基于红外遥感技术的柽柳林火险监测

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
Jin Wang, Ruiting Liu, Liming Liu, Xiaoxiang Cheng, Feiyong Chen, Xue Shen
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引用次数: 0

摘要

摘要 本研究以山东省昌邑国家级海洋生态特别保护区内的柽柳林为研究对象,开展了基于热红外遥感技术的林火监测研究。我们将基于遥感技术的林火点常用监测方法归纳为两类:固定阈值法(包括其变形模型和扩展模型)和相邻像素分析法(又称背景像素相关法)。并分析了这两种方法的优缺点。从 IRS 传感器(HJ 1B 号卫星)和 TIRS 传感器(Landsat-8 号卫星)的遥感图像反演的 BT(亮度温度)数据表明,在上述阶段,保护区内没有足够的热辐射形成火点。研究结果和方法也证实了热红外遥感技术可用于林火监测和宏观林火点的识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fire Risk Monitoring of Tamarix chinensis Forest Based on Infrared Remote Sensing Technology

Fire Risk Monitoring of Tamarix chinensis Forest Based on Infrared Remote Sensing Technology

Fire Risk Monitoring of Tamarix chinensis Forest Based on Infrared Remote Sensing Technology

In this study, the Tamarix chinensis forest in Changyi national marine ecological special protected area in Shandong province, China, was researched for forest fire monitoring based on thermal infrared remote sensing technology. We summarized the commonly monitoring methods for forest fire point based on remote sensing technology into two types: fixed threshold method (including its deformation model and extension model) and adjacent pixel analysis method (also known as background pixel correlation method). And we analyzed the advantages and disadvantages of these two methods. The BT (brightness temperature) data inverted from the remote sensing images of IRS sensor (HJ 1B satellite) and TIRS sensor (Landsat-8 satellite) indicated that there not had enough thermal radiation to form a fire point during the above phases in the protected zone. The research results and methods also confirmed that thermal infrared remote sensing technology can be used for forest fire monitoring and identification of macro forest fire point.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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