Power Line Corridor Research Articles

Maximizing benefits to bat populations through management of power line corridors

Abstract Power line corridors are ubiquitous worldwide and are commonly used by bats as habitat. Targeted management of these corridor habitats has the potential to aid bat populations, which is critically important given the multifaceted threats facing bat species, including the emerging infectious disease white‐nose syndrome (WNS) in North America. Here, we review known and potential impacts of management of existing power line corridors on three bat behaviours: foraging, roosting and commuting. We also identify bats in the United States that would benefit from changes to management of power line corridors for improvements to roosting and foraging habitats, particularly species of conservation concern that roost and forage along forest edges. Key recommendations are to avoid disturbance to roosting bats when maintaining vegetation along power line corridors, apply integrated vegetation management to maximize native plant diversity to improve prey options for bats and apply targeted interventions (e.g. artificial roost creation, creation of ponds) in a well‐justified ecological context. Practical implication. We highlight high‐priority research topics to fill knowledge gaps, including testing whether vegetation management treatments targeting plant and insect communities increase bat fitness and cause positive population‐level responses in focal bat species. We conclude that building evidence on how bats are affected by power line corridor management is a conservation need.

Effect of frequent clearcutting on some soil properties, temperatures, and herbaceous vegetation and the potential of their recovery in a short time.

Harvesting or degradation of forest ecosystems directly affects the microclimate, causing changes in air and soil temperatures and soil moisture in the forestlands. The objectives of this study were to investigate the effect of frequent clearcutting of forest cover on some selected soil properties, ambient and soil temperatures, soil moisture, and herbaceous vegetation cover and determine their recovery in a short period in the area subject to frequent clearcutting under the powerline corridors (PLCs). The study was conducted in the research forest of Istanbul University-Cerrahpaşa, Faculty of Forestry. The treatment plots were selected from the clearcut area, and control plots were selected from an untouched oak-hornbeam forestland. Soil temperature and moisture and maximum and minimum ambient temperatures were measured in the treatment and control plots between 2020 and 2021 and topsoil sampled between 2019 and 2021. Data were analyzed using analysis of variance (ANOVA) to test the effects of clearcutting on some selected soil properties in the short term after cutting. Clearcutting caused a significant increase in soil bulk density (BD) and a decrease in the soil total porosity (TP), soil hydraulic conductivity (HC), and saturation capacity (SC). Forest cover removal significantly decreased the soil organic matter (SOM) content by 3%, increased average soil temperature by 2.1°C, and the difference between maximum and minimum temperatures by 8.8°C. Additionally, clearcutting reduced the average soil moisture from 36 to 35%. The findings revealed that clearcutting negatively affected some hydro-physical soil properties and soil microclimate conditions that may not recover to their previous states within the next few years.

Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds.

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network's ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.

Safety monitoring method for powerline corridors based on single‐stage detector and visual matching

Safety monitoring method for powerline corridors based on single‐stage detector and visual matching

Tree barriers height information extraction under power line based on watershed segmentation and local maximum algorithm

ABSTRACT In view of the problem that the traditional power line inspection cannot accurately obtain the height of the tree barriers under the power line, this study takes a forest area in Guilin City and a power line corridor in Yinchuan City as the research object, and combines watershed segmentation algorithm and local maximum algorithm to study the height of the tree barriers under power line. The results show that the watershed segmentation algorithm and the local maximum algorithm can accurately extract the height of tree barriers in power lines. In the Guilin experiment, there is a good correlation between the extracted height of different trees and the measured height, The correlation coefficients (R2) for osmanthus, eucalyptus, and pine trees are 0.9368, 0.8923, and 0.8227, respectively. In the experiment in Yinchuan, the tree height extracted by unmanned aerial vehicles (UAV) and LiDAR also has a good correlation with the measured height, with correlation coefficients (R2) of 0.926 and 0.952, respectively. These two experiments have verified the feasibility of watershed segmentation algorithm and local maximum algorithm in extracting the height of tree barriers under power lines, which is of great significance for monitoring and warning of tree barriers under power lines.

