{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:57:26Z","timestamp":1781020646407,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Project","award":["JCYJ20180504165440088"],"award-info":[{"award-number":["JCYJ20180504165440088"]}]},{"name":"Shenzhen Science and Technology Project","award":["GXWD20201231165807007-20200827105738001"],"award-info":[{"award-number":["GXWD20201231165807007-20200827105738001"]}]},{"name":"Shenzhen Science and Technology Project","award":["42001022"],"award-info":[{"award-number":["42001022"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JCYJ20180504165440088"],"award-info":[{"award-number":["JCYJ20180504165440088"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GXWD20201231165807007-20200827105738001"],"award-info":[{"award-number":["GXWD20201231165807007-20200827105738001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001022"],"award-info":[{"award-number":["42001022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With drastic changes to the environment arising from global warming, there has been an increase in both the frequency and intensity of typhoons in recent years. Super typhoons have caused large-scale damage to the natural ecological environment in coastal cities. The accurate assessment and monitoring of urban vegetation damage after typhoons is important, as they contribute to post-disaster recovery and resilience efforts. Hence, this study examined the application of the easy-to-use and cost-effective Unmanned Aerial Vehicle (UAV) oblique photography technology and proposed an improved detection and diagnostic measure for the assessment of street-level damage to urban vegetation caused by the super typhoon Mangkhut in Shenzhen, China. The results showed that: (1) roadside trees and artificially landscaped forests were severely damaged; however, the naturally occurring urban forest was less affected by the typhoon. (2) The vegetation height of roadside trees decreased by 20\u201330 m in most areas, and that of artificially landscaped forests decreased by 5\u201315 m; however, vegetation height in natural forest areas did not change significantly. (3) The real damage to vegetation caused by the typhoon is better reflected by measuring the change in vegetation height. Our study validates the use of UAV remote sensing to accurately measure and assess the damage caused by typhoons to roadside trees and urban forests. These findings will help city planners to design more robust urban landscapes that have greater disaster coping capabilities.<\/jats:p>","DOI":"10.3390\/rs14092093","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T22:20:20Z","timestamp":1651098020000},"page":"2093","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Accurate Measurement and Assessment of Typhoon-Related Damage to Roadside Trees and Urban Forests Using the Unmanned Aerial Vehicle"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0358-2849","authenticated-orcid":false,"given":"Longjun","family":"Qin","sequence":"first","affiliation":[{"name":"Lab of Environmental and Energy Information Engineering, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Mao","sequence":"additional","affiliation":[{"name":"Lab of Environmental and Energy Information Engineering, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenbang","family":"Xu","sequence":"additional","affiliation":[{"name":"Lab of Environmental and Energy Information Engineering, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"He","sequence":"additional","affiliation":[{"name":"Lab of Environmental and Energy Information Engineering, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8142-4280","authenticated-orcid":false,"given":"Chunhua","family":"Yan","sequence":"additional","affiliation":[{"name":"Lab of Environmental and Energy Information Engineering, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Hayat","sequence":"additional","affiliation":[{"name":"Lab of Environmental and Energy Information Engineering, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo-Yu","family":"Qiu","sequence":"additional","affiliation":[{"name":"Lab of Environmental and Energy Information Engineering, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"04014024","DOI":"10.1061\/(ASCE)NH.1527-6996.0000162","article-title":"Power law or lognormal? Distribution of normalized hurricane damages in the United States, 1900\u20132005","volume":"16","author":"Blackwell","year":"2015","journal-title":"Nat. Hazards Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jenvman.2015.11.011","article-title":"A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment","volume":"168","author":"Gallina","year":"2016","journal-title":"J. Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/1365-2745.13039","article-title":"Globally consistent impact of tropical cyclones on the structure of tropical and subtropical forests","volume":"107","author":"Ibanez","year":"2019","journal-title":"J. Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/nature07234","article-title":"The increasing intensity of the strongest tropical cyclones","volume":"455","author":"Elsner","year":"2008","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1038\/nclimate1410","article-title":"Global trends in tropical cyclone risk","volume":"2","author":"Peduzzi","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.tcrr.2020.11.001","article-title":"Assessment of the damages and direct economic loss in Hong Kong due to Super Typhoon Mangkhut in 2018","volume":"9","author":"Choy","year":"2020","journal-title":"Trop. Cyclone Res. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10021-010-9399-1","article-title":"Typhoon disturbance and forest dynamics: Lessons from a northwest Pacific subtropical forest","volume":"14","author":"Lin","year":"2011","journal-title":"Ecosystems"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1023\/A:1026058312853","article-title":"Spatial pattern analysis of pre-and post-hurricane forest canopy structure in North Carolina, USA","volume":"18","author":"Boutet","year":"2003","journal-title":"Landsc. Ecol."