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A Method for Identifying Vegetation Under Distribution Power Lines by Remote Sensing

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Abstract

One of the major causes of interruption in distribution power lines is the vegetation encroachment. The vegetation management is challenging and demands efforts in trimming trees planning. The literature presents many methods for encroachment over power lines detection that depends on local installation and manipulation of equipment, which may be unfeasible. Thus, the remote sensing raises as an valuable solution. Therefore, this work proposed a remote sensing based method for identification of probable vegetation encroachment over distribution power lines. Since the free satellite images have low resolution considering the size of treetops, and the high-resolution ones are expensive, our method used the Google Earth images. From that images, texture features and support vector machines were used to identify regions with and without vegetation. The accuracy of the method was of 95% and F1-score above 92% for testing and validation datasets. The method is suitable for real-time application in tree trimming planning, in addition to opening up new possibilities for innovation in vegetation management.

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References

  • Ahmad, J., Malik, A. S., & Xia, L. (2011). Effective techniques for vegetation monitoring of transmission lines right-of-ways. In 2011 IEEE International Conference on Imaging Systems and Techniques (pp. 34–38). IEEE.

  • Ahmad, J., Malik, A. S., & Xia, L. (2011). Vegetation monitoring for high-voltage transmission line corridors using satellite stereo images. In 2011 National Postgraduate Conference (pp. 1–5). IEEE.

  • Ahmad, J., Malik, A. S., Abdullah, M. F., Kamel, N., & Xia, L. (2015). A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera. Pattern Analysis and Applications, 18(2), 419–440.

    Article  MathSciNet  Google Scholar 

  • Ahmad, J., Malik, A. S., Xia, L., & Ashikin, N. (2013). Vegetation encroachment monitoring for transmission lines right-of-ways: A survey. Electric Power Systems Research, 95, 339–352.

    Article  Google Scholar 

  • Bradski, G. (2000). The OpenCV library. Dr. Dobb’s Journal of Software Tools

  • Butler, D. (2006). Virtual globes: The web-wide world. Nature, 439(7078), 776–779.

    Article  Google Scholar 

  • Carvalho, F. B., Medeiros, T. I., & Rodriguez, Y. P. (2018). Monitoring system for vegetation encroachment detection in power lines based on wireless sensor networks. In 2018 41st International Conference on Telecommunications and Signal Processing (TSP) (pp. 1–4). IEEE

  • Chen, Y., Lin, J., & Liao, X. (2022). Early detection of tree encroachment in high voltage powerline corridor using growth model and UAV-borne lidar. International Journal of Applied Earth Observation and Geoinformation, 108, 102740.

    Article  Google Scholar 

  • Compieta, P., Di Martino, S., Bertolotto, M., Ferrucci, F., & Kechadi, T. (2007). Exploratory spatio-temporal data mining and visualization. Journal of Visual Languages & Computing, 18(3), 255–279.

    Article  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Article  MATH  Google Scholar 

  • Daily, W. (1999). Engineering justification for tree trimming [power system maintenance]. IEEE Transactions on Power Delivery, 14(4), 1511–1518.

    Article  Google Scholar 

  • Daubechies, I. (1998). Recent results in wavelet applications. In Wavelet Applications V (Vol. 3391, pp. 2–9). SPIE.

  • Daubechies, I. (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36(5), 961–1005.

    Article  MathSciNet  MATH  Google Scholar 

  • Daubechies, I. (1992). Ten Lectures on Wavelets. Philadelphia: SIAM.

    Book  MATH  Google Scholar 

  • Dibs, H., Ali, A. H., Al-Ansari, N., & Abed, S. A. (2023). Fusion landsat-8 thermal tirs and oli datasets for superior monitoring and change detection using remote sensing. Emerging Science Journal, 7(2), 428–444.

    Article  Google Scholar 

  • Fang, S., Xiaoyu, W., Haiyang, C., Sheng, L., Lei, Z., & Yongxin, F. (2020). Research and advances in vegetation management for power line corridor monitoring. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) (Vol. 1, pp. 146–149). IEEE.

  • Gazzea, M., Aalhus, S., Kristensen, L. M., Ozguven, E. E., & Arghandeh, R. (2021). Automated 3D vegetation detection along power lines using monocular satellite imagery and deep learning. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 3721–3724). IEEE

  • Guan, H., Sun, X., Su, Y., Hu, T., Wang, H., Wang, H., Peng, C., & Guo, Q. (2021). Uav-lidar aids automatic intelligent powerline inspection. International Journal of Electrical Power & Energy Systems, 130, 106987.

    Article  Google Scholar 

  • Guralnick, R. P., Hill, A. W., & Lane, M. (2007). Towards a collaborative, global infrastructure for biodiversity assessment. Ecology Letters, 10(8), 663–672.

    Article  Google Scholar 

  • Jabal, Z. K., Khayyun, T. S., & Alwan, I. A. (2022). Impact of climate change on crops productivity using modis-ndvi time series. Civil Engineering Journal, 8(06)

  • Jaramillo-Leon, B., & Leite, J. B. (2022). Multi-objective optimization for preventive tree trimming scheduling in overhead electric power distribution networks. Journal of Control, Automation and Electrical Systems, 1–11.