VEPL-Net: A Deep Learning Ensemble for Automatic Segmentation of Vegetation Encroachment in Power Line Corridors Using UAV Imagery

Vegetation encroachment in power line corridors remains a major challenge for modern energy-dependent societies, as it can cause power outages and lead to significant financial losses. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for monitoring infrastructure, owing to their ability to acquire high-resolution overhead images of these areas quickly and affordably. However, accurate segmentation of the vegetation encroachment in this imagery is a challenging task, due to the complexity of the scene and the high pixel imbalance between the power lines, the vegetation and the background classes. In this paper, we propose a deep learning-based approach to tackle this problem caused by the original and different geometry of the objects. Specifically, we use DeepLabV3, U-Net and a modified version of the U-Net architecture with VGG-16 weights to train two separate models. One of them segments the dominant classes, the vegetation from the background, achieving an IoU of 0.77. The other one segments power line corridors from the background, obtaining an IoU of 0.64. Finally, ensembling both models into one creates an “encroachment” zone, where power lines and vegetation are intersected. We train our models using the Vegetation Encroachment in Power Line Corridors dataset (VEPL), which includes RGB orthomosaics and multi-label masks for segmentation. Experimental results demonstrate that our approach outperforms individual networks and original prominent architectures when applied to this specific problem. This approach has the potential to significantly improve the efficiency and accuracy of vegetation encroachment monitoring using UAV, thus helping to ensure the reliability and sustainability of power supply.

Mapping and monitoring of vegetation regeneration and fuel under major transmission power lines through image and photogrammetric analysis of drone-derived data

The use of drones and remote sensing in combination with geospatial analysis is a cost-efficient way to monitor energy distribution networks, especially those in fire-prone areas. This study investigated the use of image and photogrammetric analysis together with segmentation algorithms to assess vegetation height and volume in power line corridors in Southeast Queensland, Australia. Various fuel reduction techniques, including mega-mulching, spot sprays and cool mosaic burns, were implemented, and drone-generated models were employed to evaluate their effectiveness. The fuel hazard reduction and regrowth in terms of vegetation height and volume were recorded and analysed. Importantly, the study demonstrates a robust correlation (R 2 = 0.9073; df = 1,16; F = 156; p < .001) between field observations and drone-derived models, affirming the efficacy of this method in assessing fuel heights. This validation suggests that the approach could represent a viable, cost-efficient option for future monitoring and management of energy distribution networks in fire-prone areas.

VEPL Dataset: A Vegetation Encroachment in Power Line Corridors Dataset for Semantic Segmentation of Drone Aerial Orthomosaics

Vegetation encroachment in power line corridors has multiple problems for modern energy-dependent societies. Failures due to the contact between power lines and vegetation can result in power outages and millions of dollars in losses. To address this problem, UAVs have emerged as a promising solution due to their ability to quickly and affordably monitor long corridors through autonomous flights or being remotely piloted. However, the extensive and manual task that requires analyzing every image acquired by the UAVs when searching for the existence of vegetation encroachment has led many authors to propose the use of Deep Learning to automate the detection process. Despite the advantages of using a combination of UAV imagery and Deep Learning, there is currently a lack of datasets that help to train Deep Learning models for this specific problem. This paper presents a dataset for the semantic segmentation of vegetation encroachment in power line corridors. RGB orthomosaics were obtained for a rural road area using a commercial UAV. The dataset is composed of pairs of tessellated RGB images, coming from the orthomosaic and corresponding multi-color masks representing three different classes: vegetation, power lines, and the background. A detailed description of the image acquisition process is provided, as well as the labeling task and the data augmentation techniques, among other relevant details to produce the dataset. Researchers would benefit from using the proposed dataset by developing and improving strategies for vegetation encroachment monitoring using UAVs and Deep Learning.

