{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T17:36:13Z","timestamp":1768412173670,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Faculty of Geography and Regional Studies, University of Warsaw","award":["SWIB 46\/2022"],"award-info":[{"award-number":["SWIB 46\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83\u20130.96), SVM (0.87\u20130.93), and lower results for XGBoost (0.69\u20130.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73\u20130.97 (RF) and 0.66\u20130.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one\u2013four percent points and Sentinel-1 data by 1%\u20139%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area.<\/jats:p>","DOI":"10.3390\/rs16081451","type":"journal-article","created":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T08:44:31Z","timestamp":1713516271000},"page":"1451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Combining Multitemporal Optical and Radar Satellite Data for Mapping the Tatra Mountains Non-Forest Plant Communities"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0984","authenticated-orcid":false,"given":"Marcin","family":"Kluczek","sequence":"first","affiliation":[{"name":"Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7882-5318","authenticated-orcid":false,"given":"Bogdan","family":"Zagajewski","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5133-3727","authenticated-orcid":false,"given":"Marlena","family":"Kycko","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e09922","DOI":"10.1111\/oik.09922","article-title":"Increases in functional diversity of mountain plant communities is mainly driven by species turnover under climate change","volume":"2023","author":"Schuchardt","year":"2023","journal-title":"Oikos"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/nyas.14104","article-title":"Effects of climate change on alpine plants and their pollinators","volume":"1469","author":"Inouye","year":"2020","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5614","DOI":"10.1111\/gcb.15820","article-title":"The tempo of greening in the European Alps: Spatial variations on a common theme","volume":"27","author":"Choler","year":"2021","journal-title":"Glob. Change Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1038\/s41586-018-0005-6","article-title":"Accelerated increase in plant species richness on mountain summits is linked to warming","volume":"556","author":"Steinbauer","year":"2018","journal-title":"Nature"},{"key":"ref_5","first-page":"e02140","article-title":"Climate change simulations in Alpine summer pastures suggest a disruption of current vegetation zonation","volume":"37","author":"Peringer","year":"2022","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4933","DOI":"10.5194\/hess-23-4933-2019","article-title":"Are the effects of vegetation and soil changes as important as climate change impacts on hydrological processes?","volume":"23","author":"Kabir","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.isprsjprs.2022.06.001","article-title":"A twenty-years remote sensing study reveals changes to alpine pastures under asymmetric climate warming","volume":"190","author":"Zheng","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"146813","DOI":"10.1016\/j.scitotenv.2021.146813","article-title":"Morphological and ecological responses of a managed coastal sand dune to experimental notches","volume":"782","author":"Castelle","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e12643","DOI":"10.1111\/avsc.12643","article-title":"About the link between biodiversity and spectral variation","volume":"25","author":"Fassnacht","year":"2022","journal-title":"Appl. Veg. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1080\/22797254.2020.1795727","article-title":"Contribution of SPOT-7 multi-temporal imagery for mapping wetland vegetation","volume":"53","author":"Fabre","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105004","DOI":"10.1088\/2515-7620\/ab4a85","article-title":"20 cm resolution mapping of tundra vegetation communities provides an ecological baseline for important research areas in a changing Arctic environment","volume":"1","author":"Greaves","year":"2019","journal-title":"Environ. Res. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Meng, B., Yang, Z., Yu, H., Qin, Y., Sun, Y., Zhang, J., Chen, J., Wang, Z., Zhang, W., and Li, M. (2021). Mapping of Kobresia pygmaea Community Based on Umanned Aerial Vehicle Technology and Gaofen Remote Sensing Data in Alpine Meadow Grassland: A Case Study in Eastern of Qinghai\u2013Tibetan Plateau. Remote Sens., 13.","DOI":"10.3390\/rs13132483"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112320","DOI":"10.1016\/j.rse.2021.112320","article-title":"Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series","volume":"256","author":"Sun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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_15","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_16","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1002\/rse2.253","article-title":"Large-scale and fine-grained mapping of heathland habitats using open-source remote sensing data","volume":"8","author":"Rozo","year":"2022","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_17","first-page":"102128","article-title":"Does environmental data increase the accuracy of land use and land cover classification?","volume":"91","author":"Zeferino","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","first-page":"101035","article-title":"The impact of selection of reference samples and DEM on the accuracy of land cover classification based on Sentinel-2 data","volume":"32","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_19","first-page":"102320","article-title":"Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia","volume":"99","author":"Xiao","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"547","DOI":"10.14358\/PERS.22-00123R2","article-title":"Leveraging NAIP Imagery for Accurate Large-Area Land Use\/land Cover Mapping: A Case Study in Central Texas","volume":"89","author":"Subedi","year":"2023","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109594","DOI":"10.1016\/j.ecolmodel.2021.109594","article-title":"Combined climate and regional mosquito habitat model based on