},{"key":"ref_9","first-page":"102536","article-title":"The damage of urban vegetation from super typhoon is associated with landscape factors: Evidence from Sentinel-2 imagery","volume":"104","author":"Xu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"045023","DOI":"10.1088\/1748-9326\/8\/4\/045023","article-title":"Impacts of an extreme cyclone event on landscape-scale savanna fire, productivity and greenhouse gas emissions","volume":"8","author":"Hutley","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1641\/0006-3568(2001)051[0723:CCAFD]2.0.CO;2","article-title":"Climate change and forest disturbances: Climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides","volume":"51","author":"Dale","year":"2001","journal-title":"BioScience"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1890\/1540-9295(2007)5[80:SHIUER]2.0.CO;2","article-title":"Spatial heterogeneity in urban ecosystems: Reconceptualizing land cover and a framework for classification","volume":"5","author":"Cadenasso","year":"2007","journal-title":"Front. Ecol. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hogan, J.A., Zimmerman, J.K., Thompson, J., Uriarte, M., Swenson, N.G., Condit, R., Hubbell, S., Johnson, D.J., Sun, I.F., and Chang-Yang, C.-H. (2018). The frequency of cyclonic wind storms shapes tropical forest dynamism and functional trait dispersion. Forests, 9.","DOI":"10.3390\/f9070404"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.foreco.2018.03.052","article-title":"Understanding hurricane resistance and resilience in tropical dry forest trees: A functional traits approach","volume":"426","author":"Paz","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"683","DOI":"10.3389\/fpls.2020.00683","article-title":"Functional relationships of wood anatomical traits in Norway Spruce","volume":"11","author":"Piermattei","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hernandez, J.O., Maldia, L.S.J., and Park, B.B. (2020). Research Trends and Methodological Approaches of the Impacts of Windstorms on Forests in Tropical, Subtropical, and Temperate Zones: Where Are We Now and How Should Research Move Forward?. Plants, 9.","DOI":"10.3390\/plants9121709"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.ijdrr.2017.02.008","article-title":"Tropical cyclone disaster management using remote sensing and spatial analysis: A review","volume":"22","author":"Hoque","year":"2017","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"St. Peter, J., Anderson, C., Drake, J., and Medley, P. (2020). Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event. Remote Sens., 12.","DOI":"10.3390\/rs12071138"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.agrformet.2009.09.009","article-title":"Post-hurricane forest damage assessment using satellite remote sensing","volume":"150","author":"Wang","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s10980-015-0195-3","article-title":"Quantifying spatiotemporal pattern of urban greenspace: New insights from high resolution data","volume":"30","author":"Qian","year":"2015","journal-title":"Landsc. Ecol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s10661-008-0500-6","article-title":"Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale","volume":"156","author":"Wang","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Shi, Y., Maja, J.M., and Pe\u00f1a, J.M. (2021). UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions. Remote Sens., 13.","DOI":"10.3390\/rs13112139"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108156","DOI":"10.1016\/j.ecolind.2021.108156","article-title":"Characterizing vegetation complexity with unmanned aerial systems (UAS)\u2014A framework and synthesis","volume":"131","author":"Gago","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Larrinaga, A.R., and Brotons, L. (2019). Greenness indices from a low-cost UAV imagery as tools for monitoring post-fire forest recovery. Drones, 3.","DOI":"10.3390\/drones3010006"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shin, J.-I., Seo, W.-W., Kim, T., Park, J., and Woo, C.-S. (2019). Using UAV multispectral images for classification of forest burn severity\u2014A case study of the 2019 Gangneung forest fire. Forests, 10.","DOI":"10.3390\/f10111025"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ufug.2018.01.010","article-title":"Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft","volume":"30","author":"Honkavaara","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mokro\u0161, M., V\u00fdbo\u0161\u0165ok, J., Mergani\u010d, J., Hollaus, M., Barton, I., Kore\u0148, M., Toma\u0161t\u00edk, J., and \u010cer\u0148ava, J. (2017). Early Stage Forest Windthrow Estimation Based on Unmanned Aircraft System Imagery. Forests, 8.","DOI":"10.3390\/f8090306"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107784","DOI":"10.1016\/j.agrformet.2019.107784","article-title":"Impact assessment of a super-typhoon on Hong Kong\u2019s secondary vegetation and recommendations for restoration of resilience in the forest succession","volume":"280","author":"Abbas","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e03518","DOI":"10.1002\/ecy.3518","article-title":"Satellite-derived NDVI underestimates the advancement of alpine vegetation growth over the past three decades","volume":"102","author":"Wang","year":"2021","journal-title":"Ecology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s11056-019-09754-5","article-title":"Monitoring forest structure to guide adaptive management of forest restoration: A review of remote sensing approaches","volume":"51","author":"Camarretta","year":"2020","journal-title":"New For."