Advancements in Forest Fire Prevention: A Comprehensive Survey

Carta, Francesco; Zidda, Chiara; Putzu, Martina; Loru, Daniele; Anedda, Matteo; Giusto, Daniele

doi:10.3390/s23146635

Cited by 27 publications

(11 citation statements)

References 119 publications

(145 reference statements)

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“…A comprehensive survey of fire forest detection was presented in [31], where the authors recently highlighted the strengths, limitations, and future developments of fire detection systems. More than 100 papers have been described in [31], which direct attention to existing works and open problems. Advancements in fire detection systems are the result of the Internet of Things (IoT), deep learning, and big data analytics.…”

Section: Related Work Early Wildfire Surveillancementioning

confidence: 99%

Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance

Duangsuwan,

Klubsuwan

2023

Forests

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Open burning is the main factor contributing to the occurrence of wildfires in Thailand, which every year result in forest fires and air pollution. Open burning has become the natural disaster that threatens wildlands and forest resources the most. Traditional firefighting systems, which are based on ground crew inspection, have several limits and dangerous risks. Aerial imagery technologies have become one of the most important tools to prevent wildfires, especially drone real-time monitoring for wildfire surveillance. This paper presents an accuracy assessment of drone real-time open burning imagery detection (Dr-TOBID) to detect smoke and burning as a framework for a deep learning-based object detection method using a combination of the YOLOv5 detector and a lightweight version of the long short-term memory (LSTM) classifier. The Dr-TOBID framework was designed using OpenCV, YOLOv5, TensorFlow, LebelImg, and Pycharm and wirelessly connected via live stream on open broadcaster software (OBS). The datasets were separated by 80% for training and 20% for testing. The resulting assessment considered the conditions of the drone’s altitudes, ranges, and red-green-black (RGB) mode in daytime and nighttime. The accuracy, precision, recall, and F1-Score are shown for the evaluation metrics. The quantitative results show that the accuracy of Dr-TOBID successfully detected open burning monitoring, smoke, and burning characteristics, where the average F1-score was 80.6% for smoke detection in the daytime, 82.5% for burning detection in the daytime, 77.9% for smoke detection at nighttime, and 81.9% for burning detection at nighttime.

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Section: Related Work Early Wildfire Surveillancementioning

confidence: 99%

Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance

Duangsuwan,

Klubsuwan

2023

Forests

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“…Every year, the world witnesses new, devastating wildfires. These forest fires present significant hazards to human lives, wildlife, and global vegetation [1]. Forest fires can be categorized into two primary types: surface and crown fires.…”

Section: Introductionmentioning

confidence: 99%

Real-time forest fire detection, monitoring, and alert system using Arduino

Ikhwan Mohd Anuar,

Mohamad,

Munira Markom

et al. 2024

IJEECS

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<div align="center"><span>Early fire detection is critical to protecting forests from wildfires and enabling rapid responses to minimize fire spread. Existing forest fire detection methods cannot quickly detect forest fires and evaluate the fire risk of these sensitive areas. Hence, this research aims to develop a real-time forest fire detection, monitoring, and alert system. The development of the</span><span> system started with assembling temperature and humidity sensors, a smoke sensor, an Arduino microcontroller, and a wireless fidelity module. Then, a fire monitoring and alert system was developed using Blynk. From the sensitivity flame sensor analysis with the fire, the flame sensor detected the <span>presence of fire up to 60 cm. The sensor also indicated high temperature (45 °C)</span> and low humidity (53.4%) at noon. Low temperature (29 ℃) and high humidity (88.4%) were identified in the morning. Moreover, the highest carbon dioxide (CO<sub>2</sub>) concentration of 1,800 ppm was recorded when the smoke from the fire was detected. The global positioning system module shows the accurate real-time location of the system displayed in the Blynk application. In conclusion, this system can detect and monitor early forest fires in real-time and can alert the authorities to protect forests from wildfires.</span></div>

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“…El impacto creciente del cambio climático ha incrementado la frecuencia y severidad de los incendios forestales, lo que representa un riesgo significativo para los ecosistemas y comunidades, haciendo imprescindibles sistemas de detección temprana eficientes (Wasserman & Mueller, 2023). Dichos sistemas son claves para alcanzar unos Objetivos de Desarrollo Sostenible de la ONU, especialmente aquellos enfocados en la acción climática (Carta, Zidda, Putzu, Loru, Anedda & Giusto, 2023;Robinne, 2021). La teledetección, utilizando imágenes satelitales y fotografía aérea, es esencial en la recopilación de datos para la gestión de incendios (Mohapatra & Trinh, 2022).…”

Section: Introductionunclassified

Research advances in Graphic and Design Engineering at the UPC BarcelonaTech

Farrerons Vidal,

Olmedo Torre,

Roncero Vivero

et al. 2024

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Aquest llibre recull part de la recerca que porta a terme el Departament d’Enginyeria Gràfica i de Disseny (DEGD) de la Universitat Politècnica de Catalunya. Com podreu llegir, és una recerca molt variada i pluridisciplinària adaptada als temps actuals, on apareixen diverses de les principals preocupacions de la comunitat científica internacional, i referenciada en algunes de les línies d’investigació de Catalunya. En el llibre trobareu tan sols una petita mostra de la temàtica dels articles publicats pels professors del DEGD en les principals revistes científiques internacionals, però també en congressos, capítols de llibre, catàlegs d’exposició, projectes competitius industrials, projectes no competitius, projectes d’innovació docent i altres projectes de propietat intel·lectual industrial. El DEGD, a més de la recerca, té assignada docència a l’Escola Superior d’Enginyeries Industrial Aeroespacial i Audiovisual de Terrassa, a l’Escola Tècnica Superior d'Enginyeria Industrial de Barcelona, a l’Escola d'Enginyeria de Barcelona Est, a l’Escola Politècnica Superior d'Enginyeria de Vilanova i la Geltrú, a l’Escola Politècnica Superior d'Enginyeria de Manresa i a l’Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels. Imparteix docència en gairebé totes les titulacions que assignades en aquestes escoles, tant en graus com en màsters universitaris. A més, els professors del departament col·laboren en diversos programes de doctorat. En tot cas, la docència és una branca més des de la que s’accedeix a la recerca.

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Advancements in Forest Fire Prevention: A Comprehensive Survey

Cited by 27 publications

References 119 publications

Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance

Accuracy Assessment of Drone Real-Time Open Burning Imagery Detection for Early Wildfire Surveillance

Real-time forest fire detection, monitoring, and alert system using Arduino

Research advances in Graphic and Design Engineering at the UPC BarcelonaTech

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