{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T21:16:22Z","timestamp":1773436582071,"version":"3.50.1"},"reference-count":102,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,11]],"date-time":"2020-10-11T00:00:00Z","timestamp":1602374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Commission LIFE Integrated Project, LIFE-IP 4 NATURA","award":["LIFE 16 IPE\/G\/000002"],"award-info":[{"award-number":["LIFE 16 IPE\/G\/000002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land-Use\/Land-Cover (LULC) products are a common source of information and a key input for spatially explicit models of ecosystem service (ES) supply and demand. Global, continental, and regional, readily available, and free land-cover products generated through Earth Observation (EO) data, can be potentially used as relevant to ES mapping and assessment processes from regional to national scales. However, several limitations exist in these products, highlighting the need for timely land-cover extraction on demand, that could replace or complement existing products. This study focuses on the development of a classification workflow for fine-scale, object-based land cover mapping, employed on terrestrial ES mapping, within the Greek terrestrial territory. The processing was implemented in the Google Earth Engine cloud computing environment using 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Furthermore, the relevance of different training data extraction strategies and temporal EO information for increasing the classification accuracy was also evaluated. The different classification schemes demonstrated differences in overall accuracy ranging from 0.88% to 4.94% with the most accurate classification scheme being the manual sampling\/monthly feature classification achieving a 79.55% overall accuracy. The classification results suggest that existing LULC data must be cautiously considered for automated extraction of training samples, in the case of new supervised land cover classifications aiming also to discern complex vegetation classes. The code used in this study is available on GitHub and runs on the Google Earth Engine web platform.<\/jats:p>","DOI":"10.3390\/rs12203303","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["National Scale Land Cover Classification for Ecosystem Services Mapping and Assessment, Using Multitemporal Copernicus EO Data and Google Earth Engine"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4023-9414","authenticated-orcid":false,"given":"Natalia","family":"Verde","sequence":"first","affiliation":[{"name":"School of Rural and Surveying Engineering, Laboratory of Photogrammetry and Remote Sensing Unit (PERS lab), The Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"},{"name":"Forest Remote Sensing and Geospatial Analysis Laboratory, Democritus University of Thrace, 68200 Orestiada, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0647-3445","authenticated-orcid":false,"given":"Ioannis P.","family":"Kokkoris","sequence":"additional","affiliation":[{"name":"Department of Biology, Laboratory of Botany, University of Patras, 26504 Patras, Greece"}]},{"given":"Charalampos","family":"Georgiadis","sequence":"additional","affiliation":[{"name":"School of Rural and Surveying Engineering, Laboratory of Photogrammetry and Remote Sensing Unit (PERS lab), The Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"given":"Dimitris","family":"Kaimaris","sequence":"additional","affiliation":[{"name":"School of Rural and Surveying Engineering, Laboratory of Photogrammetry and Remote Sensing Unit (PERS lab), The Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0699-9954","authenticated-orcid":false,"given":"Panayotis","family":"Dimopoulos","sequence":"additional","affiliation":[{"name":"Department of Biology, Laboratory of Botany, University of Patras, 26504 Patras, Greece"}]},{"given":"Ioannis","family":"Mitsopoulos","sequence":"additional","affiliation":[{"name":"Ministry of Environment &amp; Energy, Directorate of Biodiversity and Natural Environment Management, 11526 Athens, Greece"}]},{"given":"Giorgos","family":"Mallinis","sequence":"additional","affiliation":[{"name":"School of Rural and Surveying Engineering, Laboratory of Photogrammetry and Remote Sensing Unit (PERS lab), The Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"},{"name":"Forest Remote Sensing and Geospatial Analysis Laboratory, Democritus University of Thrace, 68200 Orestiada, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1080\/15481603.2015.1033809","article-title":"Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: A review","volume":"52","author":"Andrew","year":"2015","journal-title":"GIScience Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9483","DOI":"10.1073\/pnas.0706559105","article-title":"An operational model for mainstreaming ecosystem services for implementation","volume":"105","author":"Cowling","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.ecoser.2012.06.004","article-title":"Mapping