scholarly journals Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 128
Author(s):  
Aqsa Tehseen ◽  
Nazir Ahmad Zafar ◽  
Tariq Ali ◽  
Fatima Jameel ◽  
Eman H. Alkhammash
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Formal Model ◽  
Human Life ◽  
Fire Detection ◽  
Natural World ◽  
Vital Role ◽  
Human Beings ◽  
Control Room ◽  
Forest Fire Detection

Forests are an enduring component of the natural world and perform a vital role in protecting the environment. Forests are valuable resources to control global warming and provide oxygen for the survival of human life, including wood for households. Forest fires have recently emerged as a major threat to biological processes and the ecosystem. Unfortunately, almost every year, fire damages millions of hectares of forest land due to late and inefficient detection of fire. However, it is important to identify the forest fire at the initial level before it spreads to vast areas and destroys natural resources. In this paper, a formal model of the Internet of Things (IoT) and drone-based forest fire detection and counteraction system is presented. The proposed system comprises network maintenance. Sensor deployment is on trees, the ground, and animals in the form of subnets to transmit sensed data to the control room. All subnets are connected to the control room through gateway nodes. Alarms are being used to alert human beings and animals to save their lives, which will help to initially protect them from fire. The embedded sensors collect the information and transfer it to the gateways. Drones are being used for real-time visualization of fire-affected areas and to perform actions to control fires because they play a vital role in disasters. Graph theory is used to construct an efficient model and to show the connectivity of the network. To identify failures and develop recovery procedures, the algorithm is designed through the graph-based model. The model is developed by the Vienna Development Method-Specification Language (VDM-SL), and the correctness of the model is ensured using various VDM-SL toolbox facilities.

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.

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 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 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.

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.

scholarly journals Forest Fire Detection System using Zig Bee

2020 ◽  
Vol 9 (6) ◽  
pp. 15-17
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Disaster Response ◽  
Detection System ◽  
Environmental Parameters ◽  
Fire Detection ◽  
Fire Departments ◽  
Average Temperature ◽  
Response Capacity ◽  
Forest Fire Detection

The forest fires are alone not a threat to the environment but this add on multiple factors which are threat to the environment few examples are decreasing of ground water level, landslides, global warming. These are dangerous for the animal species and few vulnerable species. In recent years few a case has been heard of forest fires in Amazon, California which damaged the flora and fauna. Scientist have also predicted that by end of twentieth century the average temperature of earth will rise by 4.4 degree Celsius. The forest fire detection will give strength to the disaster response capacity. Environmental parameters such as forest temperature and humidity can be tracked in real time. From the information gathered by the system, wildfire fighting or fire prevention decisions can be taken as quickly as possible by the respective fire departments.

scholarly journals Forest Fire Detection System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1138-1142
Keyword(s):  
Real Time ◽  
Forest Fire ◽  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Wildlife Habitat ◽  
Support Vector ◽  
Time Data ◽  
Temporal Segmentation ◽  
Forest Fire Detection

The forest is one of the most important wealth of every country. The forest fires destroys the wildlife habitat, damages the environment, affects the climate, spoils the biological properties of the soil, etc. So the forest fire detection is a major issue in the present decade. At the same time the forest fire have to be detected as fast as possible. In the proposed method, a color spatial segmentation, temporal segmentation, global motion compensation, Support Vector Machine (SVM) classifications are used to detect the fire and to segment the fire from the video sequence. The method is implemented over the two real time data sets. The proposed method is most suitable for segmenting fire events over unconstrained videos in real time.

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.

Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires

2020 ◽  
Vol 149 ◽  
pp. 1-16 ◽  
Author(s):  
S. Sudhakar ◽  
V. Vijayakumar ◽  
C. Sathiya Kumar ◽  
V. Priya ◽  
Logesh Ravi ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
False Alarms ◽  
Aerial Vehicle ◽  
Forest Fire Detection

scholarly journals Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

2019 ◽  
Vol 11 (3) ◽  
pp. 271 ◽  
Author(s):  
Eunna Jang ◽  
Yoojin Kang ◽  
Jungho Im ◽  
Dong-Won Lee ◽  
Jongmin Yoon ◽  
...  
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Forest Fire ◽  
Satellite Data ◽  
Forest Fires ◽  
Fire Detection ◽  
Geostationary Satellite ◽  
False Alarms ◽  
Post Processing ◽  
Forest Fire Detection

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50–60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

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