Forest Fire Monitoring System Research Articles

Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets

Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets

Deep Learning-Based Forest Fire Risk Research on Monitoring and Early Warning Algorithms

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy.

An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework

Smoke, a byproduct of forest and grassland combustion, holds the key to precise and rapid identification—an essential breakthrough in early wildfire detection, critical for forest and grassland fire monitoring and early warning. To address the scarcity of middle–high-resolution satellite datasets for forest and grassland fire smoke, and the associated challenges in identifying smoke, the CAF_SmokeSEG dataset was constructed for smoke segmentation. The dataset was created based on GF-6 WFV smoke images of forest and grassland fire globally from 2019 to 2022. Then, an optimized segmentation algorithm, GFUNet, was proposed based on the UNet framework. Through comprehensive analysis, including method comparison, module ablation, band combination, and data transferability experiments, this study revealed that GF-6 WFV data effectively represent information related to forest and grassland fire smoke. The CAF_SmokeSEG dataset was found to be valuable for pixel-level smoke segmentation tasks. GFUNet exhibited robust smoke feature learning capability and segmentation stability. It demonstrated clear smoke area delineation, significantly outperforming UNet and other optimized methods, with an F1-Score and Jaccard coefficient of 85.50% and 75.76%, respectively. Additionally, augmenting the common spectral bands with additional bands improved the smoke segmentation accuracy, particularly shorter-wavelength bands like the coastal blue band, outperforming longer-wavelength bands such as the red-edge band. GFUNet was trained on the combination of red, green, blue, and NIR bands from common multispectral sensors. The method showed promising transferability and enabled the segmentation of smoke areas in GF-1 WFV and HJ-2A/B CCD images with comparable spatial resolution and similar bands. The integration of high spatiotemporal multispectral data like GF-6 WFV with the advanced information extraction capabilities of deep learning algorithms effectively meets the practical needs for pixel-level identification of smoke areas in forest and grassland fire scenarios. It shows promise in improving and optimizing existing forest and grassland fire monitoring systems, providing valuable decision-making support for fire monitoring and early warning systems.

Improving the organization of communication in the forest fire monitoring system

The article discusses the issues of early detection of forest fires. Currently, traditional monitoring methods are gradually being replaced by modern technologies based on automation and the use of artificial intelligence. The system of remote video monitoring “Lesohranitel”, which has been widely used in many regions of Russia, is considered in detail. The main advantages of this system and the basic principle of operation are considered. The scheme of communication organization of the considered system is presented, the main ways for its improvement are defined.

Prototype of Forest and Land Fire Monitoring and Detection System Using IoT-Based WSN Technology

Forest and land fires are a recurrent issue in several Indonesian regions, necessitating advanced technological solutions for early detection and monitoring due to their significant impact. This research project focuses on developing a prototype system utilizing IoT-based Wireless Sensor Network (WSN) technology to detect forest and land fires. The prototype offers real-time remote monitoring of temperature, humidity, smoke levels, and wind speed in the forest through an Android app or web interface. To validate its performance, the prototype was compared to existing BMKG tools, with results showing minor temperature errors (2.41%) and a humidity error of 17.68%. The anemometer sensor exhibited a slight 4-second data transmission delay. Importantly, the prototype excelled in fire detection, effectively identifying temperature and humidity changes within a 2-meter radius or more indicative of fire outbreaks. This pioneering prototype promises to significantly enhance early warning and response mechanisms for forest and land fires in Indonesia, contributing to more effective environmental conservation and disaster management.

UAV-based Forest Fire Exploration and Monitoring System

The goal of this work is to develop a system for monitoring forest fires in real time using an unmanned aerial vehicle (UAV). This study proposes a quadcopter-based forest fire monitoring system based on radio control from a ground station using a geolocation system. This approach ensures the effective use of UAVs both in automatic and manual modes with the help of onboard navigation equipment and controls. The hardware system is built using the ArduPilot controller and various sensors such as GPS, gyro compass, temperature sensor, distance sensor and digital cameras. Flight tests were carried out to assess the accuracy of the positioning system. The results show that the use of UAVs is an effective way to deal with a fire situation in addition to existing methods and technologies for solving operational problems of fire detection.

