{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T15:38:05Z","timestamp":1775057885610,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T00:00:00Z","timestamp":1646006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["FPA 275\/G\/GRO\/COPE\/17\/10042"],"award-info":[{"award-number":["FPA 275\/G\/GRO\/COPE\/17\/10042"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land use, land-use change and forestry (LULUCF) is a greenhouse gas inventory sector that evaluates greenhouse gas changes in the atmosphere from land use and land-use change. This study focuses on the development of a Sentinel-2 data classification according to the LULUCF requirements on the cloud-based platform Google Earth Engine (GEE). The methods are tested in selected larger territorial regions (two Czech NUTS 2 units) using data collected in 2018. The Random Forest method was used for classification. In terms of classification accuracy, a combination of these parameters was tested: The Number of Trees (NT), the Variables per Split (VPS) and the Bag Fraction (BF). A total of 450 combinations of different parameters were tested. The highest accuracy classification with an overall accuracy = 89.1% and Cohen\u2019s Kappa = 0.84 had the following combination: NT = 150, VPS = 3 and BF = 0.1. For classification purposes, a mosaic was created using the median method. The resulting mosaic consisted of all Sentinel-2 bands in 10 and 20 m spatial resolution. Altitude values derived from SRTM and NDVI variance values were also included in the classification. These added bands were the most significant in terms of Gini importance.<\/jats:p>","DOI":"10.3390\/rs14051189","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T20:11:57Z","timestamp":1646079117000},"page":"1189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Random Forest Classification of Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data\u2014A Case Study of Czechia"],"prefix":"10.3390","volume":"14","author":[{"given":"Jan","family":"Svoboda","sequence":"first","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 12843 Prague, Czechia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0307-9688","authenticated-orcid":false,"given":"P\u0159emysl","family":"\u0160tych","sequence":"additional","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 12843 Prague, Czechia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2430-1479","authenticated-orcid":false,"given":"Josef","family":"La\u0161tovi\u010dka","sequence":"additional","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 12843 Prague, Czechia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9738-938X","authenticated-orcid":false,"given":"Daniel","family":"Paluba","sequence":"additional","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 12843 Prague, Czechia"}]},{"given":"Natalia","family":"Kobliuk","sequence":"additional","affiliation":[{"name":"EO4Landscape Research Team, Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, 12843 Prague, Czechia"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Koomen, E., Stillwell, J., Bakema, A., and Scholten, H.J. (2007). Modelling Land-Use Change the GeoJournal Library, Springer.","DOI":"10.1007\/1-4020-5648-6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10980-009-9347-7","article-title":"Trajectories of land use change in Europe: A model-based exploration of rural futures","volume":"25","author":"Verburg","year":"2010","journal-title":"Landsc. Ecol."},{"key":"ref_3","first-page":"1","article-title":"Modelling Land Use, Land-Use Change, and Forestry in Climate Change: A Review of Major Approaches","volume":"46","author":"Michetti","year":"2012","journal-title":"SSRN Electron. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1146\/annurev-environ-090710-143732","article-title":"Global Forest Transition: Prospects for an End to Deforestation","volume":"36","author":"Meyfroidt","year":"2011","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsci.2014.03.004","article-title":"Reforming the EU approach to LULUCF and the climate policy framework","volume":"40","author":"Ellison","year":"2014","journal-title":"Environ. Sci. Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.forpol.2016.09.006","article-title":"From REDD+ forests to green landscapes? Analyzing the emerging integrated landscape approach discourse in the UNFCCC","volume":"73","author":"Nielsen","year":"2016","journal-title":"For. Policy Econ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.forpol.2017.10.003","article-title":"A Land Use and Resource Allocation (LURA) modeling system for projecting localized forest CO2 effects of alternative macroeconomic futures","volume":"87","author":"Latta","year":"2018","journal-title":"For. Policy Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3724\/SP.J.1248.2011.00178","article-title":"Analysis of LULUCF accounting rules after 2012","volume":"2","author":"Liu","year":"2011","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.rse.2012.05.019","article-title":"Mapping abandoned agriculture with multi-temporal MODIS satellite data","volume":"124","author":"Alcantara","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8650","DOI":"10.1073\/pnas.0912668107","article-title":"Quantification of global gross forest cover loss","volume":"107","author":"Hansen","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lastovicka, J., Svec, P., Paluba, D., Kobliuk, N., Svoboda, J., Hladky, R., and Stych, P. (2020). Sentinel-2 data in an evaluation of the impact of the disturbances on forest vegetation. Remote Sens., 12.","DOI":"10.3390\/rs12121914"},{"key":"ref_12","unstructured":"Lewinski, S., Malinowski, R., Rybicki, M., Gromny, E., Nowakowski, A., Jenerowicz, M., Krupi\u0144ski, M., Krupi\u0144ski, M., Kr\u00e4tzschmar, E., and Guenther, S. (2019, January 13\u201317). Automatic Land Cover Classification of Europe with Sentinel-2 Imagery. 