{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:46:40Z","timestamp":1766159200321,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T00:00:00Z","timestamp":1694044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technology Agency of the Czech Republic (Environment for Life program) project","award":["SS01010046"],"award-info":[{"award-number":["SS01010046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The monitoring of Natura 2000 habitats (Habitat Directive 92\/43\/EEC) is a key activity ensuring the sufficient protection of European biodiversity. Reporting on the status of Natura 2000 habitats is required every 6 years. Although field mapping is still an indispensable source of data on the status of Natura 2000 habitats, and very good field-based data exist in some countries, keeping the field-based habitat maps up to date can be an issue. Remote sensing techniques represent an excellent alternative. Here, we present a new method for detecting habitats that were likely misclassified during the field mapping or that have changed since then. The method identifies the possible habitat mapping errors as the so-called \u201cattribute outliers\u201d, i.e., outlying observations in the feature space of all relevant (spectral and other) characteristics of an individual habitat patch. We used the Czech Natura 2000 Habitat Layer as field-based habitat data. To prepare the feature space of habitat characteristics, we used a fusion of Sentinel-1 and Sentinel-2 satellite data along with a Digital Elevation Model. We compared outlier ratings using the robust Mahalanobis distance and Local Outlier Factor using three different thresholds (Tukey rule, histogram-based Scott\u2019s rule, and 95% quantiles in \u03c72 distribution). The Mahalanobis distance thresholded by the 95% \u03c72 quantile achieved the best results, and, because of its high specificity, appeared as a promising tool for identifying erroneously mapped or changed habitats. The presented method can, therefore, be used as a guide to target field updates of Natura 2000 habitat maps or for other habitat\/land cover mapping activities where the detection of misclassifications or changes is needed.<\/jats:p>","DOI":"10.3390\/rs15184409","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T10:09:50Z","timestamp":1694081390000},"page":"4409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Finding Misclassified Natura 2000 Habitats by Applying Outlier Detection to Sentinel-1 and Sentinel-2 Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4348-2815","authenticated-orcid":false,"given":"David","family":"Moravec","sequence":"first","affiliation":[{"name":"Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 165 00 Praha, Czech Republic"}]},{"given":"Vojt\u011bch","family":"Bart\u00e1k","sequence":"additional","affiliation":[{"name":"Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 165 00 Praha, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2480-1171","authenticated-orcid":false,"given":"Petra","family":"\u0160\u00edmov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 165 00 Praha, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1111\/avsc.12115","article-title":"Mapping the local variability of Natura 2000 habitats with remote sensing","volume":"17","author":"Feilhauer","year":"2014","journal-title":"Appl. Veg. Sci."},{"key":"ref_2","first-page":"1","article-title":"Earth observation for habitat mapping and biodiversity monitoring","volume":"37","author":"Lang","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.biocon.2014.12.006","article-title":"Remote sensing change detection for ecological monitoring in United States protected areas","volume":"182","author":"Willis","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_4","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_5","first-page":"100637","article-title":"Contribution of Sentinel-2 satellite images for habitat mapping of the Natura 2000 site \u2018Estuaire de la Loire\u2019 (France)","volume":"24","author":"Robin","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Marcinkowska-Ochtyra, A., Ochtyra, A., Raczko, E., and Kope\u0107, D. (2023). Natura 2000 Grassland Habitats Mapping Based on Spectro-Temporal Dimension of Sentinel-2 Images with Machine Learning. Remote Sens., 15.","DOI":"10.3390\/rs15051388"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.5209\/mbot.66535","article-title":"Contribution of free satellite time-series images to mapping plant communities in the Mediterranean Natura 2000 site: The example of Biguglia Pond in Corse (France)","volume":"41","author":"Rapinel","year":"2020","journal-title":"Mediterr. Bot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1111\/2041-210X.13036","article-title":"Landscape history confounds the ability of the NDVI to detect fine-scale variation in grassland communities","volume":"9","author":"Prentice","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1111\/j.1472-4642.2011.00761.x","article-title":"Benefits of hyperspectral remote sensing for tracking plant invasions","volume":"17","author":"He","year":"2011","journal-title":"Divers. Distrib."