CNN Based Forest Fire Monitoring System

Abstract: As a type of natural resource, forests are vital for our existence. Wildfires are a significant threat to humans and other living beings, with the development of satellite technology it can be continuously monitored and controlled. Accurate real-time monitoring of wildfires is not only an important part of forest fire prevention, but also an important means of effectively controlling the spread of wildfires and reducing economic losses. Therefore, we need a highly mobile early forest fire warning system to ensure the safety of forest resources. In view of this, we propose a forest fire monitoring method that uses convolutional neural network to detect fire from images which can monitor the presence of fire in the forest arena.

An optimization-based approach for an integrated forest fire monitoring system with multiple technologies and surveillance drones

Wildfires pose a significant threat to forests and the delicate natural balance. In this work, we present a novel optimization approach for designing forest fire monitoring systems that incorporate surveillance towers, monitoring balloons, and drone technology, which have gained popularity in recent years for monitoring, logistics, and civil applications. We develop a compact mixed-integer linear programming model that includes location decisions for multiple monitoring technologies and routing of drones among monitoring facilities to achieve the most extensive possible terrain coverage. Additionally, we propose a matheuristic algorithm that comprises six components: a solution procedure, perturbation procedures, local search procedures, a call to the general-purpose solver to solve a mixed-integer linear programming model, a global reset strategy, a local reset strategy, and an acceptance criterion. We test the proposed model and algorithm on a set of random instances and a real-life case study in Chile. While the proposed model can only solve small instances, the matheuristic can find good-quality solutions for all instances. This study can aid the government and private sector in designing an integrated fire monitoring system that leverages watchtowers, monitoring balloons, and drones.

Incorporating User Experience Evaluation into Application Design for Optimal Usability

Forest and land fires have become a national issue every year, especially in West Kalimantan. From 2015 to 2020, around 331,268.35 hectares of forest and land were burned in West Kalimantan. As a result of forest and land fires, the haze disrupts public health, the economy, and river, land, sea, and air transportation. As anticipation and prevention, the community and government monitor forest and land fires using the Forest Fire Monitoring System Application. The purpose of this study was to the User Experience (UX) evaluation for design improvement in the Forest Fire Monitoring System Application (SIPONGI) inWest Kalimantan. The method used was User Centered Design (UCD) and Website Usability Evaluation Tool (WEBUSE) to provide new design solutions in the form of a website prototype. The research methodology included a literature study of the SIPONGI application. The study used a sample of 25 respondents with different work backgrounds to represent the population using the SIPONGI application. The results of this study showed that usability points per attribute and category are superior after making UI/UX improvements using the UCD process in prototype form. In conclusion, using the UCD method is better if it is accompanied by the WEBUSE method in improving the design of an application.

3D Drone Mapping

A major portion of the earth remains unexplored today. There are many reasons for this. One of them is because those places are or maybe dangerous for the explorers. Also, Nepal and many other under-developed countries buy satellite images and maps of their territory from other countries. The problem with these maps is that they are not always up-to-date and are very low-resolution images. In order to solve these problems, this research work has designed a quadcopter (four propellers) drone with HD camera. This device is capable of capturing images with colour, intensity, and depth values at each pixel so that a 3D map can be constructed. Unmanned Aerial Vehicles (UAVs) also called Drones can be designed for numerous objectives and tasks. This article intends to design a drone capable of creating its own pathway and taking flight autonomously. During the flight, the UAV is programmed to capture high-resolution images at regular intervals with 60-80% overlap. The overlap between images is highly necessary to obtain numerous data of the same point, which will be beneficial in creating a finer 3D map. The 3D map is generated by using a process called photogrammetry, which takes pictures as input and displays a 3D map as output by utilizing the data present in pictures. This drone can be used to capture pictures and create 3D maps of unexplored areas like caves, volcano craters, etc. Also, high-resolution pictures can be merged and uploaded for satellite imaging applications. Besides, the same vehicle can be used for many other applications like forest fire monitoring systems, endangered wildlife monitoring, surveying, and so on with slight alterations.

