{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:11:48Z","timestamp":1769631108408,"version":"3.49.0"},"reference-count":76,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2012,9,7]],"date-time":"2012-09-07T00:00:00Z","timestamp":1346976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT) for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS), as introduced by the Open Geospatial Consortium (OGC), are utilized. TWOPAC\u2019s functionality to process geospatial raster or vector data via web resources (server, network) enables TWOPAC\u2019s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user\u2019s perspective.<\/jats:p>","DOI":"10.3390\/rs4092530","type":"journal-article","created":{"date-parts":[[2012,9,7]],"date-time":"2012-09-07T11:21:14Z","timestamp":1347016874000},"page":"2530-2553","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification"],"prefix":"10.3390","volume":"4","author":[{"given":"Juliane","family":"Huth","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudia","family":"Kuenzer","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thilo","family":"Wehrmann","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen","family":"Gebhardt","sequence":"additional","affiliation":[{"name":"Remote Sensing Unit, Institute of Geography, University of Wuerzburg, Am Hubland, D-97074 Wuerzburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vo Quoc","family":"Tuan","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Dech","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1080\/13658810310001620906","article-title":"Spatial simulation for translating from land use to land cover","volume":"18","author":"Brown","year":"2004","journal-title":"Int. J. Geogr. Inf. Sci"},{"key":"ref_2","unstructured":"Campbell, J.B. (2006). Introduction to Remote Sensing, Taylor & Francis. [4th ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1080\/00330124.2001.9628460","article-title":"From land cover to land use: A methodology for efficient land use mapping over large areas","volume":"53","author":"Cihlar","year":"2001","journal-title":"Prof. Geogr"},{"key":"ref_4","unstructured":"Herold, M. (2009). Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables. Land. Land Cover, FAO."},{"key":"ref_5","unstructured":"Jensen, J.R. (2007). Remote Sensing of the Environment: An Earth Resource Perspective, Pearson Prentice Hall."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/014311600210092","article-title":"Land cover mapping of large areas from satellites: Status and research priorities","volume":"21","author":"Cihlar","year":"2000","journal-title":"Int. J. Remote Sens"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1191\/0309133302pp332ra","article-title":"Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas","volume":"26","author":"Franklin","year":"2002","journal-title":"Progr. Phys. Geogr"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.rse.2009.01.010","article-title":"Land cover mapping of large areas using chain classification of neighboring Landsat satellite images","volume":"113","author":"Knorn","year":"2009","journal-title":"Remote Sens. Environ"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1016\/j.rse.2007.10.004","article-title":"Mapping land-cover modifications over large areas: A comparison of machine learning algorithms","volume":"112","author":"Rogan","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_10","unstructured":"Schaaf, C.B., Barry, R.G., Brady, M., Brown, J., CEOS, Christiansen, H.H., Cihlar, J., Clow, G., Csiszar, I., and Dolman, H. (2008). Terrestrial Essential Climate Variables: Climate Change Assessment, Mitigation and Adaptation, FAO."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"295","DOI":"10.5194\/nhess-4-295-2004","article-title":"Flood risk assessment and associated uncertainties","volume":"4","author":"Apel","year":"2004","journal-title":"Natural Hazards Earth Syst. Sci"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1002\/(SICI)1099-1085(199711)11:14<1777::AID-HYP543>3.0.CO;2-E","article-title":"Integrating remote sensing observations of flood hydrology and hydraulic modelling","volume":"11","author":"Bates","year":"1997","journal-title":"Hydrol. Process"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"407","DOI":"10.5194\/hess-14-407-2010","article-title":"Flood trends and variability in the Mekong river","volume":"14","author":"Delgado","year":"2010","journal-title":"Hydrol. Earth Syst. Sci"},{"key":"ref_14","first-page":"247","article-title":"Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management","volume":"9","author":"Matgen","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1109\/TGRS.2009.2017937","article-title":"The utility of spaceborne radar to render flood inundation maps based on multialgorithm ensembles","volume":"47","author":"Schumann","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1080\/01431161.2011.620034","article-title":"Derivation of biomass information for semi-arid areas using remote-sensing data","volume":"33","author":"Eisfelder","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Goetz, S., Baccini, A., Laporte, N.T., Johns, T., Walker, W., Kellndorfer, J., Houghton, R., and Sun, M. (2009). Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balance Manag, 4.","DOI":"10.1186\/1750-0680-4-2"},{"key":"ref_18","unstructured":"(2009). Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables, GTOS. GTOS 67."},{"key":"ref_19","unstructured":"Guenther, K.P., Borg, E., Wisskirchen, K., Schroedter-Homscheidt, M., and Fichtelmann, B. (2006, January 2\u20134). Remote Sensing and Modelling: A Tool to Provide the Spatial Information for Biomass Production Potential. Stralsund, Germany."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens"},{"key":"ref_21","unstructured":"Dobson, J.E., Bright, E.A., Coleman, P.R., and Bhaduri, B.L. (2003). LandScan: A Global Population Database for Estimating Populations at Risk, Taylor & Francis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1080\/01431160010007015","article-title":"Census from Heaven: An estimate of the global human population using night-time satellite imagery","volume":"22","author":"Sutton","year":"2001","journal-title":"Int. J. Remote Sens"},{"key":"ref_23","first-page":"315","article-title":"Assessment of different dimensions of vulnerability to natural hazards and climate change","volume":"9","author":"Glade","year":"2009","journal-title":"Natural Hazards Earth Syst. Sci"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.landurbplan.2007.07.006","article-title":"A hybrid object-based classification approach for mapping urban sprawl in periurban environment","volume":"84","author":"Jacquin","year":"2008","journal-title":"Landsc. Urban Plan"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wisner, B., Blaikie, P., Cannon, T., and Davies, I. (2004). At Risk, Routlegde. [2nd ed.].","DOI":"10.4324\/9780203974575"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"878","DOI":"10.3390\/rs3050878","article-title":"Remote sensing of mangrove ecosystems: A review","volume":"3","author":"Kuenzer","year":"2011","journal-title":"Remote Sens"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.ecolind.2012.04.022","article-title":"The concept of ecosystem services: A review","volume":"23","author":"Kuenzer","year":"2012","journal-title":"Ecol. Indic"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/S0034-4257(00)00142-5","article-title":"-W. Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data","volume":"74","author":"DeFries","year":"2000","journal-title":"Remote Sens. Environ"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/0143116031000101675","article-title":"Review ArticleDigital change detection methods in ecosystem monitoring: a review","volume":"25","author":"Coppin","year":"2004","journal-title":"Int. J. Remote Sens"},{"key":"ref_30","first-page":"119","article-title":"Change Detection Tools","volume":"VI","author":"Jasani","year":"2009","journal-title":"Remote Sensing from Space: Supporting International Peace and Security"},{"key":"ref_31","first-page":"1363","article-title":"Trend analyses of a 15 year global soil moisture time series derived from ERS-1\/-2 and METOP scatterometer data\u2014Floods, droughts and long term changes","volume":"37","author":"Kuenzer","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/S0305-9006(03)00066-7","article-title":"Remote sensing technology for mapping and monitoring land-cover and land-use change","volume":"61","author":"Rogan","year":"2004","journal-title":"Progr. Plan"},{"key":"ref_34","unstructured":"Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2008). Remote Sensing and Image Interpretation, John Wiley & Sons, Inc. [6th ed]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (1999). Remote Sensing Digital Image Analysis. An Introduction, Springer. [3rd ed.].","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_36","unstructured":"Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann."},{"key":"ref_37","unstructured":"Roth, A., Huber, M., and Kosmann, D. (2004, January 12\u201323). Geocoding of TerraSAR-X Data. Istanbul, Turkey."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1080\/01431169608949077","article-title":"A spatially adaptive fast atmospheric correction algorithm","volume":"17","author":"Richter","year":"1996","journal-title":"Int. J. Remote Sens"},{"key":"ref_39","first-page":"1","article-title":"Framework for Change Detection Based on Image Objects","volume":"113","author":"Erasmi","year":"2005","journal-title":"Remote Sensing & GIS for Environmental Studies: Applications in Geography"},{"key":"ref_40","first-page":"311","article-title":"The comparison index: A tool for assessing the accuracy of image segmentation","volume":"9","author":"Lymburner","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf"},{"key":"ref_41","unstructured":"Neubert, M., and Herold, H. (2008, January 5\u20138). Assessment of Remote Sensing Image Segmentation Quality. Calgary, AB, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/S0734-189X(85)90153-7","article-title":"Image segmentation techniques","volume":"29","author":"Haralick","year":"1985","journal-title":"Comput. Vis. Graph. Image Process"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TGRS.1984.350619","article-title":"A physically-based transformation of thematic mapper data\u2014The TM Tasseled Cap","volume":"22","author":"Crist","year":"1984","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_46","unstructured":"Kauth, R.J., and Thomas, G.S. (July, January 29). The tasseled Cap\u2014A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. Lafayette, IN, USA."