Research Article
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An Internet of Things Platform for Forest Monitoring

Year 2023, , 80 - 87, 26.12.2023
https://doi.org/10.33904/ejfe.1383234

Abstract

Forests have a very important function in sustaining the natural life on our planet. However, wildfires, landslides, uncontrolled tree cutting, poaching, arson, and many other dangers threaten the forests and natural resources. Therefore, effective monitoring and observation of forests is crucial. This study explains the development and implementation of an Internet of Things (IoT)-enabled forest monitoring system as an innovative solution that will contribute to the protection of forests. The presented system provides real-time climate data in forestlands by using microcontrollers, low-cost sensors, data communication, and cloud platforms. It collects important information such as temperature, humidity, air quality, and ecological activities. Fire detection is achieved by associating the increase in CO gas concentration with the increase in temperature. Landslides are detected by measuring the acceleration of soil movement in 3 axes. Additionally, the system includes advanced machine learning-based acoustic tracking techniques to detect chainsaws, motor vehicles, screams, shouts, and gunshots. The IoT platform provides a web-based user interface and other tools to system users such as forest managers and researchers. These tools detect early signs of threats such as wildfires, landslides and illegal activities in forests. Our tests demonstrate the system's effectiveness in providing information for protecting and managing forests.

References

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  • Chung, S., Chung, Y. 2017. Scream sound detection based on SVM and GMM. International Conference on Recent Trends in Engineering and Technology, 3-4 May, Bangkok, Thailand, pp. 215-218.
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  • Dash, T.K. and Solanki, S.S. 2019. Investigation on the Effect of the Input Features in the Noise Level Classification of Noisy Speech. Journal of Scientific and Industrial Research, 78:868-872.
  • Gulci, S., Akay, A.E., Spinelli, R., Magagnotti, N., 2018. Assessing the exposure of chipper operators to wood dust in a roadside landing area. Fresenius Environmental Bulletin, 27(6): 4132-4138.
  • Guo, G., Li, S. 2003. Content-based audio classification and retrieval by support vector machines. IEEE transactions on neural networks, 14(1):209-223.
  • Hu, M., Chai, H., Ren, Y. 2021. Forest fire video detection based on multi-scale feature fusion with data enhancement. 4th International Conference on Artificial Intelligence and Pattern Recognition, 24-26 September, Xiamen, China, pp. 218-224.
  • Imamoglu, A.F., Tas, I. 2022. Preliminary application of a low-cost smart collar developed for wild animal tracking. European Journal of Forest Engineering, 8(1): 35-39.
  • Kathirvel, P., Manikandan, M.S., Senthilkumar, S., Soman, K.P. 2011. Noise robust zerocrossing rate computation for audio signal classification. 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011), 8-9 December, Chennai, India, pp. 65-69.
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  • Madkar, S., Sakhare, D.Y., Phutane, K., Haral, A., Nikam, K., Tharunyha, S. 2022. Video based forest fire and smoke detection using YoLo and CNN. International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 8-9 December, Chennai, India, pp. 1-5.
  • Nai-meng, C., Wan-jun, Y. 2020. Early forest fire smoke detection based on aerial video. Journal of Physics: Conference Series, 1684(1):12095.
  • Niessen, M.E., Krijnders, D., Andringa, T.C. 2009. Understanding a soundscape through its components, Euronoise, 26 - 28 October, Scotland, UK.
  • Nihei, K., Kai, N., Maruyama, Y., Yamashita, T., Kanetomo, D., Kitahara, T., Maruyama, M., Ohki, T., Kusin, K., Segah, H. 2022. Forest fire surveillance using live video streaming from UAV via multiple LTE networks. IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 8-11 January, online, pp. 465-468.
  • Oh, S.G., Lee, J.U., Lee, H.S., Chung, Y.W., Park, D.H. 2012. Abnormal sound detection and identification in surveillance system. Journal of KIISE, 39(2):144-152.
  • Patel, Y.S., Banerjee, S., Misra, R., Das, S.K. 2020. Low-latency energy-efficient cyber-physical disaster system using edge deep learning. 21st International Conference on Distributed Computing and Networking, 4-7 January, Kolkata, India, pp. 1-6.
  • Pokhrel, P., Soliman, H. 2018. Advancing early forest fire detection utilizing smart wireless sensor networks. In: Kameas, A., Stathis, K. (Eds.) Ambient Intelligence. AmI 2018. Lecture Notes in Computer Science, vol 11249. Springer, Berlin, pp. 63-73.
  • Rijsbergen, C.J.V. 1979. Information Retrieval, 2nd ed. Butterworth-Heinemann, Newton, MA, USA.
  • Selle, J., Krishna, A.V., Harish, M., Reddy, K.B. 2022. An IoT based Alert System with Gas Sensors in a WSN Framework for Evasion of Forest Fire. 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 28-29 April, India, pp. 1516-1521.
  • Seric, L., Ivanda, A., Bugaric, M., Braovic, M. 2022. Semantic conceptual framework for environmental monitoring and surveillance-a case study on forest fire video monitoring and surveillance. Electronics, 11(2):275.
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  • Wiame, B., Moussati, E., Omar, M., Mohammed, B. 2021. Early forest fire detection system using wireless sensor network and deep learning. International Journal of Advanced Computer Science Applications, 7(2):469–505.
  • Yan, X., Cheng, H., Zhao, Y., Yu, W., Huang, H., Zheng, X. 2016. Real-time identification of smoldering and flaming combustion phases in forest using a wireless sensor network-based multi-sensor system and artificial neural network. Sensors, 16(8):1228.
  • Yang, X., Wang, Y., Liu, X., Liu, Y. 2022. High-precision real-time forest fire video detection using one-class model. Forests, 13(11):1826.
  • Zope, V., Dadlani, T., Matai, A., Tembhurnikar, P., Kalani R. 2020. IoT sensor and deep neural network based wildfire prediction system. International Conference on Intelligent Computing and Control Systems, 13-15 May, Secunderabad, India, pp. 205–208.
Year 2023, , 80 - 87, 26.12.2023
https://doi.org/10.33904/ejfe.1383234

