Li Meng, Jim O’Hehir, Jing Gao, Stefan Peters, Anthony Hay
{"title":"A theoretical framework for improved fire suppression by linking management models with smart early fire detection and suppression technologies","authors":"Li Meng, Jim O’Hehir, Jing Gao, Stefan Peters, Anthony Hay","doi":"10.1007/s11676-024-01737-3","DOIUrl":null,"url":null,"abstract":"<p>Bushfires are devastating to forest managers, owners, residents, and the natural environment. Recent technological advances indicate a potential for faster response times in terms of detecting and suppressing fires. However, to date, all these technologies have been applied in isolation. This paper introduces the latest fire detection and suppression technologies from ground to space. An operations research method was used to assemble these technologies into a theoretical framework for fire detection and suppression. The framework harnesses the advantages of satellite-based, drone, sensor, and human reporting technologies as well as image processing and artificial intelligence machine learning. The study concludes that, if a system is designed to maximise the use of available technologies and carefully adopts them through complementary arrangements, a fire detection and resource suppression system can achieve the ultimate aim: to reduce the risk of fire hazards and the damage they may cause.</p>","PeriodicalId":15830,"journal":{"name":"Journal of Forestry Research","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forestry Research","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11676-024-01737-3","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0
Abstract
Bushfires are devastating to forest managers, owners, residents, and the natural environment. Recent technological advances indicate a potential for faster response times in terms of detecting and suppressing fires. However, to date, all these technologies have been applied in isolation. This paper introduces the latest fire detection and suppression technologies from ground to space. An operations research method was used to assemble these technologies into a theoretical framework for fire detection and suppression. The framework harnesses the advantages of satellite-based, drone, sensor, and human reporting technologies as well as image processing and artificial intelligence machine learning. The study concludes that, if a system is designed to maximise the use of available technologies and carefully adopts them through complementary arrangements, a fire detection and resource suppression system can achieve the ultimate aim: to reduce the risk of fire hazards and the damage they may cause.
期刊介绍:
The Journal of Forestry Research (JFR), founded in 1990, is a peer-reviewed quarterly journal in English. JFR has rapidly emerged as an international journal published by Northeast Forestry University and Ecological Society of China in collaboration with Springer Verlag. The journal publishes scientific articles related to forestry for a broad range of international scientists, forest managers and practitioners.The scope of the journal covers the following five thematic categories and 20 subjects:
Basic Science of Forestry,
Forest biometrics,
Forest soils,
Forest hydrology,
Tree physiology,
Forest biomass, carbon, and bioenergy,
Forest biotechnology and molecular biology,
Forest Ecology,
Forest ecology,
Forest ecological services,
Restoration ecology,
Forest adaptation to climate change,
Wildlife ecology and management,
Silviculture and Forest Management,
Forest genetics and tree breeding,
Silviculture,
Forest RS, GIS, and modeling,
Forest management,
Forest Protection,
Forest entomology and pathology,
Forest fire,
Forest resources conservation,
Forest health monitoring and assessment,
Wood Science and Technology,
Wood Science and Technology.