Review
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Advancements in Intelligent Technologies Approaches for Forest Fire Detection: A Comparative Study

Year 2025, Volume: 11 Issue: 1

Amira Sairi , Said Labed , Badreddine Miles

https://doi.org/10.33904/ejfe.1482838

Abstract

Forest fires pose a significant threat to ecosystems, wildlife, and communities worldwide, leading to severe environmental impacts such as soil degradation, reduced air quality, and increased greenhouse gas emissions. Effective forest fire prevention and management are a critical global challenge, with detection and suppression technologies constantly evolving. This paper provides a comparative study of various forest fire detection techniques, including watchtowers, satellites, wireless sensor networks (WSN), cameras, and drone systems. By examining the advantages and limitations of each method, the paper highlights specific examples of recent research using Artificial Intelligence (AI) and Internet of Things (IoT) technologies to illustrate their effectiveness and the problems. A detailed comparison table is included to summarize the performance and applicability of these techniques. The study concludes by evaluating the current state of fire detection technologies and proposing future research directions to enhance early fire detection systems. This comprehensive review aims to inform ongoing efforts in wildfire management and advance the development of more efficient detection strategies.

Keywords

Forest fire satellites drones Wireless Sensor Networks Internet of Things Deep Learning Camera

References

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Year 2025, Volume: 11 Issue: 1