Abstract

References

  • References Al-Masri, A.N. 2021. Automated deep learning based video summarization approach for forest fire detection. Journal of Intelligent Systems and Internet of Things, 5(2):54-61.
  • Amado, R.G., Filho, J.V. 2008. Pitch detection algorithms based on zero-cross rate and autocorrelation function for musical notes. International Conference on Audio, Language and Image Processing, Shanghai, China, pp. 449-454.
  • Ansari, M.R., Tumpa, S.A., Raya, J.A., Murshed, M.N. 2021. Comparison between support vector machine and random forest for audio classification. International Conference on Electronics, Communications and Information Technology (ICECIT), 14-16 September, online, pp. 1-4.
  • Chung, S., Chung, Y. 2017. Scream sound detection based on SVM and GMM. International Conference on Recent Trends in Engineering and Technology, 3-4 May, Bangkok, Thailand, pp. 215-218.
  • Dampage, U., Bandaranayake, L., Wanasinghe, R., Kottahachchi, K. 2022. Forest fire detection system using wireless sensor networks and machine learning. Scientific Reports, 12:1-11.
  • Dash, T.K. and Solanki, S.S. 2019. Investigation on the Effect of the Input Features in the Noise Level Classification of Noisy Speech. Journal of Scientific and Industrial Research, 78:868-872.
  • Gulci, S., Akay, A.E., Spinelli, R., Magagnotti, N., 2018. Assessing the exposure of chipper operators to wood dust in a roadside landing area. Fresenius Environmental Bulletin, 27(6): 4132-4138.
  • Guo, G., Li, S. 2003. Content-based audio classification and retrieval by support vector machines. IEEE transactions on neural networks, 14(1):209-223.
  • Hu, M., Chai, H., Ren, Y. 2021. Forest fire video detection based on multi-scale feature fusion with data enhancement. 4th International Conference on Artificial Intelligence and Pattern Recognition, 24-26 September, Xiamen, China, pp. 218-224.
  • Imamoglu, A.F., Tas, I. 2022. Preliminary application of a low-cost smart collar developed for wild animal tracking. European Journal of Forest Engineering, 8(1): 35-39.
  • Kathirvel, P., Manikandan, M.S., Senthilkumar, S., Soman, K.P. 2011. Noise robust zerocrossing rate computation for audio signal classification. 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011), 8-9 December, Chennai, India, pp. 65-69.
  • Krishnamoorthy, M., Asif, M., Kumar, P.P., Nuvvula, R.S.S., Khan, B., Colak, I. 2023. A design and development of the smart forest alert monitoring system using IoT. Journal of Sensors 2023:1–12.
  • Madkar, S., Sakhare, D.Y., Phutane, K., Haral, A., Nikam, K., Tharunyha, S. 2022. Video based forest fire and smoke detection using YoLo and CNN. International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 8-9 December, Chennai, India, pp. 1-5.
  • Nai-meng, C., Wan-jun, Y. 2020. Early forest fire smoke detection based on aerial video. Journal of Physics: Conference Series, 1684(1):12095.
  • Niessen, M.E., Krijnders, D., Andringa, T.C. 2009. Understanding a soundscape through its components, Euronoise, 26 - 28 October, Scotland, UK.
  • Nihei, K., Kai, N., Maruyama, Y., Yamashita, T., Kanetomo, D., Kitahara, T., Maruyama, M., Ohki, T., Kusin, K., Segah, H. 2022. Forest fire surveillance using live video streaming from UAV via multiple LTE networks. IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 8-11 January, online, pp. 465-468.