Species accumulation in novel grassland habitats is linked to land cover history

Novel grassland habitats along linear infrastructure have gained attention as potential supplementary or replacement habitats for species suffering from the widespread loss of traditionally managed semi‐natural grasslands. However, it can take time for species to colonise new habitats, and both the historical and the current landscape composition can affect colonisation rates, particularly for grassland specialist species. We investigated the influence of more than 100 years of landscape history on present‐day species richness of plants, butterflies and bumblebees in two types of linear infrastructure habitats: road verges and power line corridors. We analysed the effects of time since habitat establishment and the amount of surrounding grassland cover in different time periods on species richness of the three taxa. Our results suggest a long time lag in the establishment of plant species in novel grassland habitats, with richness of butterflies and bumblebees largely dependent on the richness of plant species. There was no direct relationship between habitat age and the richness of butterflies or bumblebees, but we found unexpected legacy effects of grassland area on present‐day species richness. Higher richness of bumblebee species in road verges located in landscapes with larger surrounding grassland cover in the past is most likely a consequence of those landscapes containing a mosaic of afforested and abandoned grassland habitats today. Furthermore, we found that power line corridors might be important replacement habitats for butterflies in landscapes with little other grassland area left. Provided that road verges and power‐line corridors are managed appropriately, our results show that their species richness can be expected to increase in the future.

Communities in infrastructure habitats are species rich but only partly support species associated with semi‐natural grasslands

Abstract Biodiversity linked to traditionally managed semi‐natural grasslands is declining, despite conservation efforts. At the same time, the area of novel grassland habitats along linear infrastructure, such as road verges and power line corridors, is increasing and in some regions surpass the area of semi‐natural grasslands. An open question is to what extent these novel grasslands can complement or even replace traditionally managed grasslands as habitat for grassland species. We compared the alpha (species richness) and beta (abundance‐based dissimilarities) diversity of plants, bumblebees and butterflies among semi‐natural pastures, verges of small and big roads, power line corridors and uncultivated field borders nested within 32 landscapes (2 × 2 km squares). Landscapes had either high or low road density and were with or without power line corridors. Across landscapes there was also a gradient in the area of semi‐natural pastures. Alpha diversity of all three species groups was as high in power line corridors and verges of small roads as in semi‐natural pastures, regardless of landscape composition. Although all habitat types shared a large proportion of species, community composition differed among habitats for all three species groups. The beta diversity of plants and butterflies was driven primarily by the replacement of species (turnover), while the beta diversity of bumblebees was driven by a rarer occurrence of certain species in road verges (nestedness). This means that linear infrastructure habitats cannot fully replace the role of semi‐natural grasslands for plant and pollinator diversity. The area of road verges, power line corridors and semi‐natural pastures in the landscape influenced community composition of plants and butterflies, but not the similarity in community composition among habitats within landscapes. Policy implications. Although novel grasslands along linear infrastructures have high numbers of grassland species, they only support a part of the biodiversity found in traditionally managed semi‐natural grasslands. Therefore, protecting and restoring semi‐natural grasslands should continue to be a priority for the conservation of grassland biodiversity. However, especially in landscapes where the area of semi‐natural grasslands is low, road verges and power line corridors can be important habitats for a number of grassland plants and insects and should be managed to promote biodiversity.