ecosystem services for policy support and decision making in the European Union","volume":"1","author":"Maes","year":"2012","journal-title":"Ecosyst. Serv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kokkoris, I., Mallinis, G., Bekri, E., Vlami, V., Zogaris, S., Chrysafis, I., Mitsopoulos, I., and Dimopoulos, P. (2020). National Set of MAES Indicators in Greece: Ecosystem Services and Management Implications. Forests, 11.","DOI":"10.3390\/f11050595"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.tree.2017.03.003","article-title":"Priorities to Advance Monitoring of Ecosystem Services Using Earth Observation","volume":"32","author":"Cord","year":"2017","journal-title":"Trends Ecol. Evol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e112601","DOI":"10.1371\/journal.pone.0112601","article-title":"On the effects of scale for ecosystem services mapping","volume":"9","author":"Weibel","year":"2014","journal-title":"PLoS ONE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ecolind.2011.06.019","article-title":"Mapping ecosystem service supply, demand and budgets","volume":"21","author":"Burkhard","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Burkhard, B., and Maes, J. (2017). Mapping Ecosystem Services, Pensoft Publishers.","DOI":"10.3897\/ab.e12837"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ecolmodel.2014.08.024","article-title":"\u201cThe Matrix Reloaded\u201d: A review of expert knowledge use for mapping ecosystem services","volume":"295","author":"Jacobs","year":"2015","journal-title":"Ecol. Modell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mallinis, G., and Georgiadis, C. (2019). Editorial of Special Issue \u201cRemote Sensing for Land Cover\/Land Use Mapping at Local and Regional Scales\u201d. Remote Sens., 11.","DOI":"10.3390\/rs11192202"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.ecolind.2015.01.007","article-title":"Remote sensing of ecosystem services: A systematic review","volume":"52","author":"Atkinson","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kukawska, E., Lewinski, S., Krupinski, M., Malinowski, R., Nowakowski, A., Rybicki, M., and Kotarba, A. (2017, January 27\u201329). Multitemporal Sentinel-2 data-remarks and observations. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Brugge, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035212"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/978-94-007-7969-3_5","article-title":"CORINE land cover and land cover change products","volume":"18","year":"2014","journal-title":"Remote Sens. Digit. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5309","DOI":"10.1080\/01431161.2015.1093195","article-title":"An overview of 21 global and 43 regional land-cover mapping products","volume":"36","author":"Grekousis","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Leinenkugel, P., Deck, R., Huth, J., Ottinger, M., and Mack, B. (2019). The Potential of Open Geodata for Automated Large-Scale Land Use and Land Cover Classification. Remote Sens., 11.","DOI":"10.3390\/rs11192249"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.rse.2018.12.001","article-title":"Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey","volume":"221","author":"Pflugmacher","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1080\/17445647.2015.1123656","article-title":"Land cover of Greece, 2010: A semi-automated classification using random forests","volume":"12","author":"Gounaridis","year":"2016","journal-title":"J. Maps"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2019). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.rse.2014.09.034","article-title":"SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system","volume":"156","author":"Lehmann","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1080\/22797254.2017.1401909","article-title":"Multi-data approach for crop classification using multitemporal, dual-polarimetric TerraSAR-X data, and official geodata","volume":"51","author":"Waldhoff","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2019.09.016","article-title":"Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture","volume":"158","author":"Ienco","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Van Tricht, K., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic use of radar sentinel-1 and optical sentinel-2 imagery for crop mapping: A case study for Belgium. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0066.v1"},{"key":"ref_27","first-page":"102009","article-title":"Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa","volume":"86","author":"Slagter","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Hay, G.J., and Castilla, G. (2008). Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. Object-Based Image Analysis, Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-540-77058-9_4"},{"key":"ref_30","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. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_32","unstructured":"(2020, October 09). EEA Technical Specifications for Implementation of a New land-Monitoring Concept Based on EAGLE. Public Consultation document for CLC+ Core. Available online: https:\/\/land.copernicus.eu\/user-corner\/technical-library\/clc-core-consultations-for-the-technical-specifications."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9010095"},{"key":"ref_34","unstructured":"European Commision (2011). Our Life Insurance, Our Natural Capital: An EU Biodiversity Strategy to 2020. Communication from the Commission to the European Parliament, the Council, the Economic and Social Committee and the Committee of the Regions, European Commision."},{"key":"ref_35","unstructured":"Maes, J., Teller, A., Erhard, M., Liquete, C., Braat, L., Berry, P., Egoh, B., Puydarrieux, P., Fiorina, C., and Santos, F. (2013). Mapping and Assessment of Ecosystems and their Services, Publications office of the European Union."},{"key":"ref_36","unstructured":"Schuler, M., Stucki, E., Roque, O., and Perlik, M. (2004). Mountain Areas in Europe: Analysis of Mountain Areas in EU Member States, Acceding and Other European Countries, European Commission."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.214","article-title":"Present and future k\u00f6ppen-geiger climate classification maps at 1-km resolution","volume":"5","author":"Beck","year":"2018","journal-title":"Sci. Data"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kokkoris, I., Dimopoulos, P., Xystrakis, F., and Tsiripidis, I. (2018). National scale ecosystem condition assessment with emphasis on forest types in Greece. One Ecosyst., 3.","DOI":"10.3897\/oneeco.3.e25434"},{"key":"ref_39","unstructured":"Weiss, M., and Banko, G. (2018). Ecosystem Type Map v3.1\u2014Terrestrial and marine ecosystems, EEA-European Topic Centre on Biological Diversity."},{"key":"ref_40","unstructured":"(2020, October 09). EEA Mapping Europe\u2019s Ecosystems. Available online: https:\/\/www.eea.europa.eu\/themes\/biodiversity\/mapping-europes-ecosystems."},{"key":"ref_41","unstructured":"(2020, October 09). LIFE-IP 4 NATURA GitHub Page. Available online: https:\/\/github.com\/n-verde\/LIFE-IP_4_NATURA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2017.04.016","article-title":"Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species","volume":"129","author":"Shoko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4038","DOI":"10.1109\/JSTARS.2019.2938388","article-title":"Derivation of Tasseled Cap Transformation Coefficients for Sentinel-2 MSI At-Sensor Reflectance Data","volume":"12","author":"Shi","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"d\u2019Andrimont, R., Lemoine, G., and van der Velde, M. (2018). Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations. Remote Sens., 10.","DOI":"10.3390\/rs10081300"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1080\/2150704X.2016.1249299","article-title":"A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data","volume":"8","author":"Mack","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Griffiths, P., Nendel, C., Pickert, J., and Hostert, P. (2019). Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series. Remote Sens. Environ., 1\u201312.","DOI":"10.1016\/j.rse.2019.03.017"},{"key":"ref_48","first-page":"102065","article-title":"Spatial and semantic effects of LUCAS samples on fully automated land use\/land cover classification in high-resolution Sentinel-2 data","volume":"88","author":"Weigand","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Evans, M.J., and Malcom, J.W. (2020). Automated Change Detection Methods for Satellite Data that can Improve Conservation Implementation. bioRxiv.","DOI":"10.1101\/611459"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., M\u00fcller-Wilm, U., and Gascon, F. (2017, January 11\u201314). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland.","DOI":"10.1117\/12.2278218"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., and Gao, Y. (2019). Automatic land-cover mapping using landsat time-series data based on google earth engine. Remote Sens., 11.","DOI":"10.3390\/rs11243023"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Carrasco, L., O\u2019Neil, A.W., Daniel Morton, R., and Rowland, C.S. (2019). Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11030288"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Achanta, R., and S\u00fcsstrunk, S. (2017, January 21\u201326). Superpixels and polygons using simple non-iterative clustering. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/07038992.2019.1711366","article-title":"Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform","volume":"46","author":"Mahdianpari","year":"2020","journal-title":"Can. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tu, Y., Chen, B., Zhang, T., and Xu, B. (2020). Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sens., 12.","DOI":"10.3390\/rs12071058"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/20964471.2019.1690404","article-title":"A generalized supervised classification scheme to produce provincial wetland inventory maps: An application of Google Earth Engine for big geo data processing","volume":"3","author":"Amani","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lefebvre, A., Sannier, C., and Corpetti, T. (2016). Monitoring urban areas with Sentinel-2A data: Application to the update of the Copernicus High Resolution Layer Imperviousness Degree. Remote Sens., 8.","DOI":"10.3390\/rs8070606"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/JSTARS.2009.2021959","article-title":"The Impact of Phenological Variation on Texture Measures of Remotely Sensed Imagery","volume":"2","author":"Culbert","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.rse.2016.01.017","article-title":"Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data","volume":"176","author":"Puletti","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_62","first-page":"1","article-title":"Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal","volume":"6049","author":"Pandit","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Morin, D., Planells, M., Guyon, D., Villard, L., Mermoz, S., Bouvet, A., Thevenon, H., Dejoux, J.-F., Le Toan, T., and Dedieu, G. (2019). Estimation and Mapping of Forest Structure Parameters from Open Access Satellite Images: Development of a Generic Method with a Study Case on Coniferous Plantation. Remote Sens., 11.","DOI":"10.3390\/rs11111275"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.isprsjprs.2012.05.012","article-title":"Characterizing land-use classes in remote sensing imagery by shape metrics","volume":"72","author":"Jiao","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","unstructured":"Garc\u00eda, J.C., Antonio, J., and Garz\u00f3n, A. (2015). EU-DEM Upgrade Documentation EEA User Manual, Indra Systems S.A."},{"key":"ref_66","unstructured":"Roberts, D.W., and Cooper, S. (1987, January 17\u201319). V Concepts and techniques of vegetation mapping. Proceedings of the Land Classifications Based on Vegetation: Applications for Resource Management, Moscow, ID, USA."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2032s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/rs2102369","article-title":"Applicability of Green-Red Vegetation Index for remote sensing of vegetation phenology","volume":"2","author":"Motohka","year":"2010","journal-title":"Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1080\/2150704X.2014.933276","article-title":"Development of a nationwide approach for large scale estimation of green roof retrofitting areas and roof-top solar energy potential using VHR natural colour orthoimagery and DSM data over Thessaloniki, Greece","volume":"5","author":"Mallinis","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"14227","DOI":"10.3390\/rs71014227","article-title":"The potential of EnMAP and sentinel-2 data for detecting drought stress phenomena in deciduous forest communities","volume":"7","author":"Dotzler","year":"2015","journal-title":"Remote Sens."},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2012.09.009","article-title":"BCI: A biophysical composition index for remote sensing of urban environments","volume":"127","author":"Deng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/JSTARS.2008.2002869","article-title":"A robust built-up area presence index by anisotropic rotation-invariant textural measure","volume":"1","author":"Pesaresi","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_75","unstructured":"(2020, October 09). EEA Linkages of Species and Habitat Types to MAES Ecosystems. Available online: https:\/\/www.eea.europa.eu\/data-and-maps\/data\/linkages-of-species-and-habitat."},{"key":"ref_76","unstructured":"Kosztra, B., B\u00fcttner, G., Hazeu, G., and Arnold, S. (2017). Updated CLC Illustrated Nomenclature Guidelines, European Environment Agency."},{"key":"ref_77","unstructured":"(2020, October 09). EEA NOMENCLATURE and MAPPING GUIDELINE. Copernicus Land Monitoring Service Local Component: Natura 2000 Mapping. Available online: https:\/\/land.copernicus.eu\/local\/natura\/resolveuid\/aa66ae0cd4fe4270bd5d354f145498ee."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.3390\/rs6053965","article-title":"Automated training sample extraction for global land cover mapping","volume":"6","author":"Radoux","year":"2014","journal-title":"Remote Sens."},{"key":"ref_79","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_80","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_81","unstructured":"(2020, October 09). R Core Team R: A Language and Environment for Statistical Computing. Available online: https:\/\/www.r-project.org."