{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T04:53:20Z","timestamp":1774328000277,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T00:00:00Z","timestamp":1613865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31670552"],"award-info":[{"award-number":["31670552"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971577"],"award-info":[{"award-number":["31971577"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019M651842"],"award-info":[{"award-number":["2019M651842"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"publisher","award":["PAPD"],"award-info":[{"award-number":["PAPD"]}],"id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many post-fire on-site factors, including fire severity, management strategies, topography, and local climate, are concerns for forest managers and recovery ecologists to formulate forest vegetation recovery plans in response to climate change. We used the Vegetation Change Tracker (VCT) algorithm to map forest disturbance in the Daxing\u2019anling area, Northeastern China, from 1987 to 2016. A support vector machine (SVM) classifier and historical fire records were used to separate burned patches from disturbance patches obtained from VCT. Afterward, stepwise multiple linear regression (SMLR), SVM, and random forest (RF) were applied to assess the statistical relationships between vegetation recovery characteristics and various influential factors. The results indicated that the forest disturbance events obtained from VCT had high spatial accuracy, ranging from 70% to 86% for most years. The overall accuracy of the annual fire patches extracted from the proposed VCT-SVM algorithm was over 92%. The modeling accuracy of post-fire vegetation recovery was excellent, and the validation results confirmed that the RF algorithm provided better prediction accuracy than SVM and SMLR. In conclusion, topographic variables (e.g., elevation) and meteorological variables (e.g., the post-fire annual precipitation in the second year, the post-fire average relative humidity in the fifth year, and the post-fire extreme maximum temperature in the third year) jointly affect vegetation recovery in this cold temperate continental monsoon climate region.<\/jats:p>","DOI":"10.3390\/rs13040792","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T22:04:15Z","timestamp":1613945055000},"page":"792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Quantifying Forest Fire and Post-Fire Vegetation Recovery in the Daxin\u2019anling Area of Northeastern China Using Landsat Time-Series Data and Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Jie","family":"Qiu","sequence":"first","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Heng","family":"Wang","sequence":"additional","affiliation":[{"name":"Land-Reserve Center of Jiangbei New Area, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8691-8036","authenticated-orcid":false,"given":"Wenjuan","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Yali","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Huiyi","family":"Su","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5689-5091","authenticated-orcid":false,"given":"Mingshi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"},{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1126\/science.aam7672","article-title":"Using fire to promote biodiversity","volume":"355","author":"Kelly","year":"2017","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1126\/science.1128834","article-title":"Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity","volume":"313","author":"Westerling","year":"2006","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"267","DOI":"10.5194\/bg-13-267-2016","article-title":"Climate, CO2 and human population impacts on global wildfire emissions","volume":"13","author":"Knorr","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sugihara, N.G., Van Wagtendonk, J.W., Shaffer, K.E., Fites-Kaufman, J., and Thode, A.E. (2006). Fire in California\u2019s Ecosystems, University of California Press.","DOI":"10.1525\/california\/9780520246058.003.0024"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.rse.2019.02.013","article-title":"Historical background and current developments for mapping burned area from satellite Earth observation","volume":"225","author":"Chuvieco","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ramo, R., and Chuvieco, E. (2017). Developing a Random Forest Algorithm for MODIS Global Burned Area Classification. Remote. Sens., 9.","DOI":"10.3390\/rs9111193"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1111\/geb.12095","article-title":"Integration of ecological and socio-economic factors to assess global vulnerability to wildfire","volume":"23","author":"Chuvieco","year":"2014","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_8","first-page":"173","article-title":"Numerical simulation of the impact of changes in the vegetation in the western China on the summer climate over the northern China","volume":"68","author":"Chen","year":"2010","journal-title":"Acta Meteorol. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s00382-010-0827-x","article-title":"Impact of vegetation feedback on the temperature and its diurnal range over the Northern Hemisphere during summer in a 2 \u00d7 CO2 climate","volume":"37","author":"Jeong","year":"2011","journal-title":"Clim. Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8129","DOI":"10.1002\/jgrd.50602","article-title":"Improved vegetation greenness increases summer atmospheric water vapor over Northern China","volume":"118","author":"Jiang","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"995","DOI":"10.5194\/acp-20-995-2020","article-title":"Using CESM-RESFire to understand climate\u2013fire\u2013ecosystem interactions and the implications for decadal climate variability","volume":"20","author":"Zou","year":"2020","journal-title":"Atmospheric Chem. Phys. Discuss."