Power Line Extraction and Reconstruction Methods from Laser Scanning Data: A Literature Review

Electricity has become an indispensable source of energy, and power lines play a crucial role in the functioning of modern societies. It is essential to inspect power lines promptly and precisely in order to ensure the safe and secure delivery of electricity. In steep and mountainous terrain, traditional surveying methods cannot inspect power lines precisely due to their nature. Remote sensing platforms, such as satellite and aerial images, thermal images, and light detection and ranging (LiDAR) points, were utilised for the detection and inspection of power lines. Nevertheless, with the advancements of remote sensing technologies, in recent years, LiDAR surveying has been favoured for power line corridor (PLC) inspection due to active and weather-independent nature of laser scanning. Laser ranging data and the precise location of the LiDAR can be used to generate a three-dimensional (3D) image of the PLC. The resulting 3D point cloud enables accurate extraction of power lines and measurement of their distances from the forest below. In the literature, there have been many proposals for power line extraction and reconstruction for PLC modelling. This article examines the pros and cons of each domain method, providing researchers involved in three-dimensional modelling of power lines using innovative LiDAR scanning systems with useful guidelines. To achieve these objectives, research papers were analysed, focusing primarily on geoscience-related journals and conferences for the extraction and reconstruction of power lines. There has been a growing interest in examining the extraction and reconstruction of power line spans with single and multi-conductor configurations using different image and point-based techniques. Our study provides a comprehensive overview of the methodologies offered by various approaches using laser scanning data from the perspective of power line extraction applications, as well as to discuss the benefits and drawbacks of each approach. The comparison revealed that, despite the tremendous potential of aerial and mobile laser scanning systems, human intervention and post-processing actions are still required to achieve the desired results. In addition, the majority of the methods have been evaluated on the small datasets, and very few methods have been focused on multi-conductor extraction and reconstruction for power lines modelling. These barriers hinder the automated extraction and reconstruction of power line using LiDAR data and point to unexplored areas for further research and serve as useful guidelines for future research directions. Several promising directions for future LiDAR experiments using deep learning methods are outlined in the hope that they will pave the way for applications of PLC modelling and assessment at a finer scale and on a larger scale.

Highways to invasion: Powerline servitudes as corridors for alien plant invasions

AbstractHuman use of powerline servitude corridors for the secure transmission and distribution of electricity disturbs natural landscapes and changes vegetation patterns. We examined vegetation communities under two powerlines in the Limpopo province, South Africa (Khakhu line 22KV and Fundudzi 132KV powerlines). Twelve invasive alien species were identified in Khakhu and Fundudzi, dominated by Khaki bush Tagetes minuta, common guava Psidium guajava, black‐jack Bidens pilosa, fierce thorn apple Datura ferox and common lantana Lantana camara. Sites closest to human settlements showed high alien species richness, whilst sites located further away were dominated by alien L. camara, but had a low overall species richness. Conversely, areas away from human settlements showed lower levels of alien invasion. Weak overlaps in the principal coordinates analysis polygons for the two powerline servitudes suggest that alien plant compositions are dissimilar, with five identified species groups being identifiable among sites, and thus a high potential for invasion success from multiple taxa. The research findings showed that powerline corridors could potentially trigger invasion success via disturbance as they act as pathways for arrival and should be a focus for management efforts to prevent further spread.

EXTRACTION OF POINT CLOUD-BASED INFORMATION FOR POWERLINE CORRIDORS

Abstract. It has been a challenge for electric power management to automatically extract power lines from LiDAR point clouds. However, environmental and technical issues have made management more challenging in complicated areas where power lines are in close proximity to buildings and/or trees. In this study, the structure and types of the data captured by a LiDAR sensor in regions containing line corridors were analysed. The crucial stage is appropriately identifying from the data the essential parts of a power line corridor route. The point cloud dataset used in the study belongs to the Borssele region in Zeeland, the Netherlands. By manually labelling the dataset, three classes were identified: wire, pylon, and others. For the classification of point clouds, the Random Forest method was utilised. To assess the obstacles posed by the class wire, 5 m, 10 m, and 15 m 3D buffer zones are created. The visual presentation of obstacles within the buffer zone is achieved by assigning them a separate class code and indicating that they are inside and partially within. Based on the results, the correctness values of the classes of wire and others are considered to be satisfactory. However, the class pylon contains points with incorrect labels after the classification. As a result, the accuracy of the pylon class is much lower than the accuracy of the other two classes.

Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid

LiDAR is currently one of the most utilized sensors to effectively monitor the status of power lines and facilitate the inspection of remote power distribution networks and related infrastructures. To ensure the safe operation of the smart grid, various remote data acquisition strategies, such as Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TSL) have been leveraged to allow continuous monitoring of regional power networks, which are typically surrounded by dense vegetation. In this article, an unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage, as well as the surrounding vegetation in a Power Line Corridor (PLC) solely from LiDAR data. Initially, the proposed approach eliminates the ground points from higher elevation points based on statistical analysis that applies density criteria and histogram thresholding. After denoising and transforming of the remaining candidate points by applying Principle Component Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a two-stage DBSCAN clustering to identify each power line individually. Finally, all high elevation points in the PLC are identified based on their distance to the newly segmented power lines. Conducted experiments illustrate that the proposed framework is an agnostic method that can efficiently detect the power lines and perform PLC-based hazard analysis.

Power Line Extraction and Obstacle Inspection of Unmanned Aerial Vehicle Oblique Images Constrained by the Vertical Plane

AbstractUnmanned aerial vehicle (UAV) inspection of vegetation or other obstacles in power transmission line corridors is an economical and efficient method. Spatial power lines cannot be directly restored in the single airborne photographic geometry, and automatic robust extraction of power lines under complex background is a bottleneck problem in the current transmission line reconstruction. Thus, a robust restoration method of the power line spatial position based on the vertical plane constraint was proposed that can restore the entire power line three‐dimensional (3D) points. First, the spatial position of the hanging points at both ends of the power line was solved based on an insulator plumb, and the vertical plane of the power line was determined. A light beam passing through the photographing centre and the image point could then be determined for any image point on the image power line. The intersection point of the light beam and the vertical plane of the power line was the point of the spatial power line. The proposed method was used to select a few (three or more) power line seed points manually to calculate the corresponding 3D space points and then fit these points to obtain the dense and uniform 3D points of power line. After restoring the spatial power line, the minimum distance error line segment based on splitting was used to approximate the 3D point sequence of the power line, and it was divided into several straight‐line segments. The spatial buffer zone was established with each line segment as the centre and a safety distance as the radius to detect possible obstacles in the power line corridor. Finally, a set of simulated and actual data were used for verification and analysis. Results show that the method can effectively detect obstacles around the power line, and the accuracy is within 0.25 m.

Effects of an early mass-flowering crop on wild bee communities and traits in power line corridors vary with blooming plants and landscape context

Effects of an early mass-flowering crop on wild bee communities and traits in power line corridors vary with blooming plants and landscape context

A Hybrid Framework Combining Data-Driven and Catenary-Based Methods for Wide-Area Powerline Sag Estimation

This paper is concerned with the airborne-laser-data-based sag estimation for wide-area transmission lines. A systematic data processing framework is established for multi-source data collected from power lines, which is applicable to various operating conditions. Subsequently, a k-means-based clustering approach is employed to handle the spatial heterogeneity and sparsity of powerline corridor data after comprehensive performance comparisons. Furthermore, a hybrid model of the catenary and XGBoost (HMCX) method is proposed for sag estimation, which improves the accuracy of sag estimation by integrating the adaptability of catenary and the sparsity awareness of XGBoost. Finally, the effectiveness of HMCX is verified by using power data from 116 actual lines.

