{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:30:57Z","timestamp":1762507857756,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T00:00:00Z","timestamp":1570579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013276","name":"Interreg","doi-asserted-by":"publisher","award":["019-2-03-032"],"award-info":[{"award-number":["019-2-03-032"]}],"id":[{"id":"10.13039\/100013276","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>From global monitoring to regional forest management there is an increasing demand for information about forest ecosystems. For border regions that are closely connected ecologically and economically, a key factor is the cross-border availability and consistency of up-to-date information such as the forest type. The combination of existing forest information with Earth observation data is a rational method and can provide valuable contribution to serve the increased information demand on a transnational level. We present an approach for the remote sensing-based generation of a transnational and temporally consistent forest type information layer for the German federal states of Rhineland-Palatinate and Saarland, and the Grand Duchy of Luxembourg. Existing forest information data from different countries were merged and combined with suitable vegetation indices derived from Landsat 8 and Sentinel-2 imagery acquired in early spring. An automated bootstrap-based approximation of the optimum threshold for the distinction of \u201cbroadleaved\u201d and \u201cconiferous\u201d forest was applied. The spatially explicit forest type information layer is updated annually depending on image availability. Overall accuracies between 79 and 96 percent were obtained. Every spot in the region will be updated successively within a period of expectably three years. The presented approach can be integrated in fully automated processing chains to generate basic forest type information layers on a regular basis.<\/jats:p>","DOI":"10.3390\/rs11202337","type":"journal-article","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T11:25:57Z","timestamp":1570620357000},"page":"2337","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region"],"prefix":"10.3390","volume":"11","author":[{"given":"Sascha","family":"Nink","sequence":"first","affiliation":[{"name":"Environmental Remote Sensing and Geoinformatics, Trier University, Behringstra\u00dfe 21, 54286 Trier, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joachim","family":"Hill","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing and Geoinformatics, Trier University, Behringstra\u00dfe 21, 54286 Trier, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Stoffels","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing and Geoinformatics, Trier University, Behringstra\u00dfe 21, 54286 Trier, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0956-5628","authenticated-orcid":false,"given":"Henning","family":"Buddenbaum","sequence":"additional","affiliation":[{"name":"Environmental Remote Sensing and Geoinformatics, Trier University, Behringstra\u00dfe 21, 54286 Trier, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9292-3931","authenticated-orcid":false,"given":"David","family":"Frantz","sequence":"additional","affiliation":[{"name":"Earth Observation Lab, Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, 10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joachim","family":"Langshausen","sequence":"additional","affiliation":[{"name":"State Forest Service Rhineland-Palatinate, Office for Forest Planning, Rhein-Mosel-Stra\u00dfe 7-9, 56281 Emmelshausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/506153a","article-title":"Carbon sequestration: Managing forests in uncertain times","volume":"506","author":"Bellassen","year":"2014","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.5751\/ES-02670-130251","article-title":"Mapping the World\u2019s Intact Forest Landscapes by Remote Sensing","volume":"13","author":"Potapov","year":"2008","journal-title":"Ecol. Soc."},{"key":"ref_3","unstructured":"FAO (2010). Global Forest Resources Assessment 2010, Food and Agriculture Organization of the United Nations. Main Report."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_5","unstructured":"Ministerium der Justiz Rheinland-Pfalz (2000). Landeswaldgesetz. LWaldG."},{"key":"ref_6","unstructured":"(2017). Gesetz zur Erhaltung des Waldes und zur F\u00f6rderung der Forstwirtschaft (Bundeswaldgesetz). BWaldG, BGBl. I S."},{"key":"ref_7","unstructured":"(2019, October 03). United Nations Framework Convention on Climate Change. Available online: https:\/\/unfccc.int\/resource\/docs\/convkp\/kpeng.pdf."},{"key":"ref_8","first-page":"385","article-title":"Negotiating the future under the shadow of the past: The eleventh session of the United Nations Forum on Forests and the 2015 renewal of the international arrangement on forests","volume":"17","author":"Humphreys","year":"2015","journal-title":"Int. For. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1126\/science.aaa4216","article-title":"Conservation. Committing to ecological restoration","volume":"348","author":"Suding","year":"2015","journal-title":"Science"},{"key":"ref_10","unstructured":"Forest Europe (2018, November 20). Oslo Ministerial Decision: European Forests 2020. Available online: https:\/\/www.foresteurope.org\/docs\/MC\/MC_oslo_decision.