Remote Sensing methods for power line corridor surveys

AbstractTo secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

Automatic vision based fault detection on electricity transmission components using very highresolution

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesElectricity is indispensable to modern-day governments and citizenry’s day-to-day operations. Fault identification is one of the most significant bottlenecks faced by Electricity transmission and distribution utilities in developing countries to deliver credible services to customers and ensure proper asset audit and management for network optimization and load forecasting. This is due to data scarcity, asset inaccessibility and insecurity, ground-surveys complexity, untimeliness, and general human cost. In this context, we exploit the use of oblique drone imagery with a high spatial resolution to monitor four major Electric power transmission network (EPTN) components condition through a fine-tuned deep learning approach, i.e., Convolutional Neural Networks (CNNs). This study explored the capability of the Single Shot Multibox Detector (SSD), a onestage object detection model on the electric transmission power line imagery to localize, classify and inspect faults present. The components fault considered include the broken insulator plate, missing insulator plate, missing knob, and rusty clamp. The adopted network used a CNN based on a multiscale layer feature pyramid network (FPN) using aerial image patches and ground truth to localise and detect faults via a one-phase procedure. The SSD Rest50 architecture variation performed the best with a mean Average Precision of 89.61%. All the developed SSD based models achieve a high precision rate and low recall rate in detecting the faulty components, thus achieving acceptable balance levels F1-score and representation. Finally, comparable to other works of literature within this same domain, deep-learning will boost timeliness of EPTN inspection and their component fault mapping in the long - run if these deep learning architectures are widely understood, adequate training samples exist to represent multiple fault characteristics; and the effects of augmenting available datasets, balancing intra-class heterogeneity, and small-scale datasets are clearly understood

Utilizing Skylab data in on-going resources management programs in the state of Ohio

The author has identified the following significant results. The use of Skylab imagery for total area woodland surveys was found to be more accurate and cheaper than conventional surveys using aerial photo-plot techniques. Machine-aided (primarily density slicing) analyses of Skylab 190A and 190B color and infrared color photography demonstrated the feasibility of using such data for differentiating major timber classes including pines, hardwoods, mixed, cut, and brushland providing such analyses are made at scales of 1:24,000 and larger. Manual and machine-assisted image analysis indicated that spectral and spatial capabilities of Skylab EREP photography are adequate to distinguish most parameters of current, coal surface mining concern associated with: (1) active mining, (2) orphan lands, (3) reclaimed lands, and (4) active reclamation. Excellent results were achieved when comparing Skylab and aerial photographic interpretations of detailed surface mining features. Skylab photographs when combined with other data bases (e.g., census, agricultural land productivity, and transportation networks), provide a comprehensive, meaningful, and integrated view of major elements involved in the urbanization/encroachment process

Vegetation Detection and Classification for Power Line Monitoring

Electrical network maintenance inspections must be regularly executed, to provide a continuous distribution of electricity. In forested countries, the electrical network is mostly located within the forest. For this reason, during these inspections, it is also necessary to assure that vegetation growing close to the power line does not potentially endanger it, provoking forest fires or power outages. Several remote sensing techniques have been studied in the last years to replace the labor-intensive and costly traditional approaches, be it field based or airborne surveillance. Besides the previously mentioned disadvantages, these approaches are also prone to error, since they are dependent of a human operator’s interpretation. In recent years, Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under debate, due to its flexibility and potential for customisation, as well as the fact it can fly close to the power lines. The present study proposes a vegetation management and power line monitoring method, using a UAV platform. This method starts with the collection of point cloud data in a forest environment composed of power line structures and vegetation growing close to it. Following this process, multiple steps are taken, including: detection of objects in the working environment; classification of said objects into their respective class labels using a feature-based classifier, either vegetation or power line structures; optimisation of the classification results using point cloud filtering or segmentation algorithms. The method is tested using both synthetic and real data of forested areas containing power line structures. The Overall Accuracy of the classification process is about 87% and 97-99% for synthetic and real data, respectively. After the optimisation process, these values were refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison and discussion of results is presented, providing the most important evaluation metrics and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso, durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede elétrica não a coloca em risco, provocando incêndios ou falhas elétricas. Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização, assim como o facto de conseguirem voar mais próximas das linhas elétricas. O presente estudo propõe um método para a gestão da vegetação e monitorização da rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo: deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas de classe através de um classificador baseado em features, vegetação ou estruturas da rede elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87% e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização, estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas de avaliação mais importantes e uma representação visual dos resultados obtidos