  • Oh, S.G., Lee, J.U., Lee, H.S., Chung, Y.W., Park, D.H. 2012. Abnormal sound detection and identification in surveillance system. Journal of KIISE, 39(2):144-152.
  • Patel, Y.S., Banerjee, S., Misra, R., Das, S.K. 2020. Low-latency energy-efficient cyber-physical disaster system using edge deep learning. 21st International Conference on Distributed Computing and Networking, 4-7 January, Kolkata, India, pp. 1-6.
  • Pokhrel, P., Soliman, H. 2018. Advancing early forest fire detection utilizing smart wireless sensor networks. In: Kameas, A., Stathis, K. (Eds.) Ambient Intelligence. AmI 2018. Lecture Notes in Computer Science, vol 11249. Springer, Berlin, pp. 63-73.
  • Rijsbergen, C.J.V. 1979. Information Retrieval, 2nd ed. Butterworth-Heinemann, Newton, MA, USA.
  • Selle, J., Krishna, A.V., Harish, M., Reddy, K.B. 2022. An IoT based Alert System with Gas Sensors in a WSN Framework for Evasion of Forest Fire. 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 28-29 April, India, pp. 1516-1521.
  • Seric, L., Ivanda, A., Bugaric, M., Braovic, M. 2022. Semantic conceptual framework for environmental monitoring and surveillance-a case study on forest fire video monitoring and surveillance. Electronics, 11(2):275.
  • URL-1, https://www.raspberrypi.org, Last Access: 01.10.2023.
  • URL-2, https://www.arduino.cc, Last Access: 01.10.2023. URL-3, https://www. hwsensor.com, Last Access: 01.10.2023.
  • URL-4, https://www.microbot.it, Last Access: 01.10.2023.
  • URL-5, https://www.analog.com/en/products/adxl345 .html, Last Access: 01.10.2023.
  • URL-6, https://www.simcom.ee/modules/gsm-gprs-nss/ sim808, Last Access: 01.10.2023.
  • Wiame, B., Moussati, E., Omar, M., Mohammed, B. 2021. Early forest fire detection system using wireless sensor network and deep learning. International Journal of Advanced Computer Science Applications, 7(2):469–505.
  • Yan, X., Cheng, H., Zhao, Y., Yu, W., Huang, H., Zheng, X. 2016. Real-time identification of smoldering and flaming combustion phases in forest using a wireless sensor network-based multi-sensor system and artificial neural network. Sensors, 16(8):1228.
  • Yang, X., Wang, Y., Liu, X., Liu, Y. 2022. High-precision real-time forest fire video detection using one-class model. Forests, 13(11):1826.
  • Zope, V., Dadlani, T., Matai, A., Tembhurnikar, P., Kalani R. 2020. IoT sensor and deep neural network based wildfire prediction system. International Conference on Intelligent Computing and Control Systems, 13-15 May, Secunderabad, India, pp. 205–208.
There are 31 citations in total.

Details

Primary Language English
Subjects Information Systems User Experience Design and Development, Information Systems (Other)
Journal Section Research Articles
Authors

Mustafa Sanlı 0000-0003-4871-6932

Early Pub Date December 19, 2023
Publication Date December 26, 2023
Submission Date October 30, 2023
Acceptance Date December 13, 2023
Published in Issue Year 2023

Cite

APA Sanlı, M. (2023). An Internet of Things Platform for Forest Monitoring. European Journal of Forest Engineering, 9(2), 80-87. https://doi.org/10.33904/ejfe.1383234

Creative Commons License

The works published in European Journal of Forest Engineering (EJFE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.