An Integrated Multi-Sensor UAV System for Early Wildfire Detection and Risk Assessment
DOI:
https://doi.org/10.58445/rars.4317Keywords:
UAV, Unmanned Aerial Vehicle, Wildfire Detection, Wildfire Risk Assessment, Remote Sensing, Thermal Imaging, Multispectral Imaging, LiDAR, Environmental Monitoring, Machine Learning, Sensor FusionAbstract
Wildfires are increasing in frequency and severity throughout California due to anthropogenic climate change (e.g., prolonged drought) and expanding development near wildland areas, creating an urgent need for improved methods of identifying wildfire risk before ignition. Existing satellite and ground-based monitoring systems provide valuable information but are often limited by low spatial resolution or delayed detection after combustion has begun. Specifically, from a satellite's perspective, high optical depth from wildfire smoke significantly disrupts hotspot and active wildfire detection. This project presents a conceptual design of an integrated unmanned aerial vehicle (UAV) platform that combines sensing approaches, including weather sensors, multispectral imaging, light detection and ranging (LiDAR), thermal imaging, and air-quality sensors, to monitor environmental conditions associated with wildfire ignition. Machine learning approaches are incorporated as a proposed component for analyzing the collected data, identifying patterns linked to wildfire risk, and supporting earlier detection of developing fires. The resulting research-supported UAV design demonstrates how multiple sensing technologies can be merged into a unified wildfire prediction and detection system. Such a platform could improve wildfire risk assessment, enable earlier mitigation strategies, and support more effective emergency response while reducing wildfire impacts on ecosystems and nearby communities. Additionally, the platform could be scaled through the coordinated deployment of multiple UAVs to increase aerial coverage and enable broader monitoring across wildfire-prone regions.
References
Abatzoglou, J. T., & Williams, A. P. (2016). Climate change has added to western US forest wildfire. Proceedings of the National Academy of Sciences, 113(42), 11770–11775. https://doi.org/10.1073/pnas.1607171113
Barber, N., Alvarado, E., Kane, V. R., Mell, W. E., & Moskal, L. M. (2021). Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors. Sensors, 21(19), Article 6350. https://doi.org/10.3390/s21196350
Boroujeni, S. P. H., Razi, A., Khoshdel, S., Afghah, F., Coen, J. L., O’Neill, L., Fule, P. Z., Watts, A., Kokolakis, N.-M. T., & Vamvoudakis, K. G. (2024). A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management. Information Fusion, 108, Article 102369. https://doi.org/10.1016/j.inffus.2024.102369
Butler, B. W., Quarles, S., Standohar-Alfano, C., Morrison, M., Jimenez, D., Sopko, P. A., Wold, C., Bradshaw, L. S., Atwood, L., Landon, J., O’Brien, J., Hornsby, B., Wagenbrenner, N. S., & Page, W. (2020). Exploring fire response to high wind speeds: Fire rate of spread, energy release and flame residence time from fires burned in pine needle beds under winds up to 27 m s⁻¹. International Journal of Wildland Fire, 29(1), 81–92. https://doi.org/10.1071/WF18216
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
Coskuner, K. A., Papavasileiou, G., Giannaros, T. M., Benali, A., & Bilgili, E. (2026). Fire behavior and propagation of twin wildfires in a Mediterranean landscape: A case study from İzmir, Türkiye. Fire, 9(2), Article 86. https://doi.org/10.3390/fire9020086
Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning (pp. 233–240). Association for Computing Machinery. https://doi.org/10.1145/1143844.1143874
Dennison, P. E., Brewer, S. C., Arnold, J. D., & Moritz, M. A. (2014). Large wildfire trends in the western United States, 1984–2011. Geophysical Research Letters, 41(8), 2928–2933. https://doi.org/10.1002/2014GL059576
Fernández-Guisuraga, J. M., Sanz-Ablanedo, E., Suárez-Seoane, S., & Calvo, L. (2018). Using unmanned aerial vehicles in postfire vegetation survey campaigns through large and heterogeneous areas: Opportunities and challenges. Sensors, 18(2), Article 586. https://doi.org/10.3390/s18020586
Gutierrez, A. A., Hantson, S., Langenbrunner, B., Chen, B., Jin, Y., Goulden, M. L., & Randerson, J. T. (2021). Wildfire response to changing daily temperature extremes in California’s Sierra Nevada. Science Advances, 7(47), Article eabe6417. https://doi.org/10.1126/sciadv.abe6417
Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J., Williamson, G. J., & Bowman, D. M. J. S. (2015). Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications, 6, Article 7537. https://doi.org/10.1038/ncomms8537
Keane, R. E. (2013). Describing wildland surface fuel loading for fire management: A review of approaches, methods and systems. International Journal of Wildland Fire, 22(1), 51–62. https://doi.org/10.1071/WF11139
Keeley, J. E., & Syphard, A. D. (2016). Climate change and future fire regimes: Examples from California. Geosciences, 6(3), Article 37. https://doi.org/10.3390/geosciences6030037
Mohapatra, A., & Trinh, T. (2022). Early wildfire detection technologies in practice—A review. Sustainability, 14(19), Article 12270. https://doi.org/10.3390/su141912270
Potter, B. E. (2012). Atmospheric interactions with wildland fire behaviour—I. Basic surface interactions, vertical profiles and synoptic structures. International Journal of Wildland Fire, 21(7), 779–801. https://doi.org/10.1071/WF11128
Reid, A. M., Fuhlendorf, S. D., & Weir, J. R. (2010). Weather variables affecting Oklahoma wildfires. Rangeland Ecology & Management, 63(5), 599–603. https://doi.org/10.2111/REM-D-09-00132.1
Rjoub, D., Alsharoa, A., & Masadeh, A. (2023). Unmanned-aircraft-system-assisted early wildfire detection with air quality sensors. Electronics, 12(5), Article 1239. https://doi.org/10.3390/electronics12051239
Sadatrazavi, A., Shafie-Pour-Motlagh, M., Noorpoor, A., & Ehsani, A. H. (2022). Predicting wildfires occurrences using meteorological parameters. International Journal of Environmental Research, 16(6), Article 106. https://doi.org/10.1007/s41742-022-00460-3
Sharples, J. J., McRae, R. H. D., & Wilkes, S. R. (2012). Wind–terrain effects on the propagation of wildfires in rugged terrain: Fire channelling. International Journal of Wildland Fire, 21(3), 282–296. https://doi.org/10.1071/WF10055
Wang, Y., Wang, X., & Xing, J. (2026). Inversion of the forest dead fuel moisture content by UAV multisepectral image under the new leaves shade. Frontiers in Physics, 14, Article 1795521. https://doi.org/10.3389/fphy.2026.1795521
Williams, A. P., Abatzoglou, J. T., Gershunov, A., Guzman-Morales, J., Bishop, D. A., Balch, J. K., & Lettenmaier, D. P. (2019). Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Future, 7(8), 892–910. https://doi.org/10.1029/2019EF001210
Zhao, F., & Liu, Y. (2021). Important meteorological predictors for long-range wildfires in China. Forest Ecology and Management, 499, Article 119638. https://doi.org/10.1016/j.foreco.2021.119638
Downloads
Posted
License
Copyright (c) 2026 Research Archive of Rising Scholars
This work is licensed under a Creative Commons Attribution 4.0 International License.
