{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T09:18:03Z","timestamp":1776590283797,"version":"3.51.2"},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2017YFD0600903"],"award-info":[{"award-number":["2017YFD0600903"]}]},{"name":"the National Natural Science Foundation of China","award":["41771370, 31760707"],"award-info":[{"award-number":["41771370, 31760707"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.<\/jats:p>","DOI":"10.3390\/rs13214426","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T21:57:49Z","timestamp":1635976669000},"page":"4426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Estimation of the Conifer-Broadleaf Ratio in Mixed Forests Based on Time-Series Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5275-1645","authenticated-orcid":false,"given":"Ranran","family":"Yang","sequence":"first","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwest National Key Laboratory Breeding Base for Land Degradation and Ecological Restoration, Ningxia University, Yinchuan 750021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0986-6479","authenticated-orcid":false,"given":"Qingjiu","family":"Tian","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nianxu","family":"Xu","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3439-6002","authenticated-orcid":false,"given":"Yanjun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, Lexington, KY 40546, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"ref_1","unstructured":"Canadian Council of Forest Ministers (2020, April 06). Criteria and Indicators of Sustainable Forest Management in Canada: National Status. Available online: https:\/\/d1ied5g1xfgpx8.cloudfront.net\/pdfs\/18104.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/S0269-7491(01)00212-3","article-title":"Measuring carbon in forests: Current status and future challenges","volume":"116","author":"Brown","year":"2002","journal-title":"Environ. Pollut."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1073\/pnas.1219393110","article-title":"Remote sensing of canopy chemistry","volume":"110","author":"Ustin","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Krzystek, P., Serebryanyk, A., Schn\u00f6rr, C., \u010cervenka, J., and Heurich, M. (2020). Large-scale mapping of tree species and dead trees in \u0161umava national park and bavarian forest national park using lidar and multispectral imagery. Remote Sens., 12.","DOI":"10.3390\/rs12040661"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/S0304-3800(01)00476-8","article-title":"Modelling mixed forest growth: A review of models for forest management","volume":"150","author":"Bartelink","year":"2002","journal-title":"Ecol. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1073\/pnas.1304176110","article-title":"Nitrogen cycling, forest canopy reflectance, and emergent properties of ecosystems","volume":"110","author":"Ollinger","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3958","DOI":"10.1016\/j.rse.2008.07.003","article-title":"Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels","volume":"112","author":"Asner","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.rse.2003.10.001","article-title":"The effect of crown shape on the reflectance of coniferous stands","volume":"89","author":"Rautiainen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"E185","DOI":"10.1073\/pnas.1210196109","article-title":"Hyperspectral remote sensing of foliar nitrogen content","volume":"110","author":"Knyazikhin","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112407","DOI":"10.1016\/j.rse.2021.112407","article-title":"Spatio-temporal spectral unmixing of time-series images","volume":"259","author":"Wang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., and Panagiotidis, D. (2020). Tree species classification and health status assessment for a mixed broadleaf-conifer forest with UAS multispectral imaging. Remote Sens., 12.","DOI":"10.3390\/rs12223722"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112440","DOI":"10.1016\/j.rse.2021.112440","article-title":"The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem","volume":"260","author":"Nieto","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111471","DOI":"10.1016\/j.rse.2019.111471","article-title":"Assessing the impact of endmember variability on linear spectral mixture analysis (LSMA): A theoretical and simulation analysis","volume":"235","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/0168-1923(91)90108-3","article-title":"Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand","volume":"56","author":"Chen","year":"1991","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zou, T.Y., and Zhang, J. (2020). A New fluorescence quantum yield efficiency retrieval method to simulate chlorophyll fluorescence under natural conditions. Remote Sens., 12.","DOI":"10.3390\/rs12244053"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0034-4257(99)00006-1","article-title":"Spatial scaling of a remotely sensed surface parameter by contexture","volume":"69","author":"Chen","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/TGRS.1995.8746028","article-title":"Hybrid geometric optical-radiative transfer approach for modeling albedo and directional reflectance of discontinuous canopies","volume":"33","author":"Li","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liang, L., Di, G., Yan, J., Qiu, S., Di, L., Wang, S., Xu, L., Wang, L., Kang, J., and Li, L. (2020). Estimating crop LAI using spectral feature extraction and the hybrid inversion method. Remote Sens., 12.","DOI":"10.3390\/rs12213534"},{"key":"ref_19","unstructured":"Atzberger, C. (2000, January 14\u201316). Development of an invertible forest reflectance model: The INFOR-Model. A Decade of Trans-European Remote Sensing Cooperation. Proceedings of the 20th EARSeL Symposium, Dresden, Germany."