
Research on Accurate Fire Point Positioning Technology in the Forest Region of Northeast China Based on Aerial Photography
DOI:
https://doi.org/10.30564/jees.v8i5.13175Abstract
Forest fires are one of the most destructive natural disasters to global ecosystems, and accurate and rapid fire point positioning is crucial for forest fire management. In this study, we propose an accurate fire point positioning technology based on aerial photography. This method uses a high-accuracy Global Positioning System and an inertial measurement unit to record the flight attitude and location information of an aircraft, and then it employs precise correction to convert camera coordinates to the aircraft body coordinate system. Finally, this method utilizes a weighted error correction algorithm to fuse the information from multiple images, thereby achieving accurate fire point positioning. To verify the effectiveness of the fire point positioning method proposed in this research, two fire points in the Greater Khingan Region of Heilongjiang Province are selected for verification. A Yun-12 aircraft equipped with a high-accuracy positioning and image-recording sensor was used for fire point observations. The longitude, latitude, flight altitude, roll angle, pitching angle and yaw angle of the aircraft are recorded in real time during image capture. Images of fire points at different angles were captured from multiple flight routes, and they are used to verify the performance and accuracy of the fire point positioning model. The comparison of the latitude and longitude information between estimated and observed fire points indicates that the errors of fire point positioning are reduced to less than 160 m in the model. This represents a 5–6 fold improvement in positioning accuracy over current meteorological-satellite-based kilometer-scale fire point positioning methods, highlighting the notable potential of this technique for precise forest fire positioning.
Keywords:
Fire Point Positioning; Coordinate Conversion; Error Correction; MeasurementReferences
[1] Certini, G., 2005. Effects of Fire on Properties of Forest Soils: A Review. Oecologia. 143(1), 1–10.
[2] Bowman, D.M.J.S., Balch, J.K., Artaxo, P., et al., 2009. Fire in the Earth system. Science. 324(5926), 481–484.
[3] Arana, P.V., Cabrera, A.F., Perez, M.J., et al., 2018. Challenges of an autonomous wildfire geolocation system based on synthetic vision technology. Sensors. 18(11), 3631.
[4] Zhang, Z., Zhang, T., Zeng, W., et al., 2025. Application Progress of Forest Fire Monitoring Based on Satellite Remote Sensing. Journal of Southwest Forestry University. 45(6), 211–220. (in Chinese)
[5] Roberts, G., Wooster, M.J., 2014. Development of a multi-temporal Kalman filter approach to geostationary active fire detection & fire radiative power (FRP) estimation. Remote Sensing of Environment. 152, 392–412.
[6] Liu, X., He, B., Quan, X., et al., 2018. Near real-time extracting wildfire spread rate from Himawari-8 satellite data. Remote Sensing. 10(10), 1654.
[7] Moreno, M.V., Laurent, P., Ciais, P., et al., 2020. Assessing satellite-derived fire patches with functional diversity trait methods. Remote Sensing of Environment. 247, 111897.
[8] Zheng, W., Chen, J., Tang, S.H., et al., 2020. Fire monitoring based on FY-3D/MERSI-II far-infrared data. Journal of Infrared and Millimeter Waves. 39(1), 120–127. (in Chinese)
[9] Roberts, G., Wooster, M.J., Perry, G.L.W., et al., 2005. Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI imagery. Journal of Geophysical Research: Atmospheres. 110, D21111.
[10] Rao, Y., Wang, C., Huang, H., 2020. Forest fire monitoring based on multisensor remote sensing techniques in Muli County, Sichuan Province. Journal of Remote Sensing. 24(5), 559–570. (in Chinese)
[11] Chung, Y.S., Kim, H.S., 2008. Satellite monitoring of forest fires and associated smoke plumes occurring in Korea. Air Quality, Atmosphere & Health. 1(2), 111–118.
[12] Jones, H.G., Sirault, X.R.R., 2014. Scaling of Thermal Images at Different Spatial Resolution: The Mixed Pixel Problem. Agronomy. 4(3), 380–396.
[13] Güney, O.C., Mert, A., Gülsoy, S., 2023. Assessing fire severity in Turkey's forest ecosystems using spectral indices from satellite images. Journal of Forestry Research. 34(6), 1747–1761.