A FOREST FIRE IDENTIFICATION METHOD FOR UNMANNED AERIAL VEHICLE MONITORING VIDEO IMAGES

An automatic forest fire monitoring system based on UAV (unmanned aerial vehicle) acquired video images was studied in this paper. This novel method was proposed to address current problems in forest fire information monitoring practices such as poor real-time performance and low efficiency. Besides, it aims to realize the dynamic monitoring of forest fires in wild environment. In this paper, a forest fire monitoring method based on active analysis of UAV-acquired video image features is proposed to automatically detect and identify the occurrence of forest fires. The motion detection method based on dense optical flow and background modeling method were used to extract the motion regions for eliminating the influence of image background. By using wavelet energy feature and texture feature, 9 video images acquired by multi-rotor UAV on forest fire monitoring were selected as sample images (8 images for experiment and 1 image for contrast purpose). The mean values and standard deviations of the gray level co-occurrence matrix eigen values (angular second moment, entropy moment and reciprocal differential moment) were calculated as the discriminate basis for identifying forest fires. The experimental results showed that the proposed algorithm can effectively identify the forest fire, which provides a theoretical guarantee for the forest resources protection.

High-Precision Real-Time Forest Fire Video Detection Using One-Class Model

Machine learning-base fire detection methods play a vital role in the current forest fire monitoring systems. In the literature, fire detection is usually viewed as a two-class (or multi-class) classification task. However, this violates the most foundational hypothesis in machine learning, e.g., independent and identical distribution (i.i.d.), especially for the non-fire samples drawn from a complex forest background. Moreover, for omni-directional video -monitoring, the background is also always changing, which leads this violation to a worse situation. In this work, by relaxing the i.i.d. of non-fire samples, we aim to learn a one-class model that just relies on the fire samples. Considering the requirements of high-precision and real-time detection, training samples are directly constructed on the fire pixels without a complex feature transformation. Additionally, we also provide a batch decision-making strategy to speed up fire detection. This work also includes an extensive experimental comparison on the public forest fire videos, obtained by ground- or unmanned aerial vehicle (UAV)-monitoring cameras. Compared with the state-of-the-art methods, the results show the superiority of our proposal in terms of a high-fire detection rate, low-error warning rate, accurate fire location positioning, and real-time detection.

A forest fire monitoring and detection system based on wireless sensor networks

Global climate change is affecting Earth in various ways, including glacial detachment, sea-level rise, and more intense heat waves, which in turn contribute to fires in areas rich in flora and fauna, causing erosion and changes in plant and animal ranges. Despite international efforts to mitigate climate change, the problem remains uncontrolled in many areas. In this study, a forest fire monitoring and early warning system is proposed. It is based on a wireless sensor network (WSN) that measures the environment to detect and prevent fires in rural areas. The WSN uses open-access technologies and the Zigbee standard for radiofrequency communication and sends collected data to a web server via GPRS and TCP/IP protocol. The WSN's performance is evaluated using two metrics: data transmission rate and time delay. The WSN achieved an average success rate of data transmission greater than 86% with an average time delay of less than 500 milliseconds in all tests, demonstrating the potential of WSNs as near real-time forest fire detection systems.