2019. Poster. Proceedings of the Living Planet Symposium, MiCo\u2013Milano Congressi, Milan, Italy."},{"key":"ref_13","unstructured":"Herold, M., and di Gregorio, A. (2012). Evaluating land-cover legends using the UN land-cover classification system. Remote Sensing of Land Use and Land Cover: Principles and Applications, CRC Press."},{"key":"ref_14","unstructured":"Giri, C. (2012). Global Land-Cover Map Validation Experiences: Toward the Characterization of Quantitative Uncertainty, Taylor and Francis. JRC73563."},{"key":"ref_15","unstructured":"Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry, Institute for Global Environmental Strategies (IGES) for the IPCC."},{"key":"ref_16","unstructured":"Ho, T.K. (1995, January 14). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8703","DOI":"10.1080\/01431161.2018.1490976","article-title":"Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong","volume":"39","author":"Jin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.3390\/rs5062838","article-title":"The performance of random forests in an operational setting for large area sclerophyll forest classification","volume":"5","author":"Mellor","year":"2013","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"\u0160andera, J., and \u0160tych, P. (2020). Selecting relevant biological variables derived from Sentinel-2 data for mapping changes from grassland to arable land using random forest classifier. Land, 9.","DOI":"10.3390\/land9110420"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Micek, O., Feranec, J., and Stych, P. (2020). Land use\/land cover data of the urban atlas and the cadastre of real estate: An evaluation study in the Prague metropolitan region. Land, 9.","DOI":"10.3390\/land9050153"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.landusepol.2016.11.022","article-title":"Changes of urbanised landscape identified and assessed by the urban atlas data: Case study of Prague and Bratislava","volume":"61","author":"Feranec","year":"2017","journal-title":"Land Use Policy"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Manakos, I., Tomaszewska, M., Gkinis, I., Brovkina, O., Filchev, L., Genc, L., Gitas, I.Z., Halabuk, A., Inalpulat, M., and Irimescu, A. (2018). Comparison of global and continental land cover products for selected study areas in South Central and Eastern European Region. Remote Sens., 10.","DOI":"10.3390\/rs10121967"},{"key":"ref_25","unstructured":"Google Earth Engine (2022, February 16). Sentinel-2 MSI: MultiSpectral Instrument, Level-2A. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_S2_SR."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"223","article-title":"Kategorie pozemk\u016f v N\u00e1rodn\u00ed Inventarizaci LES\u016e \u010cesk\u00e9 Republiky","volume":"58","year":"2010","journal-title":"Acta Univ. Agric. Silvic. Mendel. Brun."},{"key":"ref_28","unstructured":"Sentinel Hub (2022, February 16). Sentinel Hub\u2019s Cloud Detector for Sentinel-2 Imagery. Available online: https:\/\/medium.com\/sentinel-hub\/cloud-masks-at-your-service-6e5b2cb2ce8a."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Puigdollers, D., Mateo-Garc\u00eda, G., and G\u00f3mez-Chova, L. (2021). Benchmarking deep learning models for cloud detection in Landsat-8 and Sentinel-2 images. Remote Sens., 13.","DOI":"10.3390\/rs13050992"},{"key":"ref_30","unstructured":"Google Earth Engine (2022, February 16). Sentinel-2 Cloud Masking with s2cloudles. Available online: https:\/\/developers.google.com\/earth-engine\/tutorials\/community\/sentinel-2-s2cloudless."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Noi Phan, T., Kuch, V., and Lehnert, L.W. (2020). Land cover classification using Google Earth Engine and random forest classifier-the role of image composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pratic\u00f2, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine learning classification of mediterranean forest habitats in Google Earth Engine based on seasonal Sentinel-2 time-series and input image composition optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2013.11.013","article-title":"Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers","volume":"88","author":"Mutanga","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and Interpreting Measures of Thematic Classification Accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Tassi, A., Gigante, D., Modica, G., Di Martino, L., and Vizzari, M. (2021). Pixel-vs. Object-based Landsat 8 data classification in Google Earth Engine using random forest: The case study of maiella national park. Remote Sens., 13.","DOI":"10.3390\/rs13122299"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2020.06.022","article-title":"Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine","volume":"167","author":"Phalke","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","first-page":"110","article-title":"Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud","volume":"81","author":"Oliphant","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","first-page":"100590","article-title":"Mapping and quantifying agricultural irrigation in heterogeneous landscapes using Google Earth Engine","volume":"23","author":"Zurqani","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m Landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","unstructured":"Kosztra, B., B\u00fcttner, G., Hazeu, G., and Arnold, S. (2017). Updated CLC Illustrated Nomenclature Guidelines, European Environment Agency."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1189\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:29:12Z","timestamp":1760135352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1189"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,28]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051189"],"URL":"https:\/\/doi.org\/10.3390\/rs14051189","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,28]]}}}