©2023 International Society of Arboriculture Arboriculture & Urban Forestry 49(3): May 2023 153 Brunker SW. 2016. Aerial LIDAR for overhead transmission lines.Lessons .rom t.e ... in re.lisin. .ene.t .nd ..lue. In: 2016 IEEE International Conference on Power System Technology. POWERCON 2016; 2016 September 28–October 1; Wollongong, Australia. IEEE. p. 1-6. https://doi.org/10 .1109/POWERCON.2016.7753929 Carson WW, Andersen HE, Reutebuch SE, Mcgaughey RJ. 2004. LIDAR applications in forestry—An overview. In: ASPRS Annual Conference Proceedings. ASPRS 2004 Annual Con- ference “Mountains of Data—Peak Decisions”; 2004 May 23–28; Denver, Colorado, USA. Bethesda (MD, USA): American Society of Photogrammetry and Remote Sensing. Chen C, Yang B, Song S, Peng X, Huang R. 2018. Automatic clearance anomaly detection for transmission line corridors utilizing UAV-borne LIDAR data. Remote Sensing. 10(4):613. https://doi.org/10.3390/rs10040613 Ferguson N, Ryder S, Richardson P. 2012. Mitigating the environ- mental impact of compliance with NERC FAC-003-1: A working methodology for intelligent vegetation hazard detec- tion using aerial LIDAR technology. In: Evans JM, Goodrich- Mahoney JW, Mutrie D, Reinemann J, editors. Environmental Concerns in Rights-of-Way Management: The 9th International Symposium. 2009 September 27–30; Portland, Oregon, USA. Champaign (IL, USA): International Society of Arboriculture. p. 41-46. https://www.rights-of-way.org/past-proceedings Frank M, Pan Z, Raber B, Lenart C. 2010. Vegetation manage- ment of utility corridors using high-resolution hyperspectral imaging and LIDAR. In: 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. 2010 June 14–16; Reykjavik, Iceland. IEEE. https://doi.org/ 10.1109/WHISPERS.2010.5594887 Goodfellow JW. 2020. Best management practices: Utility tree risk assessment. Champaign (IL, USA): International Society of Arboriculture. 95 p. Guggenmoos S. 2003. Effects of tree mortality on power line security. Journal of Arboriculture. 29(4):181-192. https://doi .org/10.48044/jauf.2003.022 Guggenmoos S. 2007. Increased risk of electrical service inter- ruption associated with tree branches overhanging conductors. Utility Arborist Association Quarterly. 15:8-14. Guggenmoos S. 2010. Storm hardening the electric system against tree-caused service interruptions. T&D World. 2010 November 18. https://www.tdworld.com/vegetation-management/reliability -safety/article/20961732/storm-hardening-the-electric-system -against-treecaused-service-interruptions Guggenmoos S. 2011. Tree-related outages due to wind loading. Arboriculture & Urban Forestry. 37(4):147-151. https://doi .org/10.48044/jauf.2011.019 Guikema S, Davidson R, Liu H. 2006. Statistical models of the effects of tree trimming on power system outages. IEEE Transactions on Power Delivery. 21(3):1549-1557. https:// doi.org/10.1109/TPWRD.2005.860238 Hamraz H, Contreras MA, Zhang J. 2016. A robust approach for tree segmentation in deciduous forests using small-footprint airborne LIDAR data. International Journal of Applied Earth Observation and Geoinformation. 52:532-541. https://doi.org/ 10.1016/j.jag.2016.07.006 particularly promising for distribution vegetation management, as they may provide a faster, more accurate, and ultimately more cost-effective method for inspection compared to traditional UVM, where inspection is done visually by foot patrol, potentially over rugged terrain (Chen et al. 2018). Due to this r..id .nd .ccur.te identi.c.tion o. .e.et.tion t.re.ts. individual trees and problematic areas may be tar- geted for more prescriptive management (Miller et al. 2015). While Walker (2020) provided a conceptual basis for “fall in” detection and “fall in” detection risk management along electrical distribution corridors, there is a need for more research on the detection and risk management of “fall in” risks along electrical distribution-level ROWs. Other frontiers in the litera- ture include the development of spatially informed outage prediction models such as those presented in Hartling et al. (2021) and Jain et al. (2021). UAS have been combined with LIDAR to develop platforms for forest inventories (Jaakkola et al. 2010; Wallace et al. 2012; Wallace et al. 2014; Hamraz et al. 2016; Wallace et al. 2016) and detection of vegetation encroachment in the electrical transmission corridor environment (Chen et al. 2018). Given these separate but related research directions, the combined use of UAS and LIDAR for electrical distribution UVM seems to be a logical extension of the application of these new technologies. LITERATURE CITED Ahmad J, Malik A, Xia L. 2011. Effective techniques for vegeta- tion monitoring of transmission lines right-of-ways. In: 2011 IEEE International Conference on Imaging Systems and Techniques. IST 2011; 2011 May 17–18; Batu Ferringhi, Malaysia. IEEE. p. 34-38. https://doi.org/10.1109/IST.2011 .5962216 Ahmad J, Malik A, Xia L, Ashikin N. 2013. Vegetation encroach- ment monitoring for transmission lines right-of-ways: A survey. Electric Power Systems Research. 95:339-352. https://doi.org/ 10.1016/j.epsr.2012.07.015 ASCE. 2017. 2017 Infrastructure report card: A comprehensive assessment of America’s infrastructure. Reston (VA, USA): American Society of Civil Engineers. 112 p. https://www .infrastructurereportcard.org/wp-content/uploads/2016/10/ 2017-Infrastructure-report-card.pdf Brandtberg T, Warner TA, Landenberger RE, McGraw JB. 2003. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density LIDAR data from the eastern deciduous forest in North America. Remote Sensing of Environment. 85(3):290-303. https://doi.org/10.1016/ S0034-4257(03)00008-7
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