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s13595-017-0669-3","article-title":"Stand-level wind damage can be assessed using diachronic photogrammetric canopy height models","volume":"74","author":"Renaud","year":"2017","journal-title":"Ann. For. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107494","DOI":"10.1016\/j.ecolind.2021.107494","article-title":"An improved approach to estimate above-ground volume and biomass of desert shrub communities based on UAV RGB images","volume":"125","author":"Mao","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Dragut","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.ecolind.2012.05.017","article-title":"A novel thermal index improves prediction of vegetation zones: Associating temperature sum with thermal seasonality","volume":"23","author":"Chiu","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.scitotenv.2017.11.255","article-title":"Identification of fine scale and landscape scale drivers of urban aboveground carbon stocks using high-resolution modeling and mapping","volume":"622","author":"Mitchell","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ecolind.2017.09.043","article-title":"Spatial pattern of urban green spaces in a long-term compact urbanization process\u2014A case study in China","volume":"96","author":"Sun","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"126946","DOI":"10.1016\/j.ufug.2020.126946","article-title":"Remote sensing of urban green spaces: A review","volume":"57","author":"Shahtahmassebi","year":"2021","journal-title":"Urban For. Urban Green."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s40725-019-00094-3","article-title":"Structure from motion photogrammetry in forestry: A review","volume":"5","author":"Iglhaut","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree allometry and improved estimation of carbon stocks and balance in tropical forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1111\/j.1365-2664.2011.02021.x","article-title":"Mapping an urban ecosystem service: Quantifying above-ground carbon storage at a city-wide scale","volume":"48","author":"Davies","year":"2011","journal-title":"J. Appl. Ecol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2013.02.017","article-title":"Region-based automatic building and forest change detection on Cartosat-1 stereo imagery","volume":"79","author":"Tian","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.foreco.2016.09.005","article-title":"Forest susceptibility to storm damage is affected by similar factors regardless of storm type: Comparison of thunder storms and autumn extra-tropical cyclones in Finland","volume":"381","author":"Suvanto","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1093\/forestry\/cpn022","article-title":"A review of mechanistic modelling of wind damage risk to forests","volume":"81","author":"Gardiner","year":"2008","journal-title":"Forestry"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.scs.2018.08.001","article-title":"Improving pedestrian level low wind velocity environment in high-density cities: A general framework and case study","volume":"42","author":"Du","year":"2018","journal-title":"Sustain. Cities Soc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"795","DOI":"10.5194\/nhess-8-795-2008","article-title":"Storm damage in the Black Forest caused by the winter storm \u201cLothar\u201d\u2014Part 1: Airborne damage assessment","volume":"8","author":"Schmoeckel","year":"2008","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1080\/02827581.2015.1056751","article-title":"Using multi-source data to map and model the predisposition of forests to wind disturbance","volume":"31","author":"Saarinen","year":"2016","journal-title":"Scand. J. For. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1002\/qj.328","article-title":"Large-eddy simulation of turbulent flow over a forested hill: Validation and coherent structure identification","volume":"134","author":"Dupont","year":"2008","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0378-1127(00)00449-7","article-title":"Species diversity of three major urban forest types in Guangzhou City, China","volume":"146","author":"Jim","year":"2001","journal-title":"For. Ecol. Manag."},{"key":"ref_49","first-page":"3539","article-title":"Key street tree species selection in urban areas","volume":"6","author":"Li","year":"2011","journal-title":"Afr. J. Agric. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.ufug.2014.11.002","article-title":"Preferences for street configuration and street tree planting in urban Hong Kong","volume":"14","author":"Ng","year":"2015","journal-title":"Urban For. Urban Green."},{"key":"ref_51","first-page":"711","article-title":"Use of a multispectral UAV photogrammetry for detection and tracking of forest disturbance dynamics","volume":"41","author":"Langhammer","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Miyoshi, G.T., Arruda, M.D.S., Osco, L.P., Marcato Junior, J., Gon\u00e7alves, D.N., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., and Gon\u00e7alves, W.N. (2020). A novel deep learning method to identify single tree species in UAV-based hyperspectral images. Remote Sens., 12.","DOI":"10.3390\/rs12081294"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wu, J., Yao, W., and Polewski, P. (2018). Mapping individual tree species and vitality along urban road corridors with LiDAR and imaging sensors: Point density versus view perspective. Remote Sens., 10.","DOI":"10.3390\/rs10091403"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens., 12.","DOI":"10.3390\/rs12061046"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2093\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:01:59Z","timestamp":1760137319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2093"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":54,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092093"],"URL":"https:\/\/doi.org\/10.3390\/rs14092093","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,27]]}}}