Advancements in Forest Fire Prevention: A Comprehensive Survey

被引:50
作者
Carta, Francesco [1 ]
Zidda, Chiara [1 ]
Putzu, Martina [1 ]
Loru, Daniele [1 ]
Anedda, Matteo [1 ]
Giusto, Daniele [1 ]
机构
[1] Univ Cagliari, Dept Elect & Elect Engn, CNIT UdR, I-09123 Cagliari, Italy
关键词
fire detection; terrestrial; aerial; satellite; artificial intelligence; deep learning; UAV; sensors; REAL-TIME FIRE; SMOKE; SENSOR; IMAGE; NETWORKS; DEPLOYMENT; SYSTEM; MODEL;
D O I
10.3390/s23146635
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires.
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页数:26
相关论文
共 119 条
[1]   Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model [J].
Abdollahi, Arnick ;
Pradhan, Biswajeet .
SCIENCE OF THE TOTAL ENVIRONMENT, 2023, 879
[2]   An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach [J].
Abdusalomov, Akmalbek Bobomirzaevich ;
Islam, Bappy M. D. Siful ;
Nasimov, Rashid ;
Mukhiddinov, Mukhriddin ;
Whangbo, Taeg Keun .
SENSORS, 2023, 23 (03)
[3]   Unsupervised Segmentation of Fire and Smoke From Infra-Red Videos [J].
Ajith, Meenu ;
Martinez-Ramon, Manel .
IEEE ACCESS, 2019, 7 :182381-182394
[4]  
Andrews PL., 1986, General Technical Report INT-194, P1
[5]   An intelligent system for false alarm reduction in infrared forest-fire detection [J].
Arrue, BC ;
Ollero, A ;
de Dios, JRM .
IEEE INTELLIGENT SYSTEMS & THEIR APPLICATIONS, 2000, 15 (03) :64-73
[6]   Fire-smart solutions for sustainable wildfire risk prevention: Bottom-up initiatives meet top-down policies under EU green deal [J].
Ascoli, Davide ;
Plana, Eduard ;
Oggioni, Silvio Daniele ;
Tomao, Antonio ;
Colonico, Mario ;
Corona, Piermaria ;
Giannino, Francesco ;
Moreno, Mauro ;
Xanthopoulos, Gavriil ;
Kaoukis, Konstantinos ;
Athanasiou, Miltiadis ;
Colaco, Maria Conceicao ;
Rego, Francisco ;
Sequeira, Ana Catarina ;
Acacio, Vanda ;
Serra, Marta ;
Barbati, Anna .
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 2023, 92
[7]  
Aslan S, 2019, INT CONF ACOUST SPEE, P8315, DOI [10.1109/ICASSP.2019.8683629, 10.1109/icassp.2019.8683629]
[8]   A framework for use of wireless sensor networks in forest fire detection and monitoring [J].
Aslan, Yunus Emre ;
Korpeoglu, Ibrahim ;
Ulusoy, Ozgur .
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2012, 36 (06) :614-625
[9]  
Athanasiou M., 2022, P 9 INT C FOR FIR RE
[10]   A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing [J].
Barmpoutis, Panagiotis ;
Papaioannou, Periklis ;
Dimitropoulos, Kosmas ;
Grammalidis, Nikos .
SENSORS, 2020, 20 (22) :1-26