},{"key":"ref_12","first-page":"1","article-title":"Effects of Fire and Climate on Successions and Structural Changes in The Siberian Boreal Forest","volume":"2","author":"Furyaev","year":"2001","journal-title":"Eurasian J. For. Res.-Hokkaido Univ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1111\/j.1442-9993.2011.02355.x","article-title":"Relative influence of habitat structure, species interactions and rainfall on the post-fire population dynamics of ground-dwelling vertebrates","volume":"37","author":"Arthur","year":"2012","journal-title":"Austral Ecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.catena.2006.05.003","article-title":"Influence of vegetation recovery on water erosion at short and medium-term after experimental fires in a Mediterranean shrubland","volume":"69","author":"Andreu","year":"2007","journal-title":"Catena"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/nclimate2200","article-title":"Cheap carbon and biodiversity co-benefits from forest regeneration in a hotspot of endemism","volume":"4","author":"Gilroy","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_16","first-page":"139","article-title":"Conifer regeneration after forest fire in the Klamath-Siskiyous: How much, how soon?","volume":"105","author":"Shatford","year":"2007","journal-title":"J. For."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.foreco.2017.04.006","article-title":"Interacting effects of fire severity, time since fire and topography on vegetation structure after wildfire","volume":"396","author":"Bassett","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.quascirev.2012.11.029","article-title":"Global biomass burning: A synthesis and review of Holocene paleofire records and their controls","volume":"65","author":"Marlon","year":"2013","journal-title":"Quat. Sci. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.foreco.2014.06.005","article-title":"Severity of an uncharacteristically large wildfire, the Rim Fire, in forests with relatively restored frequent fire regimes","volume":"328","author":"Lydersen","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1109\/TGRS.2009.2031557","article-title":"Mapping Postfire Vegetation Recovery Using EO-1 Hyperion Imagery","volume":"48","author":"Mitri","year":"2009","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/17538947.2012.713190","article-title":"Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges","volume":"5","author":"Townshend","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ecolind.2018.02.008","article-title":"Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series","volume":"89","author":"Bruno","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107641","DOI":"10.1016\/j.agrformet.2019.107641","article-title":"Local land surface temperature change induced by afforestation based on satellite observations in Guangdong plantation forests in China","volume":"276","author":"Shen","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1016\/j.foreco.2008.12.023","article-title":"Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model","volume":"257","author":"Li","year":"2009","journal-title":"For. Ecol. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/LGRS.2015.2418159","article-title":"Use of Vegetation Change Tracker and Support Vector Machine to Map Disturbance Types in Greater Yellowstone Ecosystems in a 1984\u20132010 Landsat Time Series","volume":"12","author":"Zhao","year":"2015","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1080\/01431161.2018.1533662","article-title":"Mapping forest disturbance across the China\u2013Laos border using annual Landsat time series","volume":"40","author":"Tang","year":"2018","journal-title":"Int. J. Remote. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2011.11.022","article-title":"Reconstructing disturbance history using satellite-based assessment of the distribution of land cover in the Russian Far East","volume":"118","author":"Loboda","year":"2012","journal-title":"Remote. Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.rse.2010.07.009","article-title":"Validation of North American Forest Disturbance dynamics derived from Landsat time series stacks","volume":"115","author":"Thomas","year":"2011","journal-title":"Remote. Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"470","DOI":"10.3390\/rs6010470","article-title":"Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review","volume":"6","author":"Chu","year":"2013","journal-title":"Remote. Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.rse.2015.10.024","article-title":"Effects of fire severity and post-fire climate on short-term vegetation recovery of mixed-conifer and red fir forests in the Sierra Nevada Mountains of California","volume":"171","author":"Meng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2018.03.019","article-title":"Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques","volume":"210","author":"Meng","year":"2018","journal-title":"Remote. Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00442-004-1788-8","article-title":"Effects of fire on properties of forest soils: A review","volume":"143","author":"Certini","year":"2005","journal-title":"Oecologia"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s11676-016-0262-x","article-title":"Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987\u20132011)","volume":"27","author":"Li","year":"2016","journal-title":"J. For. