Viktor Tuba, Romana Capor-Hrosik, Eva Tuba



Forest Fires Detection in Digital Images Based on Color Features

pdf PDF


One of the biggest ecological catastrophes are forest fires. Early detections of the fires can have significant impact on the control of forest fires which is very important for successful extinguishing. Areal or satellite surveillance is one of the systems used for monitoring forest areas. Based on the images provided by this system, fire can be detected by using some image processing techniques. In this paper we proposed an algorithm for forest fires detection based on color features. Different characteristics of the color components of YCbCr color model were used to detect fire based on the predefined threshold values and combination of the components values. After pixel classification morphological operations were performed to remove incorrect classified pixels. Our proposed method successfully detected fire regions in forest images with different lightning conditions.


Image processing, YCbCr, fire detection, color features


[1] K. B. Nizha, T. Gayathri, K. Ilakkiya, and C. Rajeswari, “Portable wirelesssystem for monitoring wildfire,” Development, vol. 2, no. 3, 2015. [1] K. B. Nizha, T. Gayathri, K. Ilakkiya, and C. Rajeswari, “Portable wirelesssystem for monitoring wildfire,” Development, vol. 2, no. 3, 2015. 

[2] K. Trivedi and A. K. Srivastava, “An energy effi- cient framework for detection and monitoring of forest fire using mobile agent in wireless sensor networks,” in International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2014, pp. 1–4. 

[3] V. Vipin, “Image processing based forest fire detection,” International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 2, pp. 87–95, 2012. 

[4] J. Zhao, Z. Zhang, S. Han, C. Qu, Z. Yuan, and D. Zhang, “Svm based forest fire detection using static and dynamic features,” Computer Science and Information Systems, vol. 8, no. 3, pp. 821– 841, 2011. 

[5] D. Y. Chino, L. P. Avalhais, J. F. Rodrigues, and A. J. Traina, “Bowfire: detection of fire in still images by integrating pixel color and texture analysis,” in 28th Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2015, pp. 95–102. 

[6] T. Celik, “Fast and efficient method for fire detection using image processing,” Electronics and Telecommunications Research Institute Journal, vol. 32, no. 6, pp. 881–890, 2010. 

[7] T. Qiu, Y. Yan, and G. Lu, “An autoadaptive edge-detection algorithm for flame and fire image processing,” IEEE Transactions on instrumentation and measurement, vol. 61, no. 5, pp. 1486–1493, 2012. 

[8] K. Angayarkkani and N. Radhakrishnan, “An intelligent system for effective forest fire detection using spatial data,” International Journal of Computer Science and Information Security, vol. 7, no. 1, pp. 202–208, 2010. 

[9] R. C. Hrosik, M. Tuba, and M. Vukovic, “Face detection algorithm based on skin detection and invariant moments,” 2013, vol. 10, pp. 110–115. 

[10] E. Kee, M. K. Johnson, and H. Farid, “Digital image authentication from JPEG headers,” IEEE transactions on information forensics and security, vol. 6, no. 3, pp. 1066–1075, 2011. 

[11] S. MISWAN, M. Miswan, M. Ngadi, M. S. H. Salam, and M. Abdul Jamil, “Red blood cell segmentation using masking and watershed algorithm: A preliminary study,” in International Conference on Biomedical Engineering (ICoBE). IEEE, 2012, pp. 258–262. 

[12] A. S. Ghotkar and G. K. Kharate, “Hand segmentation techniques to hand gesture recognition for natural human computer interaction,” International Journal of Human Computer Interaction (IJHCI), vol. 3, no. 1, p. 15, 2012.

Cite this paper

Viktor Tuba, Romana Capor-Hrosik, Eva Tuba. (2017) Forest Fires Detection in Digital Images Based on Color Features. International Journal of Environmental Science, 2, 66-70


Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0