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1109\/TGRS.2011.2158548","article-title":"Data Fusion of Different Spatial Resolution Remote Sensing Images Applied to Forest-Type Mapping","volume":"49","author":"Kempeneers","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","unstructured":"Langanke, T. (2017). Copernicus Land Monitoring Service\u2014High Resolution Layer Forest: Product Specifications Document."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1579\/0044-7447-32.8.542","article-title":"Countrywide Estimates of Forest Variables Using Satellite Data and Field Data from the National Forest Inventory","volume":"32","author":"Reese","year":"2003","journal-title":"AMBIO"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4937","DOI":"10.1080\/01431160903022936","article-title":"A comparative analysis of k NN and decision tree methods for the Irish National Forest Inventory","volume":"30","author":"McInerney","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","unstructured":"Tomppo, E., Katila, M., M\u00e4kisara, K., and Per\u00e4saari, J. (2012). The Multi-source National Forest Inventory of Finland\u2014Methods and results 2007, Working Papers of the Finnish Forest Research Institute."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2006.09.034","article-title":"Remote sensing support for national forest inventories","volume":"110","author":"McRoberts","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1016\/j.rse.2011.08.021","article-title":"Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area","volume":"115","author":"Gobakken","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","unstructured":"Olui\u0107, M. (2005). Operational use of remote sensing for regional level assessment of forest estate values. New Strategies for European Remote Sensing: Proceedings of the 24th Symposium of the European Association of Remote Sensing Laboratories, Dubrovnik, Croatia, 25\u201327 May 2004, Millpress."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2010.10.001","article-title":"Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia","volume":"115","author":"Potapov","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.3390\/f6061982","article-title":"Satellite-Based Derivation of High-Resolution Forest Information Layers for Operational Forest Management","volume":"6","author":"Stoffels","year":"2015","journal-title":"Forests"},{"key":"ref_21","unstructured":"Beek, K.J., and Molenaar, M. (2000). Combining Remote Sensing Data Sources and Terrestrial Sample-Based Inventory Data for the Use In Forest Management Inventories. International Archives of Photogrammetry and Remote Sensing, GITC BV."},{"key":"ref_22","unstructured":"Ranchin, T., and Wald, L. (2000). Integration of terrestrial forest sample plot data, map information and satellite data: An operational multisource inventory concept. Fusion of Earth Data: Merging Point Measurements, Raster Maps and Remotely Sensed Images, Proceedings of the Fusion of Earth Data, The Third International Conference, Sophia Antipolis, C\u00f4te d\u2019Azur, France, 26\u201328 January 2000, SEE\/URISCA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1080\/01431160310001642296","article-title":"Contextual classification of Landsat TM images to forest inventory cover types","volume":"25","author":"Magnussen","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5453","DOI":"10.1080\/01431160500285076","article-title":"Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods","volume":"26","author":"Buddenbaum","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0378-1127(03)00113-0","article-title":"Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification","volume":"183","author":"Dorren","year":"2003","journal-title":"For. Ecol. Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.rse.2006.08.017","article-title":"A method for calibrated maximum likelihood classification of forest types","volume":"110","author":"Hagner","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.14358\/PERS.74.11.1379","article-title":"Conterminous U.S. and Alaska Forest Type Mapping Using Forest Inventory and Analysis Data","volume":"74","author":"Ruefenacht","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1080\/15481603.2013.819161","article-title":"Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest","volume":"50","author":"Li","year":"2013","journal-title":"Gisci. Remote Sens."},{"key":"ref_29","unstructured":"Schaepman, M.E., Liang, S., Groot, N., and Kneub\u00fchler, M. (2007, January 12\u201314). Alternative application of the k-NN Method for mapping forest cover type. Proceedings of the ISPRS Working Group VII\/1 Workshop ISPMSRS\u201907, Physical Measurements and Signatures in Remote Sensing, Davos, Switzerland."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.rse.2008.05.021","article-title":"Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery","volume":"113","author":"Tomppo","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1080\/10042857.2004.10677383","article-title":"Identification of Forest Vegetation Using Vegetation Indices","volume":"2","author":"Jinguo","year":"2004","journal-title":"Chin. J. Popul. Resour. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/19475683.2018.1552621","article-title":"Genetic algorithm-based method for forest type classification using multi-temporal NDVI from Landsat TM imagery","volume":"25","author":"Tao","year":"2019","journal-title":"Ann. GIS"},{"key":"ref_33","unstructured":"Gauer, J., and Aldinger, E. (2005). Wald\u00f6kologische Naturr\u00e4ume Deutschlands: Forstliche Wuchsgebiete und Wuchsbezirke. Klima. Wald\u00f6kologische Naturr\u00e4ume Deutschlands: Forstliche Wuchsgebiete und Wuchsbezirke; mit Karte 1:1.000.000, Verein f\u00fcr Forstliche Standortskunde und Forstpflanzenz\u00fcchtung."