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.rse.2012.06.010","article-title":"Ordination and hyperspectral remote sensing approach to classify peatland biotopes along soil moisture and fertility gradients","volume":"124","author":"Middleton","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s13762-015-0859-1","article-title":"Digital and real-habitat modeling of Hipparchia statilinus based on hyper spectral remote sensing data","volume":"13","author":"Luft","year":"2016","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1002\/rse2.68","article-title":"Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status","volume":"4","author":"Schmidt","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.07.006","article-title":"Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery","volume":"216","author":"Erinjery","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3897\/natureconservation.24.21608","article-title":"Effectiveness of Natura 2000 system for habitat types protection: A case study from the Czech Republic","volume":"24","author":"Pechanec","year":"2018","journal-title":"Nat. Conserv."},{"key":"ref_15","unstructured":"H\u00e4rtel, H., Lon\u010d\u00e1kov\u00e1, J., and Ho\u0161ek, M. (2009). Mapov\u00e1n\u00ed Biotop\u016f v \u010cesk\u00e9 Republice. V\u00fdchodiska, V\u00fdsledky, Perspektivy, Agentura Ochrany P\u0159\u00edrody a Krajiny \u010cR."},{"key":"ref_16","first-page":"209","article-title":"Landscape classification of the Czech Republic based on the distribution of natural habitats","volume":"86","author":"Grulich","year":"2014","journal-title":"Preslia"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schneider, J., Ruda, A., Kalasov\u00e1, \u017d., and Paletto, A. (2020). The forest stakeholders\u2019 perception towards the NATURA 2000 network in the Czech Republic. Forests, 11.","DOI":"10.3390\/f11050491"},{"key":"ref_18","first-page":"41","article-title":"Natura 2000 Sites as an Asset for Rural Development: The German-Czech Ore Mountains Green Network Project","volume":"3","author":"Bastian","year":"2012","journal-title":"J. Landsc. Ecol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101310","DOI":"10.1016\/j.ecoinf.2021.101310","article-title":"Analysis on change detection techniques for remote sensing applications: A review","volume":"63","author":"Afaq","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Coops, N.C., Wulder, M.A., and White, J.C. (2007). Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches, CRC Press (Taylor and Francis).","DOI":"10.1201\/9781420005189"},{"key":"ref_21","unstructured":"Chytr\u00fd, M., Ku\u010dera, T., and Ko\u010d\u00ed, M. (2001). Katalog Biotop\u016f \u010cesk\u00e9 Republiky, AOPK."},{"key":"ref_22","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\u2019s 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_23","doi-asserted-by":"crossref","unstructured":"Persson, M., Lindberg, E., and Reese, H. (2018). Tree species classification with multi-temporal Sentinel-2 data. Remote Sens., 10.","DOI":"10.3390\/rs10111794"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1080\/10106049.2018.1474274","article-title":"Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data","volume":"34","author":"Otunga","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/36.158869","article-title":"Sensitivity of Microwave Measurements to Vegetation Biomass and Soil Moisture Content: A Case Study","volume":"30","author":"Ferrazzoli","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Paloscia, S., Macelloni, G., and Pampaloni, P. (1998, January 6\u201310). The relations between backscattering coefficient and biomass of narrow and wide leaf crops. Proceedings of the IGARSS \u201898. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174), Seattle, WA, USA.","DOI":"10.1109\/IGARSS.1998.702811"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/36.917914","article-title":"The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops","volume":"39","author":"Macelloni","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dobrini\u0107, D., Ga\u0161parovi\u0107, M., and Medak, D. (2021). Sentinel-1 and 2 time-series for vegetation mapping using random forest classification: A case study of northern croatia. Remote Sens., 13.","DOI":"10.3390\/rs13122321"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s41976-021-00064-z","article-title":"Crop Health Assessment Using Sentinel-1 SAR Time Series Data in a Part of Central India","volume":"4","author":"Kaushik","year":"2021","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1080\/014311698214848","article-title":"Review article Synergy in remote sensing-what\u2019s in a pixel?","volume":"19","author":"Cracknell","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","unstructured":"Kirches, G. (2022, December 20). Algorithm Theoretical Basis Document Sentinel 2 Global Mosaics Copernicus Sentinel-2 Global Mosaic (S2GM) within the Global Land Component of the Copernicus Land Service. Available online: https:\/\/usermanual.readthedocs.io\/en\/1.1.2\/_downloads\/5a2d961d53dea1eb1117ec73e4cbff09\/S2GM-SC2-ATBD-BC-v1.3.2.pdf."