Extraction and analysis of tree canopy height information in high-voltage transmission-line corridors by using integrated optical remote sensing and LiDAR

Hao, Jinpeng; Li, Xiuguang; Wang, Hong; Yang, Kai; Zeng, Yumeng; Wang, Yu; Pan, Yuanjin

doi:10.1016/j.geog.2022.11.008

Cited by 7 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Especially in mountainous areas, forest areas, unmanned areas and other areas with complex terrain, in where the transportation is inconvenient, the communication is poor and the existing methods are difficult to meet the normalization monitoring of transmission channel obstacles. three-dimensional surveying satellites have entered the market, providing important data support for large-scale regular monitoring of transmission channel obstacles [5]. The essence of 3D tree obstacle inspection technology based on satellite remote sensing stereo pairs is to model the transmission line towers, wires, and trees in 3D [6].…”

Section: Introductionmentioning

confidence: 99%

Research on 3D monitoring method of tree barrier based on satellite remote sensing fusion of transmission tower features

Zhang,

Dou,

Yang

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

With the construction of city and the continuous expansion of power grid assets, transmission lines are increasingly radiating and expanding from urban areas to suburbs, mountainous areas, and even unmanned areas. Overhead transmission lines exposed in the wild are often susceptible to the impact of tree barriers. When the trees around the transmission line grow to a certain height, causing the distance between the wires and the trees to be too small, it can cause the wires to discharge from the trees, leading to accidents such as short circuits and trips. Therefore, the investigation of tree barriers is a highly concerned issue for various provincial companies. Based on the advantages of satellite remote sensing, such as wide coverage and unrestricted environmental conditions, this article proposes a three-dimensional monitoring method for tree obstacle based on satellites remote sensing images that integrates tower features.The effectiveness of the proposed method in this paper is verified by conducting experiments on a 500 kV transmission line in Chongqing and comparing it with unmanned aerial vehicle monitoring methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on 3D monitoring method of tree barrier based on satellite remote sensing fusion of transmission tower features

Zhang,

Dou,

Yang

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

show abstract

“…Airborne LiDAR point cloud data is widely utilized for measuring the geometric characteristics of tree canopies [1][2][3][4][5], significantly enhancing the accuracy of estimating forest canopy cover [6][7][8], forest biomass [9], and carbon sinks [10]. In particular, the canopy height model (CHM), which is derived from LiDAR point clouds, is valuable for accurately estimating the true physical height of trees within forested areas [11,12]. This capability greatly contributes to the efficient extraction of individual tree parameters [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

Zhou,

Li,

Huang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called “pixels weighted differential gradient”, to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction.

show abstract

“…With the growth of society's demand for electricity resources, the transmission line corridor has been greatly expanded. In order to ensure safe transmission lines for electricity, the relevant departments invest significant manpower and financial resources to inspect transmission lines every year [1][2][3][4]. The traditional manual inspection method has a high operating intensity, a high risk factor, a low inspection efficiency, and poor line coverage.…”

Section: Introductionmentioning

confidence: 99%

Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR

Xi,

Zhang,

Niu

et al. 2023

Sensors

View full text Add to dashboard Cite

Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear–catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor’s reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor’s reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method.

show abstract

Extraction and analysis of tree canopy height information in high-voltage transmission-line corridors by using integrated optical remote sensing and LiDAR

Cited by 7 publications

References 16 publications

Research on 3D monitoring method of tree barrier based on satellite remote sensing fusion of transmission tower features

Research on 3D monitoring method of tree barrier based on satellite remote sensing fusion of transmission tower features

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR

Contact Info

Product

Resources

About