The drone was mounted with a LiDAR sensor and multispectral camera to capture true-color aerial imagery, LiDAR (-500 points m-2) and NDVI (green, red, red edge and nearinfrared bands) on the same flight. McCord Engineering had already been flying the drone to collect engineering line design specific data. In collaboration with McCord Engineering, an engineering consultancy firm that handles the design for all of the cooperative’s work plan jobs, Mid-South Synergy embarked on a pilot project to test the effectiveness of using a drone to capture data for vegetation management (among other things). Very low NDVI values (<0.1) correspond to barren areas of rock, sand or snow moderate values (0.2 to 0.3) represent shrub and grassland and high values (0.6 to 0.8) indicate temperate and tropical rainforests. NDVI values range from -1.0 to 1.0 with negative NDVI values being generated from clouds, water and snow values near zero are mainly generated from rock and bare soil. The index outputs an image showing greenness or relative biomass. NDVI is a standardized index that takes advantage of the contrasting characteristics of two bands from a multispectral raster data set: the chlorophyll pigment absorptions in the red band and the high reflectivity of plant materials in the near-infrared band. This data can be used to determine vegetation health using the Normalized Difference Vegetation Index (NDVI). Attaching a multispectral camera to the drone allows for capturing data in the visible as well as near-infrared and infrared bands of the electromagnetic spectrum. If equipped with light detection and ranging (LiDAR), vegetation height antd the distance of vegetation from the distribution conductors under all operating temperatures and conditions can be determined from the acquired point cloud data. These four missing pieces of the puzzle could help to take the guesswork out of any vegetation assessment, which the cooperative thought was currently subjective, inconsistent, costly and inefficient.Īn unmanned aerial vehicle (UAV ) or drone flying at a low altitude of 200 ft (61 m) can capture high-resolution images of overhead distribution lines if mounted with the right camera. Trees that are overhangs and could fall on power lines.Trees within and outside of the right-of-way that are dead and need to be cleared as soon as possible.Trees growing outside the right-of-way that could contact the distribution lines if they fell, causing an outage and resulting in mechanical damage or triggering a fire.Trees growing within the right-of-way that pose clearance.Without sending a truck roll, Mid-South Synergy wanted the ability to identify the following issues: Vegetation health – and whether a tree is dead or alive – is another crucial piece of information the coop believes will go a long way in its vegetation management efforts. The model also does not have the ability to determine the distance between hazard trees and distribution lines, or to help to determine vegetation height. If known, this ability would go a long way to reducing the number of truck rolls, because crews would be dispatched only to specific areas where hazard trees are present. The model lacks the ability to pinpoint the actual location of a dead tree or hazard tree. In as much as the coop’s GIS model gives general possible locations of dead trees, the method is not foolproof. This also resulted in a significant drop in the number of dead-tree-related customer calls, as the trees were being cut down before the customers had time to call in and alert the coop.Īlthough the GIS-derived results went a long way to improving the hazard tree control effort at Mid-South Synergy, the coop thought there was still room for improvement. Using this two-pronged approach of GIS and previously established cycles, the coop cut down more than 60,000 dead trees close to distribution lines over a three-year period, resulting in a significant decrease in vegetation-related outages to the coop’s pre-2012 levels. This was in addition to the coop’s ongoing cyclical trimming exercise, which was done concurrently. These maps became the source of work packets dispatched to right-of-way (ROW) crews for hazard tree control. The coop managed to create maps showing areas with the highest risk of dead trees. Historic outages were also part of the data set used in the analysis. The coop responded by using geographic information system (GIS) modeling (see T&D World, March 2013) using freely available GIS data on soils, vegetation and rainfall. A few years ago, following a major drought that swept across the state of Texas, dead trees resulted in thousands of power outages in Mid-South Synergy Electric Cooperative’s service area.
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