主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2024, Vol. 43 ›› Issue (11): 1590-1595.

• • 上一篇    下一篇

遥感技术在森林火灾预测中的应用现状分析

刘晓倩, 陈振国, 卢瑞芳, 穆达   

  1. (华北科技学院,北京 065201)
  • 收稿日期:2023-11-22 修回日期:2024-05-05 出版日期:2024-11-15 发布日期:2024-11-15
  • 作者简介:刘晓倩(1999- ),女,河北唐山人,华北科技学院计算机学院硕士研究生,主要从事遥感、深度学习方面的研究,河北省廊坊市三河市燕郊,065201。
  • 基金资助:
    基金项目:国家重点研发计划项目(2018YFC0808306);河北省重点研发计划项目(19270318D);河北省物联网监控技术创新中心项目(21567693H);青海省物联网重点实验室项目(2017-ZJ-Y21);廊坊市科技支撑计划项目(2023011091)

Analysis of applying status remote sensing in forest fire alarming

Liu Xiaoqian,Chen Zhenguo, Lu Ruifang, Mu Da   

  1. (North China Institute of Science and Technology, Beijing 065201, China)
  • Received:2023-11-22 Revised:2024-05-05 Online:2024-11-15 Published:2024-11-15

摘要: 为了有效应对森林火灾,遥感技术被广泛应用于火灾监测和预测。遥感技术具有数据采集迅速、实时动态监测以及采集范围广泛等特点,将其应用于森林火灾监测与预测,可以提高火灾应对的及时性与准确率,从而更好地保障人民生命财产安全。使用CiteSpace对遥感森林火灾研究成果进行可视化分析,总结现有的应用研究与研究方法,做出热点总结与展望,为未来遥感森林火灾研究提供参考。分析结果显示:该领域的发文量呈逐年增长趋势;发文作者之间的关联性相对较少;对遥感森林火灾的研究多侧重于成因与影响因素的分析;遥感技术与火灾探测算法的结合日益深入,旨在提升火灾探测的准确度和效率。

关键词: 遥感, 森林火灾, 文献计量, 深度学习, 应急管理

Abstract: To effectively address forest fires, remote sensing technology is widely used for fire monitoring and prediction. Remote sensing technology offers rapid data collection, real-time dynamic monitoring, and broad coverage, which enhances the timeliness and accuracy of fire response, thereby better safeguarding public safety and property. Using CiteSpace for visual analysis of research outcomes in remote sensing and forest fires, the current application research and methodologies are summarized, highlighting ing key areas and future directions to provide a reference for future research in this domain. An analysis of relevant literature reveals that the number of publications in this field is growing annually; the correlation among authors is relatively low; research on remote sensing and forest fires primarily focuses on the causes and influencing factors; and recent advancements increasingly integrate remote sensing technology with fire detection algorithms to improve detection accuracy and efficiency.

Key words: remote sensing, forest fires, bibliometric, deep learning, emergency management