Bi-spectral infrared forest fire detection and analysis using classification techniques

Jose Manuel Aranda; Juan Melendez; Antonio Jose de Castro; Fernando Lopez

doi:10.1117/12.507156

22 December 2003 Bispectral infrared forest fire detection and analysis using classification techniques

Jose Manuel Aranda, Juan Melendez, Antonio Jose de Castro, Fernando Lopez

Proceedings Volume 5153, Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture; (2003) https://doi.org/10.1117/12.507156
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

Infrared cameras are well established as a useful tool for fire detection, but their use for quantitative forest fire measurements faces difficulties, due to the complex spatial and spectral structure of fires. In this work it is shown that some of these difficulties can be overcome by applying classification techniques, a standard tool for the analysis of satellite multispectral images, to bi-spectral images of fires. Images were acquired by two cameras that operate in the medium infrared (MIR) and thermal infrared (TIR) bands. They provide simultaneous and co-registered images, calibrated in brightness temperatures. The MIR-TIR scatterplot of these images can be used to classify the scene into different fire regions (background, ashes, and several ember and flame regions). It is shown that classification makes possible to obtain quantitative measurements of physical fire parameters like rate of spread, embers temperature, and radiated power in the MIR and TIR bands. An estimation of total radiated power and heat release per unit area is also made and compared with values derived from heat of combustion and fuel consumption.

Citation Download Citation

Jose Manuel Aranda, Juan Melendez, Antonio Jose de Castro, and Fernando Lopez "Bispectral infrared forest fire detection and analysis using classification techniques", Proc. SPIE 5153, Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture, (22 December 2003); https://doi.org/10.1117/12.507156

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