Understanding Structure and Function in Semiarid Ecosystems: Implications for Terrestrial Carbon Dynamics in Drylands

This study advances understanding of how the changes in ecosystem structure and function associated with woody shrub encroachment in semi-arid grasslands alter ecosystem carbon (C) dynamics. In terms of both magnitude and dynamism, dryland ecosystems represent a major component of the global C cycle. Woody shrub encroachment is a widespread phenomenon globally, which is known to substantially alter ecosystem structure and function, with resultant impacts on C dynamics. A series of focal sites were studied at the Sevilleta National Wildlife Refuge in central New Mexico, USA. A space-for-time analogue was used to identify how landscape structure and function change at four stages over a grassland to shrubland transition. The research had three key threads: 1. Soil-associated carbon: Stocks of organic and inorganic C in the near-surface soil, and the redistribution of these C stocks by erosion during high-intensity rainfall events were quantified using hillslope-scale monitoring plots. Coarse (>2 mm) clasts were found to account for a substantial proportion of the organic and inorganic C in these calcareous soils, and the erosional effluxes of both inorganic and organic C increased substantially across the vegetation ecotone. Eroded sediment was found to be significantly enriched in organic C relative to the contributing soil with systematic changes in OC enrichment across the vegetation transition. The OC enrichment dynamics observed were inconsistent with existing understanding (derived largely from reductionist, laboratory-based experiments) that OC enrichment is largely insignificant in the erosional redistribution of C. 2. Plant biomass: Cutting-edge proximal remote sensing approaches, using a remotely piloted lightweight multirotor drone combined with structure-from-motion (SfM) photogrammetry were developed and used to quantify biomass carbon stocks at the focal field sites. In such spatially heterogeneous and temporally dynamic ecosystems existing measurement techniques (e.g. on-the-ground observations or satellite- or aircraft-based remote sensing) struggle to capture the complexity of fine-grained vegetation structure, which is crucial for accurately estimating biomass. The data products available from the novel SfM approach developed for this research quantified plants just 15 mm high, achieving a fidelity nearly two orders of magnitude finer than previous implementations of the method. The approach developed here will revolutionise the study of biomass dynamics in short-sward ecogeomorphic systems. 3. Ecohydrological modelling: Understanding the effects of water-mediated degradation processes on ecosystem carbon dynamics over greater than observable spatio-temporal scales is complicated by significant scale-dependencies and thus requires detailed mechanistic understanding. A process-based, spatially-explicit ecohydrological modelling approach (MAHLERAN - Model for Assessing Hillslope to Landscape Erosion, Runoff and Nutrients) was therefore comprehensively evaluated against a large assemblage of rainfall runoff events. This evaluation highlighted both areas of strength in the current model structure, and also areas of weakness for further development. The research has improved understanding of ecosystem degradation processes in semi-arid rangelands, and demonstrates that woody shrub encroachment may lead to a long-term reduction in ecosystem C storage, which is contrary to the widely promulgated view that woody shrub encroachment increases C storage in terrestrial ecosystems.NERC Doctoral Training Grant (NE/K500902/1)NSF Long Term Ecological Research Program at the Sevilleta National Wildlife Refuge (DEB-1232294

Power line mapping technique using all-terrain mobile laser scanning

Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3% and 93.6%, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70% of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network.</p