Amira Sairi , Said Labed , Badreddine Miles

https://doi.org/10.33904/ejfe.1482838

Abstract

References

  • Al-Dahoud, A., Fezari, M., Alkhatib, A.A., Soltani, M. N., Al- Dahoud, A. 2023. Forest fire detection system based on low-cost wireless sensor network and internet of things. WSEAS Transactions on Environment and Development. 19:506-513.
  • Alkhatib, A.A. 2014. A review on forest fire detection techniques. International Journal of Distributed Sensor Networks, 10(3): 597368.
  • Almeida, J.S., Jagatheesaperumal, S.K., Nogueira, F.G., de Albuquerque,V.H.C. 2023. Edgefiresmoke++: A novel lightweight algorithm for real-time forest fire detection and visualization using internet of things- human machine interface. Expert Systems with Applications, 221: 119747.
  • Anh, N.D., Van Thanh, P., Lap, D.T., Khai, N.T., Van An, T., Tan, T.D., An, N.H., Dinh, D.N. 2022. Efficient forest fire detection using rule-based multi-color space and correlation coefficient for application in unmanned aerial vehicles. KSII Transactions on Internet and Infor- mation Systems (TIIS), 16(2):381–404.
  • Anshad, P., Gowda, N.M., Vijaykumar, C., Prasad, A.S. 2023. Forest fire detection using nrf24l01 wireless sensor network and prediction by machine learning model. In International Conference on Recent Trends in Electronics and Communication (ICRTEC), pp. 1–5.
  • Antwi, E.K., Boakye-Danquah, J., Owusu-Banahene, W., Webster, K., Dabros, A., Wiebe, P., Mayor, S.J., Westwood, A., Mansuy, N., Setiawati, M.D. et al., 2022. A global review of cumulative effects assessments of disturbances on forest ecosystems. Journal of Environmental Management, 317, 115277.
  • Arjun, D., Hanumanthaiah, A. 2020. Wireless sensor network framework for early detection and warning of forest fire. 2020 International Conference on Inventive Computation Technologies (ICICT), pp. 186– 191.
  • Ba, R., Chen, C., Yuan, J. Song, W., Lo, S. 2019. Smokenet: Satellite smoke scene detection using convolutional neural network with spatial and channel-wise attention. Remote Sensing, 11(14): 1702.
  • Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., Grammalidis, N. 2020a. A review on early forest fire detection systems using optical remote sensing. Sensors, 20(22): 6442.
  • Barmpoutis, P., Stathaki, T., Dimitropoulos, K., Grammalidis, N. 2020b. Early fire detection based on aerial 360-degree sensors, deep convolu- tion neural networks and exploitation of fire dynamic textures. Remote Sensing, 12(19): 3177.
  • Barschke, M.F., Bartholom ̈aus, J., Gordon, K., Lehmann, M., Brieß, K. 2017. The tubin nanosatellite mission for wildfire detection in thermal infrared. CEAS Space Journal, (9):183–194.
  • Benzekri, W., El Moussati, A., Moussaoui, O., Berrajaa, M. 2020. Early forest fire detection system using wireless sensor network and deep learning. International Journal of Advanced Computer Science and Applications, 11:5.
  • Bouguettaya, A., Zarzour, H., Taberkit, A. M., and Kechida, A., 2022. A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. Signal Processing, 190, 108309.
  • Budiyanto, S., Silalahi, L. M., Silaban, F. A., Darusalam, U., Andryana, S., Rahayu, I. F. 2020. Optimization of sugeno fuzzy logic based on wireless sensor network in forest fire monitoring system. 2nd International Conference on Industrial Electrical and Electronics (ICIEE), pp. 126–134.
  • Chen, G., Zhou, H., Li, Z., Gao, Y., Bai, D., Xu, R., Lin, H. 2023. Multi- scale forest fire recognition model based on improved yolov5s. Forests, 14(2): 315.
  • Chen, J., He,Y., Wang, J. 2010. Multi-feature fusion based fast video flame detection. Building and Environment, 45(5):1113– 1122.
  • Chowdary, V., Gupta, M.K. 2018. Automatic forest fire detection and monitoring techniques: a survey. In Intelligent Communication, Control and Devices, pp. 1111–1117, Springer.
  • Chowdary, V., Deogharia, D., Sowrabh, S., and Dubey, S., 2022. Forest fire detection system using barrier coverage in wireless sensor networks. Materials Today: Proceedings. 64(3): 1322-1327.
  • Conrad, A., Liu, Q., Russell, J., Lalla, J. 2009. Enhanced forest fire detection system with GPS Pennsylvania. 2009.
  • Dampage, U., Bandaranayake, L., Wanasinghe, R., Kottahachchi, K., Jayasanka, B. 2022. Forest fire detection system using wireless sensor networks and machine learning. Scientific reports, 12(1): 1– 11.
  • De Vivo, F., Battipede, M., Johnson, E. 2021. Infra-red line camera data- driven edge detector in UAV forest fire monitoring. Aerospace Science and Technology, 111: 106574.
  • Devadevan V., Sankaranarayanan, S. 2019. Forest fire information system using wireless sensor network. In Environmental Information Systems: Concepts, Methodologies, Tools, and Applications, IGI Global. pp. 894-911.
  • Dubey, V., Kumar, P., Chauhan, N. 2019. Forest fire detection system using iot and artificial neural network, in International Conference on Innovative Computing and Communications, Springer. pp. 323–337.
  • Gaur, A., Singh, A., Kumar, A., Kumar, A., Kapoor, K. 2020. Video flame and smoke based fire detection algorithms: A literature review. Fire technology, 56: 1943–1980.
  • Geetha, S., Abhishek, C., Akshayanat, C. 2021. Machine vision based fire detection techniques: A survey. Fire technology, 57:591–623.
  • Ghali, R., Akhloufi, M. A. 2023. Deep learning approaches for wildland fires using satellite remote sensing data: Detection, mapping, and pre- diction. Fire, 6(5): 192.
  • Guan, Z., Miao, X., Mu, Y., Sun, Q., Ye, Q., Gao, D. 2022. Forest fire segmentation from aerial imagery data using an improved instance segmentation model. Remote Sensing, 4(13): 3159.
