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Vegetation Management Supported with Lidar

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While vegetation is an essential part of our ecosystem, it’s also the single largest cause of electric power outages in the US, writes Gregor Wilke of Trimble. According to the solutions engineer, advances in digital technology connect data and workflows for streamlined vegetation management operations and maintenance.

From an article in Smart Energy International.

The US electricity network consists of over 7,300 power plants, nearly 200,000 miles of high-voltage transmission lines, and millions of miles of low-voltage distribution lines and distribution transformers. It’s essential for utilities to manage the vegetation growing around these assets to decrease the risk of wildfires and power outages and ensure safety for residents and communities. At the same time, vegetation management is a top operational expenditure and entails substantial risk.

Despite the growing utility network, many electric utilities still rely on conventional vegetation management methods such as scoping for hazard trees with helicopter or foot patrols or using antiquated programmes and paper processes that create data silos and communication gaps. These methods rely on labour-intensive field work, which introduces human error. Improperly measured trees or vegetation missed by the human eye, especially on steep slopes, can pose a hazard to utility lines. It’s also difficult for workers to find and return to a hazard tree spotted by air or foot because these methods don’t capture a precise location.


Utility companies can generate a digital model of their networks by converting classified LiDAR point clouds into GIS information. A LiDAR scan creates a more accurate GIS inventory of assets such as poles, structures, spans, and bays by capturing the precise X, Y and Z coordinates. With point-cloud data from a variety of aerial or ground-based sources, an electric utility can use its existing minimum voltage clearance distance (MVCD) standards or those from NERC to create a canopy-coloured risk map of all vegetation along the network.
Tree and brush canopy data is also derived from LiDAR and determined by radial distance (grow-in risk) and a tree’s likelihood to fall and come in contact with conductors (fall-in risk). This insight into which trees or brush need immediate attention can reduce operating expenses and manual labour costs by removing the need for costly helicopter line patrols or boots-on-the-ground assessments.

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