  • Jardini, M. G. M., Jacobsen, R. M., Jardini, J. A., Magrini, L. C., Masuda, M., Silva, P. L., Quintanilha, J. A., & Beltrame, A. M. K. (2007). Information system for the vegetation control of transmission lines right-of-way. In 2007 IEEE Lausanne Power Tech (pp. 28–33). IEEE.

  • Jenssen, R., Roverso, D., et al. (2018). Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning. International Journal of Electrical Power & Energy Systems, 99, 107–120.

    Article  Google Scholar 

  • Kadhim, N., Ismael, N. T., & Kadhim, N. M. (2022). Urban landscape fragmentation as an indicator of urban expansion using sentinel-2 imageries. Civil Engineering Journal, 89, 1799–1814.

    Article  Google Scholar 

  • Kerscher, P. J. P., Schmith, J., Martins, E. A., Figueiredo, R. M., & Keller, A. L. (2022). Steel type determination by spark test image processing with machine learning. Measurement, 187, 110361.

    Article  Google Scholar 

  • Lee, G., Gommers, R., Waselewski, F., Wohlfahrt, K., & O’Leary, A. (2019). Pywavelets: A python package for wavelet analysis. Journal of Open Source Software, 4(36), 1237.

    Article  Google Scholar 

  • Louit, D., Pascual, R., & Banjevic, D. (2009). Optimal interval for major maintenance actions in electricity distribution networks. International Journal of Electrical Power & Energy Systems, 31(7–8), 396–401.

    Article  Google Scholar 

  • Ma, J., Cheng, J. C., Jiang, F., Gan, V. J., Wang, M., & Zhai, C. (2020). Real-time detection of wildfire risk caused by powerline vegetation faults using advanced machine learning techniques. Advanced Engineering Informatics, 44, 101070.

    Article  Google Scholar 

  • Mahdi Elsiddig Haroun, F., Mohamed Deros, S. N., Bin Baharuddin, M. Z., & Md Din, N. (2021). Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach. Energies, 14(12), 3393.

    Article  Google Scholar 

  • Mallat, S. G. (1989). Multiresolution approximations and wavelet orthonormal bases of \(l^2\) (r). Transactions of the American Mathematical Society, 315(1), 69–87.

    MathSciNet  MATH  Google Scholar 

  • Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.

    Article  MATH  Google Scholar 

  • Mallat, S. (1999). A Wavelet Tour of Signal Processing. Burlington: Elsevier.

  • Matikainen, L., Lehtomäki, M., Ahokas, E., Hyyppä, J., Karjalainen, M., Jaakkola, A., Kukko, A., & Heinonen, T. (2016). Remote sensing methods for power line corridor surveys. ISPRS Journal of Photogrammetry and Remote sensing, 119, 10–31.

  • Medeiros, T. Í. O., Rodriguez, Y. P. M., Carvalho, F. B. S., Souza, C. P., & Andrade, P. H. M. (2018). Vegetation encroachment monitoring system for transmission lines using wireless sensor networks. In 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1–5). IEEE.

  • Munir, N., Awrangjeb, M., & Stantic, B. (2020). An improved method for pylon extraction and vegetation encroachment analysis in high voltage transmission lines using lidar data. In 2020 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). IEEE.

  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

    MathSciNet  MATH  Google Scholar 

  • Ponti, M. A., Santos, F. P., Ribeiro, L. S., & Cavallari, G. B. (2021). Training deep networks from zero to hero: Avoiding pitfalls and going beyond. In 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 9–16). IEEE.

  • Rong, S., & He, L. (2020). A joint faster RCNN and stereovision algorithm for vegetation encroachment detection in power line corridors. In: 2020 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1–5). IEEE.

  • Rong, S., He, L., Du, L., Li, Z., & Yu, S. (2020). Intelligent detection of vegetation encroachment of power lines with advanced stereovision. IEEE Transactions on Power Delivery, 36(6), 3477–3485.

    Article  Google Scholar 

  • Sikorska-Łukasiewicz, K. (2020). Methods of automatic vegetation encroachment detection for high voltage power lines. In Radioelectronic Systems Conference 2019 (Vol. 11442, pp. 481–486). SPIE.

  • Sittithumwat, A., Soudi, F., & Tomsovic, K. (2004). Optimal allocation of distribution maintenance resources with limited information. Electric Power Systems Research, 68(3), 208–220.

    Article  Google Scholar 

  • Vemula, S., & Frye, M. (2021). Multi-head attention based transformers for vegetation encroachment over powerline corriders using UAV. In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) (pp. 1–5). IEEE.

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Correspondence to Jean Schmith.

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Kinoshita, N.Y.K., Schmith, J., Martins, E.A. et al. A Method for Identifying Vegetation Under Distribution Power Lines by Remote Sensing. J Control Autom Electr Syst 34, 1284–1293 (2023). https://doi.org/10.1007/s40313-023-01035-z

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