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rse.2005.10.006","article-title":"Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data","volume":"100","author":"Schlerf","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0034-4257(92)90065-R","article-title":"A new forest light interaction model in support of forest monitoring","volume":"42","author":"Rosema","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0034-4257(84)90057-9","article-title":"Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model","volume":"16","author":"Verhoef","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0034-4257(85)90072-0","article-title":"Earth observation modeling based on layer scattering matrices","volume":"17","author":"Verhoef","year":"1985","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A model of leaf optical properties spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/0034-4257(95)00238-3","article-title":"Estimating leaf biochemistry using the prospect leaf optical properties model","volume":"56","author":"Jacquemoud","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0034-4257(98)00007-8","article-title":"LIBERTY-Modeling the effects of leaf biochemical concentration on reflectance spectra","volume":"65","author":"Dawson","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7425","DOI":"10.3390\/rs70607425","article-title":"Estimating forest fapar from multispectral landsat-8 data using the invertible forest reflectance model INFORM","volume":"7","author":"Yuan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","first-page":"566","article-title":"Inversion of forest leaf area index calculated from multi-source and multi-angle remote sensing data","volume":"45","author":"Yang","year":"2010","journal-title":"Chin. Bull. Bot."},{"key":"ref_29","first-page":"2836","article-title":"Research on plant spectral recognition method based on phenological features","volume":"10","author":"Zhang","year":"2015","journal-title":"Spectrosc. Spectr. Analysis."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhu, W., Wang, X., Zhan, P., Liu, Q., Li, X., and Sun, L. (2020). A method for monitoring and forecasting the heading and flowering dates of winter wheat combining satellite-derived green-up dates and accumulated temperature. Remote Sens., 12.","DOI":"10.3390\/rs12213536"},{"key":"ref_31","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS-1, Third Earth Resources Technology Satellite Symposium 1, NASA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dobrini\u0107, D., Ga\u0161parovi\u0107, M., and Medak, D. (2021). Sentinel-1 and 2 time-series for vegetation mapping using random forest classification: A case study of Northern Croatia. Remote Sens., 13.","DOI":"10.3390\/rs13122321"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Amoedo, A., \u00c1lvarez, X., Lorenzo, H., and Rodr\u00edguez, J.L. (2021). Multi-temporal Sentinel-2 data analysis for smallholding forest cut control. Remote Sens., 13.","DOI":"10.3390\/rs13152983"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chamberlain, D.A., Phinn, S.R., and Possingham, H.P. (2021). Mangrove forest cover and phenology with landsat dense time series in central queensland, Australia. Remote Sens., 13.","DOI":"10.3390\/rs13153032"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3129","DOI":"10.1016\/j.rse.2011.06.020","article-title":"A comparison of time series similarity measures for classification and change detection of ecosystem dynamics","volume":"115","author":"Lhermitte","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/0034-4257(94)90144-9","article-title":"Change-vector analysis in multitemporal space: A tool to detect and categorize land-cover change processes using high temporal-resolution satellite data","volume":"48","author":"Lambin","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1175\/1520-0442(1995)008<0897:OTAOCA>2.0.CO;2","article-title":"On the application of cluster analysis to growing season precipitation data in North America east of the rockies","volume":"8","author":"Gong","year":"1995","journal-title":"J. Clim."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.rse.2018.02.036","article-title":"Landsat time series-based multiyear spectral angle clustering (MSAC) model to monitor the inter-annual leaf senescence of exotic saltcedar","volume":"209","author":"Diao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.is.2015.04.007","article-title":"Time-series clustering\u2014A decade review","volume":"53","author":"Aghabozorgi","year":"2015","journal-title":"Inf. Syst."