},{"key":"ref_34","unstructured":"Landesforsten Rheinland-Pfalz (2019, October 03). Landeswaldinventur: Emmelshausen, Germany. Available online: http:\/\/www.lebensenergie-riegelsberg.de\/downloads\/WBRL_Nov_2008aktuell.pdf."},{"key":"ref_35","unstructured":"Ministerium f\u00fcr Umwelt und Verbraucherschutz Saarland (2010). Forsteinrichtung."},{"key":"ref_36","unstructured":"Administration de la Nature et des For\u00eats (2019, March 20). Administrative Units of Forest Administration, Available online: https:\/\/data.public.lu\/en\/datasets\/administrative-units-of-forest-administration\/."},{"key":"ref_37","unstructured":"Landesforsten Rheinland-Pfalz (2019, October 03). w\u00f6FIS: Wald\u00f6kologisches Forstinformationssystem. Emmelshausen. Available online: https:\/\/www.kastanien.net\/de\/data\/_uploaded\/pdf\/40projekte\/aktuell_74-15_edelkastanie_am_oberrhein.pdf."},{"key":"ref_38","first-page":"728","article-title":"Weiterentwicklung der Forsteinrichtung in Rheinland-Pfalz","volume":"58","author":"Peerenboom","year":"2003","journal-title":"For. Und Holz"},{"key":"ref_39","unstructured":"Administration de la Nature et des For\u00eats (2019, March 13). La Planification de la Gestion des For\u00eats Publiques, Available online: https:\/\/environnement.public.lu\/fr\/natur\/forets\/gestion_durable_forets_publiques.html."},{"key":"ref_40","unstructured":"Ministerium f\u00fcr Umwelt und Verbraucherschutz Saarland (2014). SaarForst Landesbetrieb."},{"key":"ref_41","unstructured":"Riedel, T., Hennig, P., Kroiher, F., Polley, H., Schmitz, F., and Schwitzgebel, F. (2017). Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertungsmethoden."},{"key":"ref_42","first-page":"3","article-title":"L\u2019inventaire forestier national permanent du Grand-Duch\u00e9 de Luxembourg, dix ann\u00e9es d\u2019existence","volume":"103","author":"Rondeux","year":"2009","journal-title":"Wallone"},{"key":"ref_43","unstructured":"(2010). Administration de la Nature et Des For\u00eats, Inventure National des For\u00eats Luxembourg."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/0034-4257(95)00195-6","article-title":"Retrieving leaf area index of boreal conifer forests using Landsat TM images","volume":"55","author":"Chen","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.07.033","article-title":"Landsat 8: The plans, the reality, and the legacy","volume":"185","author":"Loveland","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_47","unstructured":"Fletcher, K. (2012). ESA\u2019s Optical High-Resolution Mission for GMES Operational Services, ESA Communications."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free access to Landsat imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Frantz, D. (2019). FORCE\u2014Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sens., 11.","DOI":"10.3390\/rs11091124"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3928","DOI":"10.1109\/TGRS.2016.2530856","article-title":"An Operational Radiometric Landsat Preprocessing Framework for Large-Area Time Series Applications","volume":"54","author":"Frantz","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1080\/01431169008955048","article-title":"Description of a computer code to simulate signal in the solar spectrum: The 5S code","volume":"11","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.rse.2018.04.046","article-title":"Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects","volume":"215","author":"Frantz","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A Calibration and Products Validation Status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_58","unstructured":"Pearson, R.L. (1972, January 2\u20136). Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie. Proceedings of the International Sympathy on Remote Sensing of Environment, Ann Arbor, MI, USA."},{"key":"ref_59","unstructured":"Rouse, J.W. (1973). Monitoring the Vernal Advancement and Retrogradation of Natural Vegetation, NASA\/GSFCT Type II Report."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","article-title":"Calculating the vegetation index faster","volume":"34","author":"Crippen","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1214\/aos\/1176344552","article-title":"Bootstrap Methods: Another Look at the Jackknife","volume":"7","author":"Efron","year":"1979","journal-title":"Ann. Stat."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_67","unstructured":"Sch\u00f6nthaler, K. (2015). Monitoringbericht 2015 zur Deutschen Anpassungsstrategie an den Klimawandel. Bericht der Interministeriellen Arbeitsgruppe Anpassungsstrategie der Bundesregierung."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"12009","DOI":"10.3390\/rs70912009","article-title":"Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume","volume":"7","author":"Nink","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2337\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:28:40Z","timestamp":1760189320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2337"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,9]]},"references-count":68,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11202337"],"URL":"https:\/\/doi.org\/10.3390\/rs11202337","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,10,9]]}}}