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

被引:2
|
作者
Kinoshita, Natalia Yukari Kume [1 ]
Schmith, Jean [1 ,3 ]
Martins, Eduardo Augusto [1 ,2 ]
de Figueiredo, Rodrigo Marques [1 ,3 ]
机构
[1] Unisinos Univ, Polytech Sch, Ave Unisinos 950, BR-93022750 Sao Leopoldo, RS, Brazil
[2] Unisinos Univ, ittFuse Inst, Ave Unisinos 950, BR-93022750 Sao Leopoldo, RS, Brazil
[3] Unisinos Univ, Technol Automat & Elect Lab, TECAE Lab, Ave Unisinos 950, BR-93022750 Sao Leopoldo, RS, Brazil
关键词
Vegetation identification; Vegetation encroachment; Faults by vegetation; Distribution power lines; Remote sensing;
D O I
10.1007/s40313-023-01035-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1284 / 1293
页数:10
相关论文
共 50 条
  • [21] Study of estimating directional vegetation fractional cover using remote sensing method
    Tian, J
    Zhang, RH
    Zhu, ZL
    IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET, 2004, : 4348 - 4351
  • [22] Derivative vegetation indices as a new approach in remote sensing of vegetation
    Kochubey, Svetlana M.
    Kazantsev, Taras A.
    FRONTIERS OF EARTH SCIENCE, 2012, 6 (02) : 188 - 195
  • [23] Derivative vegetation indices as a new approach in remote sensing of vegetation
    Svetlana M. Kochubey
    Taras A. Kazantsev
    Frontiers of Earth Science, 2012, 6 : 188 - 195
  • [24] Effects of Geomorphic Spatial Differentiation on Vegetation Distribution Based on Remote Sensing and Geomorphic Regionalization
    Xu, Hua
    Cheng, Weiming
    Wang, Baixue
    Song, Keyu
    Zhang, Yichi
    Wang, Ruibo
    Bao, Anming
    REMOTE SENSING, 2024, 16 (06)
  • [25] Comparison of remote sensing data sources and techniques for identifying and classifying alien invasive vegetation in riparian zones
    Rowlinson, LC
    Summerton, M
    Ahmed, F
    WATER SA, 1999, 25 (04) : 497 - 500
  • [26] A secure distribution method for remote sensing image based on content
    Xu, Yanyan
    Wang, Huiying
    Xu, Zhengquan
    Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University, 2013, 38 (12): : 1475 - 1479
  • [27] Identifying urban vegetation stress factors based on open access remote sensing imagery and field observations
    Carlan, Irina
    Mihai, Bogdan-Andrei
    Nistor, Constantin
    Grosse-Stoltenberg, Andre
    ECOLOGICAL INFORMATICS, 2020, 55
  • [28] Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors
    Mills, Steven J.
    Castro, Marcos P. Gerardo
    Li, Zhengrong
    Cai, Jinhai
    Hayward, Ross
    Mejias, Luis
    Walker, Rodney A.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2010, 48 (09): : 3379 - 3390
  • [29] An identification method for dangerous lines under power flow transfer in a distribution network open circuit
    Liang X.
    Yang H.
    Xue B.
    Cheng X.
    Yang R.
    Fu D.
    Sun S.
    Sun Y.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2021, 49 (23): : 11 - 17
  • [30] Remote sensing of solar induced fluorescence of vegetation
    Smorenburg, K
    Courre'ges-Lacoste, GB
    Berger, M
    Buschmann, C
    Court, A
    Del Bello, U
    Langsdorf, G
    Lichtenthaler, HK
    Sioris, C
    Stoll, MP
    Visser, H
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY III, 2002, 4542 : 178 - 190