Advancements in Forest Fire Prevention: A Comprehensive Survey.

UAV aerial artificial intelligence deep learning fire detection satellite sensors terrestrial

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
24 Jul 2023
Historique:
received: 16 06 2023
revised: 17 07 2023
accepted: 21 07 2023
medline: 31 7 2023
pubmed: 29 7 2023
entrez: 29 7 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 37514928
pii: s23146635
doi: 10.3390/s23146635
pmc: PMC10386475
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Ministry of Education, Universities and Research
ID : PE00000005

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Auteurs

Francesco Carta (F)

CNIT UdR, Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.

Chiara Zidda (C)

CNIT UdR, Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.

Martina Putzu (M)

CNIT UdR, Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.

Daniele Loru (D)

CNIT UdR, Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.

Matteo Anedda (M)

CNIT UdR, Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.

Daniele Giusto (D)

CNIT UdR, Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.

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