An integrated edge–cloud IoT framework for resilient disaster prevention in fire detection and forest carbon assessment
統合エッジクラウドIoTフレームワークによる火災検知と森林炭素評価における強靭な防災 (AI 翻訳)
Li-Hsien Chen, S. S. Kolhe, Jie-Dong Hu, K. Tseng, Meng-Yun Chung
🤖 gxceed AI 要約
日本語
本研究は、IoT技術を用いた森林火災監視・管理システムを開発した。センシング層、伝送層、アプリケーション層、電源層の4層構成で、画像認識による炎・煙検出と気象データ連携により誤警報を低減。物体検出の平均精度84.4%、赤外線温度測定の誤差0.45%を達成し、衛星画像とGISデータの統合で火災被害マッピングを実現。森林炭素吸収源の保護と気候変動対策に貢献する。
English
This study developed a forest fire monitoring and management system using IoT technology with four layers: sensing, transmission, application, and power supply. It achieves 84.4% average precision in fire detection and 0.45% error in infrared temperature measurement, integrating satellite and GIS data for impact mapping. The system enhances early wildfire detection and protects forest carbon sinks, contributing to climate change mitigation.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の森林管理やJ-クレジット制度における森林炭素吸収量の算定・検証に、本システムの高精度な火災検知と被害評価が活用できる可能性がある。
In the global GX context
This paper provides a concrete IoT architecture for forest fire detection that directly supports climate mitigation by preserving carbon sinks. It is relevant to international frameworks such as REDD+ and national GHG inventory reporting.
👥 読者別の含意
🔬研究者:Researchers in environmental sensing and climate mitigation can adopt the layered IoT design and integrate with carbon accounting models.
🏢実務担当者:Forest managers and disaster prevention agencies can use this system for early fire detection and damage assessment.
🏛政策担当者:Policymakers can consider this technology for national forest carbon monitoring and climate adaptation strategies.
📄 Abstract（原文）
Humanity’s ongoing overuse of natural resources is degrading the Earth’s environment, intensifying climate change impacts. Recent record temperatures and frequent wildfires threaten humans directly and disrupt ecosystems, especially by damaging forest carbon sinks, which accelerates global warming in a harmful cycle. To combat this, the study developed a forest fire monitoring and management system using Internet of Things (IoT) technology. The system consists of four layers: (1) The sensing layer collects environmental data—meteorological, air quality, images, and thermal imaging—to detect fires early. It uses image recognition to identify flames and smoke, cross-checking with real-time weather data to reduce false alarms. (2) The transmission layer utilizes 4G networks for high-bandwidth, real-time data and image transfer. (3) The application layer provides real-time images, satellite monitoring maps, and field sensor data for forest management needs. (4) The power supply layer adapts to different environments, using solar energy and batteries outdoors and grid electricity indoors. Testing shows the system achieves 84.4% average precision in object detection and only 0.45% average error in infrared temperature measurement. Integrating satellite images and geographic information system data allows precise mapping of fire impact zones and severity. This innovative IoT-based forest fire system is vital for early detection and enhanced wildfire management, offering a powerful tool to help preserve ecological balance and address climate change effectively.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1038/s41598-026-43053-2first seen 2026-06-29 07:46:57
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