},{"key":"ref_32","unstructured":"Esri Inc (2022, December 20). ArcGIS Pro 2.7.0. Available online: https:\/\/www.esri.com\/."},{"key":"ref_33","unstructured":"(2022, December 20). QGIS 3.22.1. Available online: https:\/\/qgis.org\/."},{"key":"ref_34","unstructured":"Wang, A.J., Zamar, R., Alfiomarazziinsthospvdch, A.M., Yohai, V., Salibian-barrera, M., Maronna, R., Zivot, E., Rocke, D., Martin, D., and Maechler, M. (2023, January 22). robust: Port of the S+ \u201cRobust Library\u201d; R package version 0.7-1. Available online: https:\/\/cran.r-project.org\/package=robust."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1080\/00401706.1999.10485670","article-title":"A fast algorithm for the minimum covariance determinant estimator","volume":"41","author":"Rousseeuw","year":"1999","journal-title":"Technometrics"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., and Sander, J. (2000, January 15\u201318). LOF: Identifying Density-Based Local Outliers. Proceedings of the SIGMOD \u201800: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA.","DOI":"10.1145\/342009.335388"},{"key":"ref_37","unstructured":"Priv\u00e9, F. (2023, January 22). Utility Functions for Large-Scale Data; R package version 0.3.4. Available online: https:\/\/cran.r-project.org\/package=bigutilsr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5186","DOI":"10.1016\/j.csda.2007.11.008","article-title":"An adjusted boxplot for skewed distributions","volume":"52","author":"Hubert","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1093\/biomet\/66.3.605","article-title":"On optimal and data-based histograms","volume":"66","author":"Scott","year":"1979","journal-title":"Biometrika"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1002\/sim.9311","article-title":"Correcting for partial verification bias in diagnostic accuracy studies: A tutorial using R","volume":"41","author":"Arifin","year":"2022","journal-title":"Stat. Med."},{"key":"ref_41","unstructured":"Arifin, W.N. (2023, January 22). PVBcorrect: Partial Verification Bias Correction for Estimates of Accuracy Measures in Diagnostic Accuracy Studies; R package version 0.1.1. Available online: https:\/\/rdrr.io\/github\/wnarifin\/PVBcorrect\/man\/PVBcorrect.html."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kirschner, V., Franke, D., \u0158ez\u00e1\u010dov\u00e1, V., and Peltan, T. (2023). Poorer Regions Consume More Undeveloped but Less High-Quality Land Than Wealthier Regions\u2014A Case Study. Land, 12.","DOI":"10.3390\/land12010113"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shi, Z., Li, P., and Sun, Y. (2016, January 10\u201315). An outlier generation approach for one-class random forests: An example in one-class classification of remote sensing imagery. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730331"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"113591","DOI":"10.1016\/j.rse.2023.113591","article-title":"The relationship between spectral and plant diversity: Disentangling the influence of metrics and habitat types at the landscape scale","volume":"293","author":"Perrone","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhu, J., Wang, C., Lin, H., Long, J., Zhao, L., Fu, H., and Liu, Z. (2019). Forest growing stock volume estimation in subtropical mountain areas using PALSAR-2 L-Band PolSAR data. Forests, 10.","DOI":"10.3390\/f10030276"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nazarova, T., Martin, P., and Giuliani, G. (2020). Monitoring vegetation change in the presence of high cloud cover with sentinel-2 in a lowland tropical forest region in Brazil. Remote Sens., 12.","DOI":"10.3390\/rs12111829"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.3390\/rs5126481","article-title":"Seasonal composite landsat TM\/ETM+ Images using the medoid (a multi-dimensional median)","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4409\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:54Z","timestamp":1760129214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/18\/4409"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,7]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15184409"],"URL":"https:\/\/doi.org\/10.3390\/rs15184409","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,9,7]]}}}