},{"key":"ref_41","unstructured":"Zhou, Z.H. (2016). Machine Learning, Tsinghua University Press."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xing, X., Yan, C., Jia, Y., Jia, H., Lu, J., and Luo, G. (2020). An effective high spatiotemporal resolution ndvi fusion model based on histogram clustering. Remote Sens., 12.","DOI":"10.3390\/rs12223774"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Santos, L.A., Ferreira, K., Picoli, M., Camara, G., Zurita-Milla, R., and Augustijn, E.W. (2021). Identifying spatiotemporal patterns in land use and cover samples from satellite image time series. Remote Sens., 13.","DOI":"10.3390\/rs13050974"},{"key":"ref_44","first-page":"3169","article-title":"Factors affecting the cooling effect in zijin mountain forest park","volume":"34","author":"Yan","year":"2014","journal-title":"Acta Ecol. Sin."},{"key":"ref_45","first-page":"67","article-title":"Landscape pattern and dynamic analysis of zijin mountain scenic area based on GIS","volume":"5","author":"Li","year":"2004","journal-title":"J. Nanjing For. Univ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3390\/rs6010087","article-title":"Interpretation of forest resources at the individual tree level at purple mountain, Nanjing City, China, using worldview-2 imagery by combining GPS, RS and GIS Technologies","volume":"6","author":"Deng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7878","DOI":"10.3390\/rs6097878","article-title":"Estimating forest aboveground biomass by combining ALOS PALSAR and WorldView-2 data: A case study at purple mountain national park, Nanjing, China","volume":"6","author":"Deng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0168-1923(91)90074-Z","article-title":"Measuring leaf area index of plant canopies with branch architecture","volume":"57","author":"Chen","year":"1991","journal-title":"Agric. For. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination: Part i. theories, sensors and hemispherical photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"818","DOI":"10.2134\/agronj1991.00021962008300050009x","article-title":"Instrument for indirect measurement of canopy architecture","volume":"83","author":"Welles","year":"1991","journal-title":"Agron. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1093\/jxb\/47.9.1335","article-title":"Canopy structure measurement by gap fraction analysis using commercial instrumentation","volume":"47","author":"Welles","year":"1996","journal-title":"J. Exp. Bot."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"083517","DOI":"10.1117\/1.JRS.8.083517","article-title":"Estimating canopy leaf area index in the late stages of wheat growth using continuous wavelet transform","volume":"8","author":"Huang","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_53","first-page":"5","article-title":"Technical features of gaofen-1 satellite","volume":"8","author":"Bai","year":"2013","journal-title":"Aerosp. China"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2016.02.019","article-title":"Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data","volume":"177","author":"Jia","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yu, J., Liu, Y., Ren, Y., Ma, H., Wang, D., Jing, Y., and Yu, L. (2020). Application study on double-constrained change detection for land use\/land cover based on GF-6 WFV imageries. Remote Sens., 12.","DOI":"10.3390\/rs12182943"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.rse.2004.12.016","article-title":"Remote sensing of forest biophysical variables using hymap imaging spectrometer data","volume":"95","author":"Schlerf","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wu, S.P., Gao, X., Lei, J.Q., Zhou, N., and Wang, Y.D. (2020). Spatial and temporal changes in the normalized difference vegetation index and their driving factors in the desert\/grassland biome transition zone of the sahel region of africa. Remote Sens., 12.","DOI":"10.3390\/rs12244119"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Engelen","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in spectral mixture analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_61","unstructured":"Basu, S., Banerjee, A., and Mooney, R. (2002, January 8\u201312). Semi-supervised clustering by seeding. Proceedings of the 19th International Conference on Machine Learning (ICML-2002), Sydney, NSW, Australia."},{"key":"ref_62","first-page":"101919","article-title":"Evaluating the performance of PROSPECT in the retrieval of leaf traits across canopy throughout the growing season","volume":"83","author":"Gara","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Marino, S., and Alvino, A. (2021). Vegetation indices data clustering for dynamic monitoring and classification of wheat yield crop traits. Remote Sens., 13.","DOI":"10.3390\/rs13040541"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4426\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:25:22Z","timestamp":1760167522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,3]]},"references-count":64,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214426"],"URL":"https:\/\/doi.org\/10.3390\/rs13214426","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,3]]}}}