Integrasi Teknologi Drone dan Sensor Termal untuk Pemantauan Hutan Tropis
DOI:
https://doi.org/10.59031/jnts.v2i3.759Keywords:
Drones, Early Detection, Environmental Monitoring, Thermal Sensors, Tropical Forest FiresAbstract
Tropical forest fires pose a serious threat to ecosystem sustainability, particularly in Kalimantan, which is prone to seasonal fires. Early detection is key to prevention efforts, but conventional and satellite-based monitoring methods often face limitations, particularly in identifying small-scale hotspots obscured by forest canopies. This study aims to test the effectiveness of integrating drone technology with thermal sensors in tropical forest monitoring as an early fire detection system. The research method uses a field study design with an experimental approach. Drone flights were conducted over tropical forest areas in Kalimantan, systematically capturing thermal imagery according to a predetermined flight path. Thermal image data were analyzed to identify hotspots, then compared with satellite hotspot data (MODIS and VIIRS). Field validation was also conducted through direct temperature measurements using a portable infrared thermometer. Data analysis involved comparing detection results, accuracy testing, and measuring system sensitivity with a confusion matrix. The results showed that drones with thermal sensors were able to detect more hotspots than satellites, with a higher level of accuracy compared to field validation results. For example, in several study areas, drones successfully identified small hotspots that were not detected by satellites. This confirms that drones with thermal sensors have high sensitivity and can be used as early detection tools for tropical forest fires. In conclusion, the integration of drone technology and thermal sensors has proven effective as a monitoring system that complements satellite-based methods. Further development using big data and machine learning, as well as cross-institutional collaboration, is needed for optimal implementation on a large scale.
References
Atanasov, V., & Sivkov, Y. (2024). Evaluating Yolov5 models on thermal imagery for aerial drone applications. 2024 23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024 - Proceedings. https://doi.org/10.1109/SIELA61056.2024.10637871
Borah, A. R., Ravi, N., Narayan, N., & D'Souza, P. J. (2024). A methodology for forest fire detection and notification using AI and IoT approaches. 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024, 168–173. https://doi.org/10.1109/IDCIoT59759.2024.10467597
Cao, Y., & Lei, R. (2024). Progress and prospect of forest fire monitoring based on the multi-source remote sensing data [多源遥感森林火情监测的进展与展望]. National Remote Sensing Bulletin, 28(8), 1854–1869. https://doi.org/10.11834/jrs.20242314
Dixit, J., Bhardwaj, A. K., Gupta, S. K., Singh, S. K., Meraj, G., Kumar, P., Kanga, S., Singh, S., & Sajan, B. (2024). Potential of lightweight drones and object-oriented image segmentation in forest plantation assessment. Remote Sensing, 16(9), 1554. https://doi.org/10.3390/rs16091554
Guevara-Bonilla, M., Ortiz-Malavasi, E., Hernández-Cole, J., & Villalobos-Barquero, V. (2023). Unmanned aerial vehicles to monitor the nutritional and phytosanitary status of forest crops. Colombia Forestal, 26(1), 123–133. https://doi.org/10.14483/2256201X.19250
Harrison, M. E., Deere, N. J., Imron, M. A., Nasir, D., Adul, A. H. A., Soler, J. A., Boyd, N. C., Cheyne, S. M., Collins, S. A., D'Arcy, L. J., Erb, W. M., Green, H., Healy, W., Hendri, H. B., Houlihan, P. R., Husson, S. J., Iwan, J. K. A., Kulu, I. P., Kusin, K., ... Struebig, M. J. (2024). Impacts of fire and prospects for recovery in a tropical peat forest ecosystem. Proceedings of the National Academy of Sciences, 121(17), e2307216121. https://doi.org/10.1073/pnas.2307216121
Jemmali, M., Melhim, L. K. B., Boulila, W., Amdouni, H., & Alharbi, M. T. (2023). Optimizing forest fire prevention: Intelligent scheduling algorithms for drone-based surveillance system. Procedia Computer Science, 225, 1562–1571. https://doi.org/10.1016/j.procs.2023.10.145
Jha, C. M. (2015). Thermal sensors: Principles and applications for semiconductor industries. Springer. https://doi.org/10.1007/978-1-4939-2581-0
Kandhari, A., Ginkal, P. M., & Kalaiselvi, K. (2024). Forest fire detection using AI. 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, 1, 2857–2866. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208781064
