Identifying Utility Corridor Encroachment with Global Mapper
Vegetation management is an integral part of keeping utility corridors running efficiently, especially for electricity providers. Without the control of vegetation encroachment in the rights-of-way (ROW) of electrical corridors, power outages can occur. Power disruption is costly to electricity providers and risks the health and safety of customers. Global Mapper provides an easy-to-use solution to identify possible vegetation encroachment before plant life makes contact with utilities. Using lidar or other point cloud data, it is possible to identify potential hazards before going into the field for a ground inspection. Before exploring the specific tools in Global Mapper, it is important to understand why vegetation management is necessary for smooth-running utilities.
ROW refers to the area surrounding the utility lines that the electric cooperative must maintain for the integrity of the powerlines. Regarding vegetation, utility providers have a responsibility to suppress the vegetation within and adjacent to the ROW corridor to avoid any contact with vegetation and electric lines that could result in power outages. Providers must inspect the corridors annually and trim or treat vegetation to maintain the safety of the ROW and avoid any encroachment-causing outages.
Corridor inspections can be performed either on the ground or by flying above the corridor to inspect the ROW for any vegetation encroachments. Ground inspections can be time-consuming; however, they are thorough. While aerial inspections can cover the same area in a fraction of the time, they can be less accurate due to the altitude of the inspector (FERC, 2004). With the use of lidar, aerial inspections can be more accurate and still save time.
Global Mapper can be used to automatically identify, classify, and extract utility features from lidar data as well as to identify possible encroachments and display them in a 3D model. Using the Select Lidar by Distance tool, users can locate potentially hazardous vegetation located within the utility ROW. Identified hazards can be displayed in a 2D or 3D model to show areas that will need to be maintained. The extracted data can also be viewed in Global Mapper Mobile, a simplified version of the desktop application for iOS and Android devices, providing ease of access for maintenance crews or other “boots on the ground” employees.
The first step to identify possible vegetation hazards in the ROW is to classify the lidar points (if they haven’t been already) that represent vegetation. This is performed using Global Mapper’s auto-classification tools.
An analysis of the geometric characteristics of the point cloud-first identifies points representing ground and subsequently above-ground points that are likely to be high vegetation or trees. After classification, the power lines can be extracted as 3D line features using the lidar Extract Vector Features tool
After the power lines have been extracted, the potential ROW encroachments are highlighted using the Select Lidar by Distance tool. As a result the vegetation points that fall within a certain distance of the extracted powerline are selected. In the example below, we chose a Maximum Search Distance of 30 feet to search on either side of the power lines.
Next, the type of lidar to search for is selected under Specify Lidar Classification(s) to Select from… In this scenario, 5 – High Vegetation is chosen because we are primarily concerned with encroachment from trees. We also selected Search Near Loaded 3D Line Feature(s)… as opposed to searching for vegetation points within the specified distance of other lidar points.
As an initial step in encroachment analysis, the point cloud is classified to differentiate between ground, water, buildings, vegetation, and powerlines.
By selecting the extracted powerlines, the search width of 30 feet for high vegetation will only be within that distance of the powerlines. The Use 3D Distance box was also checked to search the point cloud using a 3-dimensional distance measurement instead of points at any height within the horizontal distance from the selected powerlines.
From the classified point cloud tree features can be extracted and shown in 3D as tree models.
After running the search, the trees within 30 feet are highlighted on the map and can also be shown in the 3D Viewer. To create individual point features representing each tree, choose the Extract Vector Features tool and apply the necessary Tree Extraction Settings. In this dialog box, the Only Extract from Lidar Points Selected in Digitizer options is checked so that only those tree points identified in the search by distance are extracted.
With the tree points still selected, we right-click on the map and choose EDIT – Edit Selected Features to bring up the Modify Selected Point Features dialog box. Here a 3D model can be assigned to the extracted tree features under Feature Style. After assigning a 3D model to the point features, the model is displayed in the 3D Viewer more clearly, representing what the possible hazards may look like.
Using the Select Lidar by Distance tool, classified tree points within a specific distance of powerline returns are selected to determine areas of vegetation encroachment.
The extracted point and line features can be transferred to Global Mapper Mobile under File and Export. These points can give field staff a better perspective of the inspection process if they are deployed to the field, and they can be displayed over a street map for ease of viewing.
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FERC. (2004). Utility Vegetation Management and Bulk Electric Reliability Report From The Federal Energy Regulatory Commission. https://www.ferc.gov/sites/default/files/2020-05/veg-mgmt-rpt-final_0.pdf
Transmission Vegetation Management, FAC-003-4. (2016). https://www.ferc.gov/sites/default/files/2020-04/fac-003-4.pdf
U.S.-Canada Power System Outage Task Force. (2004). Final Report on the Blackout in the United States and Canada: Causes and Recommendations. https://www3.epa.gov/region1/npdes/merrimackstation/pdfs/ar/AR-1165.pdf