Classification of Transmission Line Corridor Tree Species Based on Drone Data and Machine Learning

Tree growth in power line corridors poses a threat to power lines and requires regular inspections. In order to achieve sustainable and intelligent management of transmission line corridor forests, a transmission line corridor tree barrier management system is needed, and tree species classification is an important part of this. In order to accurately identify tree species in transmission line corridors, this study combines airborne LiDAR (light detection and ranging) point-cloud data and synchronously acquired high-resolution aerial image data to classify tree species. First, individual-tree segmentation and feature extraction are performed. Then, the random forest (RF) algorithm is used to sort and filter the feature importance. Finally, two non-parametric classification algorithms, RF and support vector machine (SVM), are selected, and 12 classification schemes are designed to perform tree species classification and accuracy evaluation research. The results show that after using RF for feature filtering, the classification results are better than those without feature filtering, and the overall accuracy can be improved by 3.655% on average. The highest classification accuracy is achieved when using SVM after combining a digital orthorectification map (DOM) and LiDAR for feature filtering, with an overall accuracy of 85.16% and a kappa coefficient of 0.79.

Powerline Corridors Can Add Ecological Value to Suburban Landscapes When Not Maintained as Lawn

Electric powerline corridors are informal green spaces that encompass large areas of land and have the potential to support biodiversity in urban and suburban landscapes. However, the extent to which these corridors provide novel habitats compared to the surrounding landscape is unclear. Biodiversity in corridors is often compared to that of “natural” habitats despite the fact that the corridors are subject to frequent vegetation management. In urban and suburban landscapes, residential yards may provide a more appropriate comparison because they are a dominant type of green space and are also characterized by frequent vegetation management. We conducted a study of the biodiversity in suburban powerline corridors in northern Illinois, USA, and compared it to the biodiversity found in nearby residential yards. Our goal was to determine whether powerline corridors added ecological value to these suburban landscapes. We included three different management styles of powerline corridors: (1) frequently mowed and kept as lawn, (2) brush mowed on a five-year cycle (“old-field”), and (3) restored and/or maintained as native prairie. We measured the species richness and composition of plants, birds, and insect pollinators in corridors and yards. The corridor management types and comparison yards differed significantly in the richness of all three taxa, with old-field and/or prairie sites having greater species richness than mown corridors and/or comparison yards. Community composition also differed by management category. While the species richness of old-field sites tended to be high, prairie sites generally had more species of conservation interest. Our study shows that both old-field- and prairie-managed powerline corridors add habitat value to Midwestern U.S. suburban landscapes by providing alternative habitat types that support many species. Nonetheless, we suggest that managers looking to specifically support native and/or grassland specialist species in this region should manage sites as prairies when possible.

Early detection of tree encroachment in high voltage powerline corridor using growth model and UAV-borne LiDAR

UAV-borne LiDAR is an innovative and effective technique for tree encroachment detection in high voltage powerline corridor. However, the periodical inspection of the whole powerline corridor is inefficient, as the powerline segments occurring the tree encroachment only account for a very small part. In this paper, taking one segment of the powerline corridor in Taining County, Fujian province, China as the test site, we acquired the point cloud data using a UAV-borne LiDAR, and then combined the tree growth model and two-phase encroachment detection algorithm to realize efficiently early detection of tree encroachment. First, the points of powerlines and trees were classified from the point cloud, and then the individual tree heights and the belonging tree species were extracted. Based on tree plot data, the relationships between tree heights and tree ages were established using Richards growth model. Secondly, the individual tree heights were predicted at given time points, and the tree encroachments were detected in advance according to the required safe distance between powerlines and trees. To tackle the huge amount of point cloud data and calculation, the two-phase tree encroachment detection algorithm based on bounding boxes was applied to replace the traditional point traversal algorithm. As a result, the exact locations of tree encroachment were early detected and the specific encroaching trees were also pre-identified. Lastly, the pre-detected tree encroachment should be verified through field survey, and treated accordingly. Thus, the inspection efficiency would be greatly improved. In accuracy assessment, the coefficient of determination (R2) and the root mean square error (RMSE) of fitted growth model for Masson pine were 0.812 and 2.308 m, and 0.861 and 2.556 m for Eucalyptus, respectively. Compared with point traversal algorithm, the calculation efficiency of the two-phase tree encroachment detection algorithm was improved by nearly 76 times on average.