  • Guede-Fern ́andez, F., Martins, L., deAlmeida, R. V., Gamboa, H., Vieira, P. 2021. A deep learning based object identification system for forest fire detection. Fire, 4(4): 75.
  • Hartung, C., Han, R., Seielstad, C., Holbrook, S. 2006. Firewxnet: A multi- tiered portable wireless system for monitoring weather conditions in wildland fire environments. In Proceedings of the 4th international conference on Mobile systems, applications and services, pp. 28–41.
  • Hossain, F.A., Zhang, Y.M., Tonima, M.A. 2020. Forest fire flame and smoke detection from uav-captured images using fire-specific color fea- tures and multi-color space local binary pattern. Journal of Unmanned Vehicle Systems, 8(4): 285–309.
  • James, G.L., Ansaf, R.B., Al Samahi, S.S., Parker, R. D., Cutler, J.M., Gachette, R.V., Ansaf, B.I. 2023. An efficient wildfire detection system for ai-embedded applications using satellite imagery. Fire, 6(4): 169.
  • Jandhyala, S. S., Jalleda, R. R., Ravuri, D. M. 2023. Forest fire classifica- tion and detection in aerial images using inception-v3 and ssd models. In International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pp. 320–325.
  • Jiao, Z., Zhang, Y., Xin, J., Mu, L., Yi, Y., Liu, H., Liu, D. 2019. A deep learning based forest fire detection approach using UAV and yolov3. In 1st International conference on industrial artificial intelligence (IAI), pp. 1–5.
  • Kadir, E.A., Rosa, S.L., Othman, M. 2019. Forest fire monitoring system using WSNS technology. Proceedings of the Second International Conference on Science, Engineering and Technology. pp. 135-139.
  • Kaur, H., Sood, S.K., Bhatia, M. 2020. Cloud-assisted green iot-enabled comprehensive framework for wildfire monitoring. Cluster Computing, 23(2):1149–1162.
  • Kinaneva, D., Hristov, G., Raychev, J., Zahariev, P. 2019. Early forest fire detection using drones and artificial intelligence. in 2019 42nd Inter- national Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1060–1065.
  • Kizilkaya, B., Ever, E., Yatbaz, H. Y., Yazici, A. 2022. An effective forest fire detection framework using heterogeneous wireless multimedia sensor networks. ACM Transactions on Multimedia Computing, Com- munications, and Applications (TOMM), 18(2): 1-21.
  • Kose, K., Tsalakanidou, F., Besbes, H., Tlili, F., Governeur, B., Pauwels, E., et al., 2010. Firesense: fire detection and managment through a multi-sensor network for protection of cultural heritage areas from the risk of fire and extreme weather conditions. Proceedings of the 7th Framework Programme for Research and Technological Development. hessaloniki, Greece.
  • Labed, S., Touati, H., Herida, A., Kerbab, S., Sairi, A. 2023. An ai-based image recognition system for early detection of forest and field fires. European Journal of Forest Engineering, 9(2): 48–56.
  • Larsen, A., Hanigan, I., Reich, B.J., Qin, Y., Cope, M., Morgan, G., Rappold, A.G. 2021. A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication. Journal of exposure science & environmental epidemiology, 31(1): 170–176.
  • Lee, W., Kim, S., Lee, Y.-T., Lee, H.-W., Choi, M. 2017. Deep neural networks for wild fire detection with unmanned aerial vehicle. In 2017 IEEE international conference on consumer electronics (ICCE), pp. 252–253.
  • Li, S. 2018. Wildfire early warning system based on wireless sensors and unmanned aerial vehicle. Journal of Unmanned Vehicle Systems, 7(1): 76–91.
  • Li, S., Qiao, L., Zhang, Y., Yan, J. 2022. An early forest fire detection system based on dji m300 drone and h20t camera. In International Conference on Unmanned Aircraft Systems (ICUAS), pp. 932–937.
  • Lloret, M., Garcia, J., Bri, D., Sendra, S., 2009. A wireless sensor network deployment for rural and forest fire detection and verification. Sensors, 9(11): 8722–8747.
  • Lorenz, E., Mitchell, S., Säuberlich, T., Paproth, C., Halle, W., Frauenberger, O. 2015. Remote sensing of high temperature events by the firebird mission. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 461-467.
  • Molina-Pico, A., Cuesta-Frau, D., Araujo, A., Alejandre, J., Rozas, A. 2016. Forest monitoring and wildland early fire detection by a hierarchical wireless sensor network. Journal of Sensors, 2016(3-4):1-8.
  • Namburu, A., Selvaraj, P., Mohan, S. Ragavanantham, S., Eldin, E. T. 2023. Forest fire identification in uav imagery using x-mobilenet. Electronics, 12(3): 733.
  • Noureddine, H., Bouabdellah, K. 2020. Field experiment testbed for forest fire detection using wireless multimedia sensor network. International Journal of Sensors Wireless Communications and Control, 10(1): 3–14.
  • Patel, J., Bhusnoor, M., Patel, D., Mehta, A., Sainkar, S., Mehen- dale, N. 2023. Unmanned aerial vehicle-based forest fire detection systems: A comprehensive review. Available at SSRN 4603404.
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There are 71 citations in total.

Details

Primary Language English
Subjects Information Systems (Other), Photogrammetry and Remote Sensing, Forestry Sciences (Other)
Journal Section Review Article
Authors

Amira Sairi 0009-0004-9716-725X

Said Labed 0000-0001-9273-9790

Badreddine Miles 0000-0002-0559-3277

Early Pub Date February 14, 2025
Publication Date
Submission Date May 19, 2024
Acceptance Date August 16, 2024
Published in Issue Year 2025 Volume: 11 Issue: 1

Cite

APA Sairi, A., Labed, S., & Miles, B. (2025). Advancements in Intelligent Technologies Approaches for Forest Fire Detection: A Comparative Study. European Journal of Forest Engineering, 11(1). https://doi.org/10.33904/ejfe.1482838
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