scholarly journals IMPROVEMENT OF FOREST FIRE DETECTION ALGORITHM USING BRIGHTNESS TEMPERATURE LAPSE RATE CORRECTION IN HIMAWARI-8 IR CHANNELS: APPLICATION TO THE 6 MAY 2017 SAMCHEOK CITY, KOREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1353-1354
Author(s):  
S. H. Park ◽  
W. Park ◽  
H. S. Jung
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Continuous Monitoring ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Time Of Day ◽  
Lapse Rate ◽  
Mountainous Areas ◽  
Temperature Lapse Rate ◽  
Forest Fire Detection

Forest fires are a major natural disaster that destroys a forest area and a natural environment. In order to minimize the damage caused by the forest fire, it is necessary to know the location and the time of day and continuous monitoring is required until fire is fully put out. We have tried to improve the forest fire detection algorithm by using a method to reduce the variability of surrounding pixels. We focused that forest areas of East Asia, part of the Himawari-8 AHI coverage, are mostly located in mountainous areas. The proposed method was applied to the forest fire detection in Samcheok city, Korea on May 6 to 10, 2017.

scholarly journals Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Mubarak A. I. Mahmoud ◽  
Honge Ren
Keyword(s):  
Temporal Variation ◽  
Forest Fire ◽  
Forest Fires ◽  
Color Space ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Image Processing Algorithm ◽  
Data Set ◽  
Detection Rates ◽  
Forest Fire Detection

Forest fires represent a real threat to human lives, ecological systems, and infrastructure. Many commercial fire detection sensor systems exist, but all of them are difficult to apply at large open spaces like forests because of their response delay, necessary maintenance needed, high cost, and other problems. In this paper a forest fire detection algorithm is proposed, and it consists of the following stages. Firstly, background subtraction is applied to movement containing region detection. Secondly, converting the segmented moving regions from RGB to YCbCr color space and applying five fire detection rules for separating candidate fire pixels were undertaken. Finally, temporal variation is then employed to differentiate between fire and fire-color objects. The proposed method is tested using data set consisting of 6 videos collected from Internet. The final results show that the proposed method achieves up to 96.63% of true detection rates. These results indicate that the proposed method is accurate and can be used in automatic forest fire-alarm systems.

scholarly journals Aerial Forest Fire Detection and Monitoring Using a Small UAV

2020 ◽  
Author(s):  
Joaquim Vasconcelos Reinolds de Sousa ◽  
Pedro Vieira Gamboa
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
The Body ◽  
Fire Fighting ◽  
Aerial Images ◽  
North East ◽  
Forest Fire Detection

In recent years, large patches of forest have been destroyed by fires, bringing tragic consequences for the environment and small settlements established around these regions. In this context, it is essential that fire fighting teams possess an increased situational awareness about the fire propagation, in order to promptly act in the extinguishing process. Recent advances in UAV technology allied with remote sensing and computer vision techniques show very promising UAVs applicability in forest fires detection and monitoring. Besides presenting lower operational costs, these vehicles are able to reach regions that are inaccessible or considered too dangerous for fire fighting crews operations. This paper describes the application of a real-time forest fire detection algorithm using aerial images captured by a video camera onboard    an Unmanned Aerial Vehicle (UAV). The forest fire detection algorithm consists of a rule-based colour model that uses both RGB and YCbCr colour spaces to identify fire pixels. An intuitive targeting system was also developed, allowing the detection of multiple fires at the same time. Additionally, a fire geolocation algorithm was developed in order to estimate the fire location in terms of latitude (φ),  longitude     (λ) and altitude (h). The geolocation algorithm consists of applying two coordinates systems transformations between the body-fixed frame, North-East-Down frame (NED) and Earth-Centered, Earth Fixed (ECEF) frame. Flight tests were performed during  a controlled burn in order to assess the fire detection algorithm performance. The algorithm was able to detect the fire with few false positive detections. Keywords: Aerial fire detection algorithm, Aerial fire monitoring, Forest fire, UAV, Remote sensing

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 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.

Forest Fire Detection Algorithm Based on Digital Image

2013 ◽  
Vol 8 (8) ◽  
Author(s):  
Rui Chen ◽  
Yuanyuan Luo ◽  
Mohanmad Reza Alsharif
Keyword(s):  
Forest Fire ◽  
Digital Image ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Forest Fire Detection

scholarly journals An Iot Based Forest Fire Detection using Raspberry Pi

2019 ◽  
Vol 8 (4) ◽  
pp. 9126-9132
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Early Stage ◽  
Fire Detection ◽  
Base Station ◽  
Raspberry Pi ◽  
Alert Message ◽  
Forest Fire Detection ◽  
Monitoring Stations

As we all know forests are the main source of oxygen and its protection is essential to sustain the human and animal race. Since we all learnt about the necessity of air, yet we lack at taking measures to protect our mother forest. Forest Fires are the main reason for the deforestation and destruction of trees and wildlife. Forest Fires are due to these two ways either by man-made or naturally caused. In either way we have to pay for the loss occurred because we have left with only certain area for the forest. So, we have to take measures to prevent forest fire at its early stage. The main aim of our project is to design and implement an IoT based hardware module that could detect the fire and prevent it by alerting the monitoring stations with an alert message and also provides location to the nearest base station. An automatic message will be sent to the nearest base station in addition to these, it has a 360 degrees rotation camera which helps to provide continuous surveillance. We can rotate the camera in any direction from the base station itself. A buzzer that alarms when the incident is happening and a water motor, this water motor will be on automatically. We can also find location where the incident is taking place with the help of Wi-Fi module. This device helps in identifying the fire at its early stage and helps in the prevention of spread all over the forest.

scholarly journals Automatic Forest Fire Detection and Suppression Smart Grid System

2021 ◽  
pp. 666-669
Author(s):  
Gopalakrishnan G ◽  
Arul Mozhi Varman S ◽  
Dinessh T C ◽  
Divayarupa S ◽  
Benazir Begam R
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Radical Change ◽  
Climatic Conditions ◽  
Grid System ◽  
The Past ◽  
Initial Stage ◽  
Smart Grid System ◽  
Forest Fire Detection

Over the past years, a radical change in Earth’s temperature has been recorded. It has caused global warming and severe changes in climatic conditions. Naturally, this has resulted in many natural disasters. Forest fire is one such calamity that harms the environment to a great extent. The traditional methods of controlling and suppressing the fires are ineffective as the fires spread too rapidly if it is not contained at the initial stage. Hence this paper proposes a system that aims to automatically detect forest fires and suppress them. This system will suppress and contain the forest fires long enough for the firefighters to arrive.

A Forest Fire Detection Algorithm Based on Sea Computing and Visual Saliency

2014 ◽  
Vol 12 (1) ◽  
pp. 129-135 ◽  
Author(s):  
Juan Juan Zhao ◽  
Quan Wang ◽  
Yan Qiang
Keyword(s):  
Forest Fire ◽  
Fire Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Forest Fire Detection

scholarly journals A Forest Fire Detection System Based on Ensemble Learning

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 217
Author(s):  
Renjie Xu ◽  
Haifeng Lin ◽  
Kangjie Lu ◽  
Lin Cao ◽  
Yunfei Liu
Keyword(s):  
Ensemble Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Ground Truth ◽  
False Positives ◽  
Learning Technology ◽  
Global Information ◽  
Forest Fire Detection

Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.

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