[14] Lin, Z., Chen, F., Niu, Z., et al., 2018. An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data. Remote Sensing of Environment. 211, 376–387.
[15] Chen, Y., Morton, D.C., Randerson, J.T., 2024. Remote sensing for wildfire monitoring: Insights into burned area, emissions, and fire dynamics. One Earth. 7(6), 1022–1028.
[16] Zhao, Y., Ma, J., Li, X., et al., 2018. Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. Sensors. 18(3), 712.
[17] Bouguettaya, A., Zarzour, H., Taberkit, A.M., et al., 2022. A Review on Early Wildfire Detection from Unmanned Aerial Vehicles using Deep Learning-Based Computer Vision Algorithms. Signal Processing. 190, 108309.
[18] Titu, M.F.S., Pavel, M.A., Michael, G.K.O., et al., 2024. Real-Time Fire Detection: Integrating Lightweight Deep Learning Models on Drones with Edge Computing. Drones. 8(9), 483.
[19] Shamsoshoara, A., Afghah, F., Razi, A., et al., 2021. Aerial imagery pile burn detection using deep learning: The FLAME dataset. Computer Networks. 193, 108001.
[20] Xu, Y., Li, J., Zhang, F., 2022. A UAV-Based Forest Fire Patrol Path Planning Strategy. Forests. 13(11), 1952.
[21] Wijikumar, P., Partheepan, S., Hassan, J., et al., 2025. Transfer Learning-Enhanced Gradient Boosting Models for Wildfire Detection Using UAV Imagery. In Proceedings of the 26th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Fort Worth, TX, USA, 27–30 May 2025; pp. 311–316.
[22] Viseras, A., Marchal, J., Schaab, M., et al., 2019. Wildfire monitoring and hotspots detection with aerial robots: Measurement campaign and first results. In Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Würzburg, Germany, 2–4 September 2019; pp. 102–103.
[23] Marčiš, M., Fraštia, M., Lieskovský, T., et al., 2024. Photogrammetric Measurement of Grassland Fire Spread: Techniques and Challenges with Low-Cost Unmanned Aerial Vehicles. Drones. 8(7), 282.
[24] Wei, Y., Kang, L., Yang, B., et al., 2013. Applications of structure from motion: A survey. Journal of Zhejiang University-Science C (Computers & Electronics). 14(7), 486–494.
[25] Iglhaut, J., Cabo, C., Puliti, S., et al., 2019. Structure from Motion Photogrammetry in Forestry: A Review. Current Forestry Reports. 5(3), 155–168.
[26] Fernández, G.J.M., Calvo, L., Pérez, R.L.A., et al., 2024. Integrating Physical-Based Models and Structure-from-Motion Photogrammetry to Retrieve Fire Severity by Ecosystem Strata from Very High Resolution UAV Imagery. Fire. 7(9), 304.
[27] Özyeşil, O., Voroninski, V., Basri, R., et al., 2017. A survey of structure from motion. Acta Numerica. 26, 305–364.
[28] Cao, M., Cao, L., Jia, W., et al., 2018. Evaluation of local features for structure from motion. Multimedia Tools and Applications. 77(9), 10979–10993.
[29] Umar, M.M., De Silva, L.C., 2017. Fire boundary detection method using a unique structure from motion for non-rigid bodies algorithm (SFM-NRBA). In Proceedings of the 9th International Conference on Signal Processing Systems, Auckland, New Zealand, 27–30 Novemnber 2017; pp. 74–78.
[30] Mlambo, R., Woodhouse, I.H., Gerard, F., et al., 2017. Structure from motion (SfM) photogrammetry with drone data: A low cost method for monitoring greenhouse gas emissions from forests in developing countries. Forests. 8(3), 68.
[31] Losso, A., Corgnati, L., Perona, G., 2010. Innovative image geo-referencing tool for decision support in wildfire fighting. WIT Transactions on Ecology and the Environment. 137, 173–183.
[32] Sargento, F., Ribeiro, R., Cherif, E.K., et al., 2022. Real-Time Georeferencing of Fire Front Aerial Images Using Structure from Motion and Iterative Closest Point. In: Rousseau, J.J., Kapralos, B. (Eds.). Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022), Vol. 13644: Lecture Notes in Computer Science. Springer: Cham, Switzerland. 194–202.