machine learning","volume":"452","author":"Wieland","year":"2021","journal-title":"Ecol. Modell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17656","DOI":"10.1038\/s41598-019-53797-9","article-title":"Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery","volume":"9","author":"Kattenborn","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s00704-018-2489-2","article-title":"Vertical climatic belts in the Tatra Mountains in the light of current climate change","volume":"136","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27483","DOI":"10.1007\/s11356-022-24197-w","article-title":"Consequences of the accessibility of the mountain national parks in Poland","volume":"30","author":"Adach","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1659\/MRD-JOURNAL-D-15-00050.1","article-title":"Assessment of Hyperspectral Remote Sensing for Analyzing the Impact of Human Trampling on Alpine Swards","volume":"37","author":"Kycko","year":"2017","journal-title":"Mt. Res. Dev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11258-018-0898-z","article-title":"Impact of the alien plant Impatiens glandulifera on species diversity of invaded vegetation in the northern foothills of the Tatra Mountains, Central Europe","volume":"220","author":"Delimat","year":"2019","journal-title":"Plant. Ecol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1007\/s11756-023-01458-8","article-title":"Changes in the Nardus grasslands in the (Sub)Alpine Zone of Western Carpathians over the last decades","volume":"79","author":"Palaj","year":"2023","journal-title":"Biologia"},{"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":"Potin, P., Colin, O., Pinheiro, M., Rosich, B., O\u2019Connell, A., Ormston, T., Gratadour, J.-B., and Torres, R. (2022, January 17\u201322). Status and Evolution of the Sentinel-1 Mission. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884753"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1016\/S2095-3119(20)63329-9","article-title":"Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine","volume":"20","author":"Luo","year":"2021","journal-title":"J. Integr. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111199","DOI":"10.1016\/j.rse.2019.05.018","article-title":"Key issues in rigorous accuracy assessment of land cover products","volume":"231","author":"Stehman","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s10115-019-01335-4","article-title":"Parameter investigation of support vector machine classifier with kernel functions","volume":"61","author":"Tharwat","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_37","first-page":"101980","article-title":"Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification","volume":"85","author":"Macintyre","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","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_39","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_40","doi-asserted-by":"crossref","first-page":"5638","DOI":"10.1080\/01431161.2018.1504344","article-title":"Mapping semi-natural grassland communities using multi-temporal RapidEye remote sensing data","volume":"39","author":"Raab","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4549","DOI":"10.1038\/s41598-023-31705-6","article-title":"The utility of airborne hyperspectral and satellite multispectral images in identifying Natura 2000 non-forest habitats for conservation purposes","volume":"13","author":"Niedzielko","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_42","first-page":"102083","article-title":"Mapping Vegetation Communities Inside Wetlands Using Sentinel-2 Imagery in Ireland","volume":"88","author":"Bhatnagar","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"110113","DOI":"10.1016\/j.ecolind.2023.110113","article-title":"Continental-scale wetland mapping: A novel algorithm for detailed wetland types classification based on time series Sentinel-1\/2 images","volume":"148","author":"Peng","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/22797254.2017.1274573","article-title":"Classification of tundra vegetation in the Krkono\u0161e Mts. National park using APEX, AISA dual and Sentinel-2A data","volume":"50","author":"Zagajewski","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.1080\/01431161.2016.1274447","article-title":"Subalpine and Alpine Vegetation Classification based on Hyperspectral APEX and Simulated EnMAP images","volume":"38","author":"Zagajewski","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1007\/s10661-021-08956-9","article-title":"Application of remote sensing in alpine grasslands cover mapping of western Himalaya, Uttarakhand, India","volume":"193","author":"Pandey","year":"2021","journal-title":"Environ. Monit. Assess."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s12524-020-01253-x","article-title":"Vegetation Characterization at Community Level Using Sentinel-2 SatelliteData and Random Forest Classifier in Western Himalayan Foothills, Uttarakhand","volume":"49","author":"Mishra","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kluczek, M., Zagajewski, B., and Kycko, M. (2022). Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping. Remote Sens., 14.","DOI":"10.3390\/rs14051209"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kluczek, M., Zagajewski, B., and Zwijacz-Kozica, T. (2023). Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15030844"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., and Kycko, M. (2021). Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkono\u0161e\/Karkonosze Transboundary Biosphere Reserve. Remote Sens., 13.","DOI":"10.3390\/rs13132581"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zagajewski, B., Kluczek, M., Zdunek, K.B., and Holland, D. (2024). Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping. Remote Sens., 16.","DOI":"10.3390\/rs16040636"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1451\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:31:06Z","timestamp":1760106666000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1451"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,19]]},"references-count":51,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081451"],"URL":"https:\/\/doi.org\/10.3390\/rs16081451","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,19]]}}}