Assessment of forest fire effects based on automated processing of Earth remote sensing imager

The article presents a study of the automated detection specifics within forest-covered areas traversed by fires based on the different time satellite imagery from the Sentinel-2A and Sentinel-2B using the differential normalised burn ratio index (dNBR) for the pre-fire and post-fire periods the calculation. The studies carried out on the research topic are given and a review of the currently functioning forest fire monitoring systems has been implemented. The urgency of the development and testing of an automated system for assessing the forest fire consequences using open source software and Earth remote sensing data has been substantiated. It has been established that the differential index dNBR, calculated from the Sentinel-2A and Sentinel-2B satellite images captured on different dates makes it possible to effectively detect burned-out areas. It is shown that the Python ecosystem makes it possible to successfully create systems for automated processing of Earth remote sensing data. A prototype of a system for the automated detection of forest-covered areas traversed by fires has been developed, based on the materials of different dates satellite imagery from Sentinel-2A and Sentinel-2B spacecraft. The flowchart of the algorithm of processing Earth remote sensing data using the proposed system was presented. For the Sentinel-2 satellite images for the dates before and after the fire, the differential index dNBR was calculated, the analysis of the results of which showed a close correlation of the dNBR index with the degree of burnout of the territory. A schematic map of the areas affected by the fire has been drawn up and the accuracy of identifying burnedout areas has been assessed by calculating the confusion matrix. An assessment of the effectiveness of the automated system for identifying areas affected by forest fires, ways of its modernisation and improvement, as well as the prospects for implementation in production has been carried out. It is noted that the results of the created system have high reliability indicators. At the same time, the need was revealed to increase the sensitivity of the system when identifying areas that have undergone partial burnout. A variant of improving the algorithms used in the work by introducing the multilevel Otsu’s method, intended to significantly increase the sensitivity of the system, has been proposed.

Design and Implementation of LoRa-Based Forest Fire Monitoring System

One of the great disasters on earth is forest fires. Attempts to detect disaster events have been made with the help of monitoring technology. However, the problem is that the sensor is less responsive to detecting the presence of fire. Furthermore, sending information about fire incidents throughout the forest cannot use the existing communication platform. Therefore, we designed a forest fire monitoring system using LoRa. This technology is based on wireless which can transmit data across the forest. To detect the presence of fire, Arduino Uno is used as a microcontroller that regulates input from the AMG8833 sensor and GPS Ubox 6M. The experiment shows that the AMG8833 sensor is more sensitive in detecting the presence of fire as the catch range changes between 3 to 10 meters. In that distance range, hotspots were detected 19.25 oC to 122.5 oC when testing the sensor node is done. The monitoring system developed in this study demonstrated that sensor nodes and gateways could communicate up to 500 meters apart with a signal quality of -134 dBm. The best LoRa configuration mode for this communication capability is a Bandwidth of 250, a Code Rate of 4/5, and a Spread Factor of 10.One of the great disasters on earth is forest fires. Attempts to detect disaster events have been made with the help of monitoring technology. However, the problem is that the sensor is less responsive to detecting the presence of fire. Furthermore, sending information about fire incidents throughout the forest cannot use the existing communication platform. Therefore, we designed a forest fire monitoring system using LoRa. This technology is based on wireless which can transmit data across the forest. To detect the presence of fire, Arduino Uno is used as a microcontroller that regulates input from the AMG8833 sensor and GPS Ubox 6M. The experiment shows that the AMG8833 sensor is more sensitive in detecting the presence of fire as the catch range changes between 3 to 10 meters. In that distance range, hotspots were detected 19.25 oC to 122.5 oC when testing the sensor node is done. The monitoring system developed in this study demonstrated that sensor nodes and gateways could communicate up to 500 meters apart with a signal quality of -134 dBm. The best LoRa configuration mode for this communication capability is a Bandwidth of 250, a Code Rate of 4/5, and a Spread Factor of 10.

Novel Detection of Forest Fire Using Satellite Observation with Improved Accuracy in Comparison with Human Observation

The objective of the study is to detect the forest fire by monitoring forest area through satellites by comparing human observation and satellite observation and to prevent the forest fire and to state which one is best for forest fire detection. Materials and Methods: A total of 28 samples are taken randomly from Google. These are my own datasets. These samples are divided into group 1 (n = 14) and group 2 (n = 14). This required samples for this analysis is given using G power calculation. Minimum power of the analysis is fixed as 0.8 and maximum accepted error is fixed as 0.5. Result: Through the forest fire monitoring system, the detection of forest fire using the images taken from satellite even from the low resolution images appeared to have an improved performance of the system. The system appears to have an accuracy of 78%. The sigma value is 0.315. Conclusion: This innovative approach appears to be better accuracy in detecting the occurrence of forest fire.