[33] El-Sheimy, N., Wright, B., 2005. The development and testing of a prototype mobile mapping system for real-time forest fire hot spot detection. Photogrammetric Engineering & Remote Sensing. 71(4), 461–470.
[34] Santana, B., Cherif, E.K., Bernardino, A., et al., 2022. Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter. Sensors. 22(3), 1150.
[35] Guo, C., Lu, F., Deng, Z., et al., 2023. Fast and Reliable Convergence of Real-Time Kinematic Single Point Positioning Using Equivalent Elimination Principle. Geomatics and Information Science of Wuhan University. 48(7), 1117–1125. (in Chinese)
[36] Chen, X., Hopkins, B., Wang, H., et al., 2022. Wildland fire detection and monitoring using a drone-collected RGB/IR image dataset. In Proceedings of the 2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, 11–13 October 2022; pp. 1–4.
[37] Partheepan, S., Sanati, F., Hassan, J., 2023. Autonomous Unmanned Aerial Vehicles in Bushfire Management: Challenges and Opportunities. Drones. 7(47), 47.
[38] Chuvieco, E., Aguado, I., Salas, J., et al., 2020. Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports. 6(2), 81–96.
[39] Zhang, Z., 1999. Flexible camera calibration by viewing a plane from unknown orientations. In Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 20–27 September 1999; pp. 666–673.
[40] Wang, D., Jia, Z., Xia, Y., et al., 2021. Research Progress and Trend in Forestry and Grassland Fires Monitoring Technology. World Forestry Research. 34(2), 26–32. (in Chinese)
[41] Yogender, Raghavendra, S., Kushwaha, S.K.P., 2020. Role of Ground Control Points (GCPs) in Integration of Terrestrial Laser Scanner (TLS) and Close-range Photogrammetry (CRP). In: Ghosh, J.K., da Silva, I. (Eds.). Applications of Geomatics in Civil Engineering: Select Proceedings of ICGCE 2018, Vol. 33: Lecture Notes in Civil Engineering. Springer: Singapore. pp. 531–537.
[42] He, Y., Yang, J., Ma, Y., et al., 2016. A method for fire detection using Landsat 8 data. Journal of Infrared and Millimeter Waves. 35(5), 600–608, 624. (in Chinese)
[43] Freeborn, P.H., Wooster, M.J., Roy, D.P., et al., 2014. Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite‐based active fire characterization and biomass burning estimation. Geophysical Research Letters. 41(6), 1988–1994.
[44] Fu, Y., Li, R., Wang, X., et al., 2020. Fire Detection and Fire Radiative Power in Forests and Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products. Remote Sensing. 12(18), 2870.
[45] Giitsidis, T., Karakasis, E.G., Gasteratos, A., et al., 2015. Human and Fire Detection from High Altitude UAV Images. 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Turku, Finland, 4–6 March 2015; pp. 309–315.
[46] Alexis, K., Nikolakopoulos, G., Tzes, A., et al., 2009. Coordination of Helicopter UAVs for Aerial Forest-Fire Surveillance. In: Valavanis, K.P. (Ed.). Applications of Intelligent Control to Engineering Systems. Intelligent Systems, Control, and Automation: Science and Engineering, Vol. 39. Springer: Dordrecht, The Netherlands. pp. 169–193.
[47] Luan, T., Zhou, S., Zhang, G., et al., 2024. Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery. Sensors. 24(9), 2710.
[48] Kumar, M., Cohen, K., 2009. Wild Land Fire Fighting Using Multiple Uninhabited Aerial Vehicles. In Proceedings of the AIAA Infotech @ Aerospace Conference, Seattle, WA, USA, 6–9 April 2009.
[49] Abdusalomov, A., Umirzakova, S., Bakhtiyor, S.M., et al., 2024. Drone-Based Wildfire Detection with Multi-Sensor Integration. Remote Sensing. 16(24), 4651.
Downloads
How to Cite
Issue
Article Type
License
Copyright © 2026 Yingyu An, Yang Ju, Shuxin Han, Shuang Wu, Xiaolei Su

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.




Yingyu An