Kapoor, A., & Kumar, D. (2024). Efficient drone-based mobile wireless sensor network for spatio-temporal environmental monitoring. International Symposium on Advanced Networks and Telecommunication Systems, ANTS. https://doi.org/10.1109/ANTS63515.2024.10898866
Karjalainen, V., Koivumäki, N., Hakala, T., George, A., Muhojoki, J., Hyyppa, E., Suomalainen, J., & Honkavaara, E. (2024). Autonomous robotic drone system for mapping forest interiors. ISPRS Archives, 48(2), 167–172. https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-167-2024
Laurance, W. F. (2004). Rapid land-use change and its impacts on tropical biodiversity. In G. A. Bradshaw & P. A. Marquet (Eds.), Global change and the biodiversity of tropical ecosystems (pp. 189–199). American Geophysical Union. https://doi.org/10.1029/153GM15
Luo, Z., & Shang, W. (2018). Bioinspired thermal detection. In Bioinspired engineering of thermal materials (pp. 175–200). Wiley. https://doi.org/10.1002/9783527687596.ch8
Mashraqi, A. M., Asiri, Y., Algarni, A. D., & Abu-Zinadah, H. (2022). Drone imagery forest fire detection and classification using modified deep learning model. Thermal Science, 26(S1), S411–S423. https://doi.org/10.2298/TSCI22S1411M
Maurya, N. K., Tripathi, A. K., Chauhan, A., Pandey, P. C., & Lamine, S. (2022). Recent advancement and role of drones in forest monitoring: Research and practices. In Advances in remote sensing for forest monitoring (pp. 223–254). Wiley. https://doi.org/10.1002/9781119788157.ch11
Nurdiati, S., Sopaheluwakan, A., & Septiawan, P. (2022). Joint pattern analysis of forest fire and drought indicators in Southeast Asia associated with ENSO and IOD. Atmosphere, 13(8), 1198. https://doi.org/10.3390/atmos13081198
Özer, M. M. (2024). Internet of Things-based drone case study for atmospheric data collection. In Lecture notes in networks and systems (Vol. 1138, pp. 507–521). Springer. https://doi.org/10.1007/978-3-031-70924-1_38
Raj, E. F. I., Manimala, K., Rani, E. F. I., & Darwin, S. (2024). Environmental monitoring and conservation using drones. In Drone applications for Industry 5.0 (pp. 358–380). IGI Global. https://doi.org/10.4018/979-8-3693-2093-8.ch019
Rao, S., Zhao, X., & Guo, Y. (2021). Deployment of wildfire monitoring and alerting drone network. In Proceedings of 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT 2021) (pp. 1183–1189). https://doi.org/10.1109/ICCASIT53235.2021.9633574
Reddington, C. L., Conibear, L., Robinson, S., Knote, C., Arnold, S. R., & Spracklen, D. V. (2021). Air pollution from forest and vegetation fires in Southeast Asia disproportionately impacts the poor. GeoHealth, 5(9), e2021GH000418. https://doi.org/10.1029/2021GH000418
Smith, W., & Dressler, W. H. (2020). Forged in flames: Indigeneity, forest fire and geographies of blame in the Philippines. Postcolonial Studies, 23(4), 527–545. https://doi.org/10.1080/13688790.2020.1745620
Souza, C. R., Maia, V. A., Mariano, R. F., Coelho de Souza, F., Araújo, F. D. C., Paula, G. G. P. D., Menino, G. C. D. O., Coelho, P. A., Santos, P. F., Morel, J. D., & Santos, R. M. (2022). Tropical forests in ecotonal regions as a carbon source linked to anthropogenic fires: A 15-year study case in Atlantic forest–Cerrado transition zone. Forest Ecology and Management, 519, 120326. https://doi.org/10.1016/j.foreco.2022.120326
Velasco, J. A. H., & Guevara, H. S. D. (2024). Development of UAVs/drones equipped with thermal sensors for the search of individuals lost under rubble due to earthquake collapses or any eventuality requiring such capabilities. ICAS Proceedings. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208781511
Velichkova, R., Gieva, E., & Simova, I. (2024). Assessment and analysis of forest fires in Bulgaria. Engineering Proceedings, 60(1), 2. https://doi.org/10.3390/engproc2024060002
Wallerman, J., Bohlin, J., Nilsson, M. B., & Fransson, J. E. S. (2018). Drone-based forest variables mapping of ICOS tower surroundings. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 9003–9006). https://doi.org/10.1109/IGARSS.2018.8518895
Xu, L., Huang, J., Jiang, T., Zhang, L., & Huang, J. (2020). Thermally invisible sensors. EPL, 132(1), 14002. https://doi.org/10.1209/0295-5075/132/14002
Yang, G., Ning, J.-B., Shu, L.-F., He, C., & Di, X.-Y. (2017). Study on the accuracy of forest fire prediction by satellite hot spots in Daxing’an Mountains of Heilongjiang, northeastern China. Journal of Beijing Forestry University, 39(12), 1–9. https://doi.org/10.13332/j.1000-1522.20170147
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of New Trends in Sciences
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