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.rse.2007.07.023","article-title":"Use of a dark object concept and support vector machines to automate forest cover change analysis","volume":"112","author":"Huang","year":"2008","journal-title":"Remote. Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote. Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1139\/x05-060","article-title":"Landscape-level interactions of prefire vegetation, burn severity, and postfire vegetation over a 16-year period in interior Alaska","volume":"35","author":"Epting","year":"2005","journal-title":"Can. J. For. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of forest harvest type using multiple dates of Landsat TM imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1177\/030913339802200402","article-title":"Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters","volume":"22","author":"Wulder","year":"1998","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2012.03.001","article-title":"A method for extracting burned areas from Landsat TM\/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm","volume":"69","author":"Stroppiana","year":"2012","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A Physically-Based Transformation of Thematic Mapper Data-The TM Tasseled Cap","volume":"22","author":"Crist","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1071\/WF08078","article-title":"Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel","volume":"19","author":"Casady","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.rse.2011.09.024","article-title":"Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan","volume":"122","author":"Kennedy","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2017.03.035","article-title":"A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series","volume":"194","author":"White","year":"2017","journal-title":"Remote. Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1080\/2150704X.2015.1126375","article-title":"Forest recovery trends derived from Landsat time series for North American boreal forests","volume":"37","author":"Pickell","year":"2015","journal-title":"Int. J. Remote. Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote. Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neucom.2014.09.091","article-title":"A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables","volume":"167","author":"Troncoso","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_49","first-page":"1","article-title":"Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon","volume":"50","author":"Feng","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shen, W., Li, M., Huang, C., and Wei, A. (2016). Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data. Remote. Sens., 8.","DOI":"10.3390\/rs8070595"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1016\/j.rse.2009.06.001","article-title":"A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data","volume":"113","author":"Cao","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"International Journal of Remote Sensing An assessment of support vector machines for land cover classification An assessment of support vector machines for land cover classi cation","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3543","DOI":"10.1002\/2014WR016826","article-title":"Long-range seasonal streamflow forecasting over the Iberian Peninsula using large-scale atmospheric and oceanic information","volume":"51","author":"Argueso","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6938","DOI":"10.3390\/rs5126938","article-title":"Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China","volume":"5","author":"Yi","year":"2013","journal-title":"Remote. Sens."},{"key":"ref_55","unstructured":"Zhang, Y. (2008). Study on the impacts of climate change on forest fires in Daxing\u2019anling mountiains. [Master\u2019s Thesis, Northeast Forestry University]. (In Chinese)."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ecoleng.2014.06.016","article-title":"Post-fire forest regeneration under different restoration treatments in the Greater Hinggan Mountain area of China","volume":"70","author":"Chen","year":"2014","journal-title":"Ecol. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/j.1365-2486.2008.01784.x","article-title":"Analysing forest recovery after wildfire disturbance in boreal Siberia using remotely sensed vegetation indices","volume":"15","author":"Gerard","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_58","unstructured":"Minore, D., and Laacke, R.J. (1992). Natural Regeneration, Oregon State University Press."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.rse.2015.11.024","article-title":"A forest vulnerability index based on drought and high temperatures","volume":"173","author":"Mildrexler","year":"2016","journal-title":"Remote. Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"37572","DOI":"10.1038\/srep37572","article-title":"Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China","volume":"6","author":"Liu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s42408-018-0021-9","article-title":"Examining post-fire vegetation recovery with Landsat time series analysis in three western North American forest types","volume":"15","author":"Bright","year":"2019","journal-title":"Fire Ecol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/792\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:27:28Z","timestamp":1760160448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,21]]},"references-count":61,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040792"],"URL":"https:\/\/doi.org\/10.3390\/rs13040792","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,21]]}}}