an improved robust fitting algorithm to model each pixel’s diurnal temperature cycles (DTC) in the middle and long infrared bands. For each pixel, a Kalman filter was used to blend the DTC to estimate the true background brightness temperature. Subsequently, we utilized the Otsu method to identify the fire after using an MVC (maximum value month composite of NDVI) threshold to test which areas have enough fuel to support such events. Finally, we used a continuous timeslot test to correct the fire detection results. The proposed algorithm was applied to four fire cases in East Asia and Australia in 2016. A comparison of detection results between MODIS Terra and Aqua active fire products (MOD14 and MYD14) demonstrated that the proposed algorithm from this paper effectively analyzed the spatiotemporal information contained in multi-temporal remotely sensed data. In addition, this new forest fire detection method can lead to higher detection accuracy than the traditional contextual and temporal algorithms. By developing algorithms that are based on AHI measurements to meet the requirement to detect forest fires promptly and accurately, this paper assists both emergency responders and the general public to mitigate the damage of forest fires.

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