},{"key":"ref_47","unstructured":"Schowengerdt, R.A. (2007). Remote Sensing. Models and Methods for Image Processing, Elsevier Academic Press. [3 ed.]."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1080\/01431168508948511","article-title":"Standardized principal components","volume":"6","author":"Singh","year":"1985","journal-title":"Int. J. Remote Sens"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tso, B., and Mather, P.M. (2001). Classification Methods for Remotely Sensed Data, Taylor & Francis Ltd. [2nd ed].","DOI":"10.4324\/9780203303566"},{"key":"ref_50","unstructured":"Available online: http:\/\/qgis.osgeo.org (accessed on 15 March 2012)."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2007). Assessing the Accuracy of Remotely Sensed Data. Principles and Practices, CRC Press. [2 ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Introduction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Machine Learn"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1613\/jair.279","article-title":"Improved use of continuous attributes in C4.5","volume":"4","author":"Quinlan","year":"1996","journal-title":"J. Artif. Intell. Res"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/S0034-4257(97)00049-7","article-title":"Decision tree classification of land cover from remotely sensed data","volume":"61","author":"Friedl","year":"1997","journal-title":"Remote Sens. Environ"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1080\/01431169608949069","article-title":"Classification trees: An alternative to traditional land cover classifiers","volume":"17","author":"Hansen","year":"1996","journal-title":"Int. J. Remote Sens"},{"key":"ref_57","first-page":"304","article-title":"Generating Production Rules from Decision Trees","volume":"1","author":"Quinlan","year":"1987","journal-title":"Proceedings of the 10th International Joint Conference on Artificial Intelligence"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/36.752215","article-title":"Maximizing land cover classification accuracies produced by decision trees at continental to global scales","volume":"37","author":"Friedl","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Rokach, L., and Maimon, O.Z. (2008). Data Mining with Decision Trees: Theroy and Applications, World Scientific Pub. Co. Inc.","DOI":"10.1142\/9789812771728"},{"key":"ref_61","unstructured":"Kamber, M., Winstone, L., Gong, W., Cheng, S., and Han, J.W. (1997, January 7\u20138). Generalization and Decision Tree Induction: Efficient Classification in Data Mining. Birmingham, UK."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/S0034-4257(03)00132-9","article-title":"An assessment of the effectiveness of decision tree methods for land cover classification","volume":"86","author":"Pal","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_63","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_64","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psych. Meas"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_66","unstructured":"Schut, P. OpenGIS Standards\u2014Web Processing Service (WPS). Available online: http:\/\/www.opengeospatial.org\/standards\/wps (accessed on 15 March 2012)."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.3390\/rs3061234","article-title":"Post-disaster image processing for damage analysis using GENESI-DR, WPS and Grid computing","volume":"3","author":"Bielski","year":"2011","journal-title":"Remote Sens"},{"key":"ref_68","first-page":"39","article-title":"Becchi geospatial processing via internet on remote servers\u2014PyWPS","volume":"1","author":"Cepicky","year":"2007","journal-title":"OSGeo J"},{"key":"ref_69","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Harlan, J.C. (1974). Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation, NASA\/GSFC."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Miller","year":"2005","journal-title":"Remote Sens. Environ"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Klein, I., Gessner, U., and Kuenzer, C. (2012). Regional land cover mapping in Central Asia using MODIS time-series. Appl. Geogr.","DOI":"10.1016\/j.apgeog.2012.06.016"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ"},{"key":"ref_74","unstructured":"Reinartz, P. (2010, January 24\u201325). The CATENA Processing Chain\u2014Multi-Sensor Pre-processing: Orthorectification, Atmospheric Correction, Future Aspects. Toulouse, France."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learn"},{"key":"ref_76","unstructured":"Vapnik, V.N., and Chervonenkis, A.Y. (1974). Teoriya Raspoznavaniya Obrazov: Statisticheskie Problemy Obucheniya, Nauka, FizMatLit.. Izdat."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/4\/9\/2530\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:52:13Z","timestamp":1760219533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/4\/9\/2530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,9,7]]},"references-count":76,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2012,9]]}},"alternative-id":["rs4092530"],"URL":"https:\/\/doi.org\/10.3390\/rs4092530","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,9,7]]}}}