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., and Rigol-Sanchez, J.P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens.","DOI":"10.1016\/j.isprsjprs.2011.11.002"},{"key":"ref_83","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_84","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_85","first-page":"499","article-title":"Introducing new indices for accuracy evaluation of classified images representing semi-natural woodland environments","volume":"67","author":"Koukoulas","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4665","DOI":"10.1109\/JSTARS.2015.2461556","article-title":"A Scalable Geospatial Web Service for Near Real-Time, High-Resolution Land Cover Mapping","volume":"8","author":"Karantzalos","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Karakizi, C., Karantzalos, K., Vakalopoulou, M., and Antoniou, G. (2018). Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover. Remote Sens., 10.","DOI":"10.3390\/rs10081214"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Pereira-Pires, J.E., Aubard, V., Ribeiro, R.A., Fonseca, J.M., Silva, J.M.N., and Mora, A. (2020). Semi-automatic methodology for fire break maintenance operations detection with sentinel-2 imagery and artificial neural network. Remote Sens., 12.","DOI":"10.3390\/rs12060909"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jenvman.2014.07.047","article-title":"Development of 2010 national land cover database for the Nepal","volume":"148","author":"Uddin","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Haest, B., Vanden Borre, J., Spanhove, T., Thoonen, G., Delalieux, S., Kooistra, L., M\u00fccher, C.A., Paelinckx, D., Scheunders, P., and Kempeneers, P. (2017). Habitat mapping and quality assessment of NATURA 2000 heathland using airborne imaging spectroscopy. Remote Sens., 9.","DOI":"10.3390\/rs9030266"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.jnc.2010.07.003","article-title":"Integrating remote sensing in Natura 2000 habitat monitoring: Prospects on the way forward","volume":"19","author":"Paelinckx","year":"2011","journal-title":"J. Nat. Conserv."},{"key":"ref_92","unstructured":"Smith, G., Pennec, A., Sannier, C., and Dufourmont, H. (2018). HRL Imperviousness Degree 2015 Validation Report, European Environment Agency."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.rse.2017.09.035","article-title":"Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites","volume":"204","author":"Heydari","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.foreco.2017.10.011","article-title":"Factors affecting forest dynamics in the Iberian Peninsula from 1987 to 2012. The role of topography and drought","volume":"406","author":"Ninyerola","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_95","first-page":"887","article-title":"Definition and application of expert knowledge on vegetation pattern, phenology, and seasonality for habitat mapping, as exemplified in a Mediterranean coastal site","volume":"151","author":"Tomaselli","year":"2017","journal-title":"Plant Biosyst. Int. J. Deal. Asp. Plant Biol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s10980-011-9580-8","article-title":"Topography-mediated controls on local vegetation phenology estimated from MODIS vegetation index","volume":"26","author":"Hwang","year":"2011","journal-title":"Landsc. Ecol."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez-Landa, A., Algeet-Abarquero, N., Fern\u00e1ndez-Moya, J., Guill\u00e9n-Climent, M.L., Pedroni, L., Garc\u00eda, F., Espejo, A., Villegas, J.F., Marchamalo, M., and Bonatti, J. (2016). An operational framework for land cover classification in the context of REDD+ mechanisms: A case study from costa rica. Remote Sens., 8.","DOI":"10.3390\/rs8070593"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Robinson, C., Saatchi, S., Clark, D., Astaiza, J.H., Hubel, A.F., and Gillespie, T.W. (2018). Topography and three-dimensional structure can estimate tree diversity along a tropical elevational gradient in Costa Rica. Remote Sens., 10.","DOI":"10.3390\/rs10040629"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Siachalou, S., Mallinis, G., and Tsakiri-Strati, M. (2017). Analysis of Time-Series Spectral Index Data to Enhance Crop Identification Over a Mediterranean Rural Landscape. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2017.2719124"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"7910","DOI":"10.1080\/01431161.2014.978039","article-title":"Usability of noise-free daily satellite-observed green\u2013red vegetation index values for monitoring ecosystem changes in Borneo","volume":"35","author":"Nagai","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_102","first-page":"49","article-title":"Frequency distribution signatures and classification of within-object pixels","volume":"15","author":"Stow","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3303\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:19:22Z","timestamp":1760177962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,11]]},"references-count":102,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12203303"],"URL":"https:\/\/doi.org\/10.3390\/rs12203303","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,11]]}}}