Wireless Sensor Network based Forest Fire Early Detection Systems: Development and Implementation

The article is devoted to the development of a monitoring system based on wireless sensor networks. The purpose of the article is to implement a system for environmental monitoring using wireless sensor networks, as well as to solve the problem of covering the area with this network. Wireless sensor networks and their involvement in environmental monitoring are investigated and interactions between wireless networks and data communication protocols are described. The monitoring system is considered and analyzed. An experiment is conducted in which it has been checked at what maximum distance the temperature parameters can be transmitted, the experiment is conducted with and without interference. Routing algorithms for how they work and how parameters (temperature) are transmitted from sensors to the server are considered. A terrestrial monitoring system layout for environmental monitoring is investigated and developed based on Zigbee wireless sensor network construction technology, given that it can be added to this network new units or their replacement. The Mesh topology is selected and the separate physical devices that are the nodes of this network are identified. Nodes are also designed, using modules from Digi called XBee as a base station for Arduino sensors and as a ZigBee data base. These nodes are investigated for noise immunity and stability using flame, smoke and temperature sensors. The study found that XBee nodes are very unstable working near Wi-Fi routers and interfering in the form of forest. The conclusion about the result of the experiment is formulated, as well as how the system can be further upgraded, which can be added and what parameters can be observed. The proposed method for the location of the ground forest fire monitoring system’s equipment ensures full control and the flame detection preciseness.

Design and implementation of forest fire monitoring system based on internet of things technology

In view of the frequent forest fire problem, this design uses the Internet of Things technology to design a forest fire monitoring system. This system is composed of data acquisition module, data processing module, display module, locating module and alarm module. The data collection module is responsible for collecting forest temperature values and concentration values of smoke. The collected data is processed by the data processing module, and the processed data is displayed in real time by the display module. When the collected temperature or smoke concentration exceeds the thresholds, the GPS locating module is activated to locate the event. Then, the forest temperature value, smoke value exceeding the threshold and location information are sent to the manager’ s mobile phone in the form of SMS through the Sim800 SMS alarm module for alarm. After experiment, the accuracy of temperature module in this design is ±2°C; The accuracy of smoke density module is ±5Pm; The accuracy of location is ±100m. This design has the characteristics of small size, easy placement, large monitoring range, low cost and high sensitivity, which is suitable for further popularization and application.

Application of Deep Learning in Forest Fire Smoke Identification Monitoring System

In order to realize the goal of "early, small and small" of forest fire and eliminate fire in the bud, a forest fire smoke identification and monitoring system based on deep learning is designed. Through real-time image processing and deep learning target recognition, the fast forest fire and smoke identification within the monitoring range is realized, and the information can be conveyed to the fire prevention headquarters in the first time after the fire occurs, so as to conduct rapid emergency response.

Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery

Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire monitoring, this paper proposes a forest fire monitoring system based on drones and deep learning. The proposed system aims to solve the shortcomings of traditional forest fire monitoring systems, such as blind spots, poor real-time performance, expensive operational costs, and large resource consumption. The image processing techniques are used to determine whether the frame returned by a drone contains fire. This process is accomplished in real time, and the resultant information is used to decide whether a rescue operation is needed. The proposed method has simple operations, high operating efficiency, and low operating cost. The experimental results indicate that the relative accuracy of the proposed algorithm is 81.97%. In addition, the proposed technique provides a digital ability to monitor forest fires in real time effectively. Thus, it can assist in avoiding fire-related disasters and can significantly reduce the labor and other costs of forest fire disaster prevention and suppression.