ReviewSensors (Basel, Switzerland)2023
Advancements in Forest Fire Prevention: A Comprehensive Survey.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 122 citations in OpenAlex.
- LRM-YOLO: A Lightweight YOLOv10n-Based Model for Forest Fire Smoke Detection in UAV Images.Sensors (Basel, Switzerland) · 2026Article
- Holistic IoT and cloud-based telemetry architecture for proactive fire monitoring in smart agriculture.Scientific reports · 2026Article
- Intelligent fire detection in agriculture using machine learning and embedded systems for risk prevention and improved sustainability.Scientific reports · 2026Article
- FireMM-IR: An Infrared-Enhanced Multi-Modal Large Language Model for Comprehensive Scene Understanding in Remote Sensing Forest Fire Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Potential Sustainability and Stress Resilience of Forest Trees Induced by Nanotechnology Applications.Plant-environment interactions (Hoboken, N.J.) · 2025Review
- Boreal Forest Fire: UAV-collected Wildfire Detection and Smoke Segmentation Dataset.Scientific data · 2025Article
- Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8.Scientific reports · 2025Article
- Recent Progress in Two-Dimensional Nanomaterials for Flame Retardance and Fire-Warning Applications.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.