{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T08:42:50Z","timestamp":1761986570741,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2022R1F1A1072491"],"award-info":[{"award-number":["2022R1F1A1072491"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The incidence of wildfires caused by tree contact with high-voltage power lines has become an increasingly pressing issue in the United States. To prevent such incidents, local safety councils have established minimum clearance regulations between trees and power lines. While most studies have focused on the tree encroachment around power lines during normal weather conditions, recent catastrophic fires have been caused by strong winds. To address this gap in knowledge, we investigated the critical wind speed that heightens the risk of wildfires by calculating the distance between trees and wires. To conduct this study, we used airborne LiDAR data collected from Sonoma County in northern California and analyzed the behavior of a sample tree having a height of 19.2 m under wind loads. Our analysis showed that the main factor determining tree deflection is the ratio of the tree height to the trunk diameter. We also found that, although the probability of fire ignition is typically low under normal conditions, it is likely to increase at a wind speed of approximately 40.3 m\/s. In conclusion, this research demonstrates the utility of point cloud data in identifying potentially dangerous trees and reducing the risk of fires.<\/jats:p>","DOI":"10.3390\/rs15061485","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T01:58:22Z","timestamp":1678240702000},"page":"1485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Measuring the Distance between Trees and Power Lines under Wind Loads to Assess the Heightened Potential Risk of Wildfire"],"prefix":"10.3390","volume":"15","author":[{"given":"Seulbi","family":"Lee","sequence":"first","affiliation":[{"name":"Division of Architecture & Urban Design, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7157-4878","authenticated-orcid":false,"given":"Youngjib","family":"Ham","sequence":"additional","affiliation":[{"name":"Department of Construction Science, Texas A&M University, 3337 TAMU, College Station, TX 77843, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102059","DOI":"10.1016\/j.apgeog.2019.102059","article-title":"Wildfire exposure to the wildland urban interface in the western US","volume":"111","author":"Ager","year":"2019","journal-title":"Appl. Geogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.firesaf.2017.04.014","article-title":"Mapping areas at elevated risk of large-scale structure loss using Monte Carlo simulation and wildland fire modeling","volume":"91","author":"Lautenberger","year":"2017","journal-title":"Fire Saf. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1049\/gtd2.12463","article-title":"Data-driven spatio-temporal analysis of wildfire risk to power systems operation","volume":"16","author":"Umunnakwe","year":"2022","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_4","unstructured":"(2023, February 21). McKinney Fire Incident, Available online: https:\/\/www.fire.ca.gov\/incidents\/2022\/7\/29\/mckinney-fire\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.firesaf.2017.04.040","article-title":"Wildland fire spot ignition by sparks and firebrands","volume":"91","year":"2017","journal-title":"Fire Saf. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sandoval, C.J. (2023, January 12). Fight utility wildfire with knowledge management. Proceedings of the Duke Environmental Law & Policy Forum, Durham, NC, USA.","DOI":"10.2139\/ssrn.4322965"},{"key":"ref_7","first-page":"1249","article-title":"Power lines: Climate change and the politics of undergrounding","volume":"71","author":"Brundy","year":"2019","journal-title":"Hastings Law J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106928","DOI":"10.1016\/j.epsr.2020.106928","article-title":"Fire hazard mitigation in distribution systems through high impedance fault detection","volume":"192","author":"Gashteroodkhani","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e4070","DOI":"10.1002\/ecs2.4070","article-title":"Cascadia Burning: The historic, but not historically unprecedented, 2020 wildfires in the Pacific Northwest, USA","volume":"13","author":"Reilly","year":"2022","journal-title":"Ecosphere"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120762","DOI":"10.1016\/j.techfore.2021.120762","article-title":"Fusion of CNN and sparse representation for threat estimation near power lines and poles infrastructure using aerial stereo imagery","volume":"168","author":"Qayyum","year":"2021","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TPWRD.2021.3059307","article-title":"Automated power lines vegetation monitoring using high-resolution satellite imagery","volume":"37","author":"Gazzea","year":"2021","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s00468-022-02305-0","article-title":"Detecting tree and wire entanglements with deep learning","volume":"37","author":"Oliveira","year":"2022","journal-title":"Trees"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","article-title":"Remote sensing methods for power line corridor surveys","volume":"119","author":"Matikainen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ijepes.2017.12.016","article-title":"Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning","volume":"99","author":"Jenssen","year":"2018","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101501","DOI":"10.1016\/j.aei.2021.101501","article-title":"3D point cloud data processing with machine learning for construction and infrastructure applications: A comprehensive review","volume":"51","author":"Mirzaei","year":"2022","journal-title":"Adv. Eng. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, B., Li, Q., Huang, X., and Wang, C. (2016). An improved method for power-line reconstruction from point cloud data. Remote Sens., 8.","DOI":"10.3390\/rs8010036"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3892","DOI":"10.1109\/JSTARS.2018.2869542","article-title":"Voxel-based extraction of transmission lines from airborne lidar point cloud data","volume":"11","author":"Yang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Q., Liu, L., Li, X., Sangaiah, A.K., and Li, K. (2018). Systematic comparison of power line classification methods from ALS and MLS point cloud data. Remote Sens., 10.","DOI":"10.3390\/rs10081222"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106987","DOI":"10.1016\/j.ijepes.2021.106987","article-title":"UAV-lidar aids automatic intelligent powerline inspection","volume":"130","author":"Guan","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.envsoft.2013.09.034","article-title":"A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR","volume":"51","author":"Widlowski","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1080\/01431161.2019.1701726","article-title":"Extraction of urban power lines and potential hazard analysis from mobile laser scanning point clouds","volume":"41","author":"Shi","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s13349-020-00426-z","article-title":"Reduction of wildfire hazard by automated monitoring of vegetation interference with power lines: Point cloud analysis combined with cable mechanics","volume":"10","author":"Takhirov","year":"2020","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_23","first-page":"102740","article-title":"Early detection of tree encroachment in high voltage powerline corridor using growth model and UAV-borne LiDAR","volume":"108","author":"Chen","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107840","DOI":"10.1016\/j.epsr.2022.107840","article-title":"Dynamic modeling of the effects of vegetation management on weather-related power outages","volume":"207","author":"Taylor","year":"2022","journal-title":"Electr. Power Syst. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"04020134","DOI":"10.1061\/(ASCE)ST.1943-541X.0002684","article-title":"Reliability assessment of electrical grids subjected to wind hazards and ice accretion with concurrent wind","volume":"146","author":"Ma","year":"2020","journal-title":"J. Struct. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.epsr.2019.02.007","article-title":"Wind speed severity scale model applied to overhead line reliability simulation","volume":"171","author":"Costa","year":"2019","journal-title":"Electr. Power Syst. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TPWRD.2020.3047101","article-title":"Characterizing probability of wildfire ignition caused by power distribution lines","volume":"36","author":"Muhs","year":"2020","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1111\/rec.12681","article-title":"Restoration thinning enhances growth and diversity in mixed redwood\/Douglas-fir stands in northern California, USA","volume":"26","author":"Dagley","year":"2018","journal-title":"Restor. Ecol."},{"key":"ref_29","unstructured":"(2022, September 27). Sonoma County Veg Map. Available online: https:\/\/sonomavegmap.org\/data-downloads\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"75","DOI":"10.14358\/PERS.78.1.75","article-title":"A new method for segmenting individual trees from the lidar point cloud","volume":"78","author":"Li","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1111\/2041-210X.12575","article-title":"Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data","volume":"7","author":"Dalponte","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1080\/07038992.2016.1196582","article-title":"Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data","volume":"42","author":"Silva","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1175\/JAM2413.1","article-title":"A simple model for simulating tornado damage in forests","volume":"45","author":"Holland","year":"2006","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s00468-013-0948-z","article-title":"A simple tree swaying model for forest motion in windstorm conditions","volume":"28","author":"Pivato","year":"2014","journal-title":"Trees"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20","DOI":"10.48044\/jauf.2008.004","article-title":"Pruning affects tree movement in hurricane force wind","volume":"34","author":"Gilman","year":"2008","journal-title":"Arboric. Urban For."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4059","DOI":"10.5194\/bg-18-4059-2021","article-title":"The motion of trees in the wind: A data synthesis","volume":"18","author":"Jackson","year":"2021","journal-title":"Biogeosciences"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.ufug.2012.05.003","article-title":"Wind tunnel study on aerodynamic characteristics of shrubby specimens of three tree species","volume":"11","author":"Cao","year":"2012","journal-title":"Urban For. Urban Green."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"99783","DOI":"10.1109\/ACCESS.2020.2995389","article-title":"Combining trunk detection with canopy segmentation to delineate single deciduous trees using airborne LiDAR data","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.foreco.2007.05.006","article-title":"Height\u2013diameter equations for boreal tree species in Ontario using a mixed-effects modeling approach","volume":"249","author":"Sharma","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/j.foreco.2008.05.002","article-title":"Wind speed and crown class influence the height\u2013diameter relationship of lodgepole pine: Nonlinear mixed effects modeling","volume":"256","author":"Meng","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_41","unstructured":"Moore, J., Gardiner, B., and Sellier, D. (2018). Plant Biomechanics: From Structure to Function at Multiple Scales, Springer."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113","DOI":"10.14214\/sf.a15666","article-title":"Swaying of trees as caused by wind: Analysis of field measurements","volume":"27","author":"Peltola","year":"1993","journal-title":"Silva Fenn."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kharal, K.H., Kim, C.-H., Park, C., Lee, J.-H., Park, C.-G., Lee, S.H., and Rhee, S.-B. (2018). A Study for the measurement of the minimum clearance distance between the 500 kV DC transmission line and vegetation. Energies, 11.","DOI":"10.3390\/en11102606"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107949","DOI":"10.1016\/j.agrformet.2020.107949","article-title":"A root-to-foliage tree dynamic model for gusty winds during windstorm conditions","volume":"287","author":"Yang","year":"2020","journal-title":"Agric. For. Meteorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1485\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:49:59Z","timestamp":1760122199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1485"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,7]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061485"],"URL":"https:\/\/doi.org\/10.3390\/rs15061485","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,3,7]]}}}