{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T08:00:05Z","timestamp":1770710405888,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European taxpayer via the European Union\u2019s Horizon 2020 research","doi-asserted-by":"publisher","award":["776681"],"award-info":[{"award-number":["776681"]}],"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>Extreme hydro-meteorological events become an increasing risk in high mountain environments, resulting in erosion events that endanger human infrastructure and life. Vegetation is known to be an important stabilizing factor; however, little is known about the spatial patterns of species composition in glacial forelands. This investigation aims to differentiate sparse vegetation in a steep alpine environment in the Austrian part of the Central Eastern Alps using low-cost multispectral cameras on an unmanned aerial vehicle (UAV). Highly resolved imagery from a consumer-grade UAV proved an appropriate basis for the SfM-based modeling of the research area as well as for vegetation mapping. Consideration must be paid to changing light conditions during data acquisition, especially with multispectral sensors. Different approaches were tested, and the best results were obtained using the Random Forest (RF) algorithm with the target class discrimination based on the RGB orthomosaic and the DEM as supplementary dataset. Our work contributes to the field of biogeomorphic research in proglacial areas as well as to the field of small-scale remote sensing and vegetation measuring. Our findings show that the occurrence of vegetation patches differs in terms of density and diversity within this relatively recent deglaciated environment.<\/jats:p>","DOI":"10.3390\/rs14194919","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Classifying Sparse Vegetation in a Proglacial Valley Using UAV Imagery and Random Forest Algorithm"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5129-3745","authenticated-orcid":false,"given":"Ulrich","family":"Zangerl","sequence":"first","affiliation":[{"name":"Department of Geography and Regional Research, University of Vienna, Universit\u00e4tsstra\u00dfe 7, 1010 Vienna, Austria"}]},{"given":"Stefan","family":"Haselberger","sequence":"additional","affiliation":[{"name":"Department of Geography and Regional Research, University of Vienna, Universit\u00e4tsstra\u00dfe 7, 1010 Vienna, Austria"}]},{"given":"Sabine","family":"Kraushaar","sequence":"additional","affiliation":[{"name":"Department of Geography and Regional Research, University of Vienna, Universit\u00e4tsstra\u00dfe 7, 1010 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,1]]},"reference":[{"key":"ref_1","unstructured":"Rudolf-Miklau, F., H\u00fcbl, J., and International Research Society Interpraevent (2009). Alpine Naturkatastrophen: Lawinen, Muren, Felsst\u00fcrze, Hochw\u00e4sser, Stocker."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/s11356-008-0075-3","article-title":"Climate and land-use changes affecting river sediment and brown trout in alpine countries\u2014A review","volume":"16","author":"Scheurer","year":"2009","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"855","DOI":"10.3170\/2008-8-18464","article-title":"Colonization processes on a central Alpine glacier foreland","volume":"19","author":"Erschbamer","year":"2008","journal-title":"J. Veg. Sci."},{"key":"ref_4","unstructured":"Erschbamer, B., and Koch, E.M. (2010). Kapitel 6. Pflanzliche Sukzessionen im Gletschervorfeld. Vegetation und Besiedlungsstrategien. Glaziale und Periglaziale Lebensr\u00e4ume im Raum Obergurgl, Innsbruck University Press."},{"key":"ref_5","unstructured":"Matthews, J.A. (1992). The Ecology of Recently-Deglaciated Terrain: A Geoecological Approach to Glacier Forelands, Cambridge University Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1016\/S0277-3791(02)00005-7","article-title":"Paraglacial geomorphology","volume":"21","author":"Ballantyne","year":"2002","journal-title":"Quat. Sci. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"J\u00f6nsson, P., Cai, Z., Melaas, E., Friedl, M.A., and Eklundh, L. (2018). A method for robust estimation of vegetation seasonality from Landsat and Sentinel-2 time series data. Remote Sens., 10.","DOI":"10.3390\/rs10040635"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/s43017-022-00298-5","article-title":"Optical vegetation indices for monitoring terrestrial ecosystems globally","volume":"3","author":"Zeng","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1080\/01431161.2019.1697004","article-title":"Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data","volume":"41","author":"Kuplich","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV remote sensing for urban vegetation mapping using random forest and texture analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","first-page":"5","article-title":"Tree species discrimination using RGB vegetation indices derived from UAV images","volume":"1","author":"Sadeghi","year":"2018","journal-title":"UAV Small Unmanned Aer. Syst. Env. Res"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ecolind.2012.09.014","article-title":"Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats","volume":"33","author":"Nagendra","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_14","first-page":"242","article-title":"Integrating optical satellite data and airborne laser scanning in habitat classification for wildlife management","volume":"38","author":"Nijland","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1002\/rse2.86","article-title":"Differentiating plant functional types using reflectance: Which traits make the difference?","volume":"5","author":"Kattenborn","year":"2019","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_16","unstructured":"Hammer, W. (1923). Geologische Spezialkarte der Republik \u00d6sterreich. Blatt Nauders, Geologische Reichsanstalt."},{"key":"ref_17","unstructured":"Vehling, L. (2016). Gravitative Massenbewegungen an Alpinen Felsh\u00e4ngen: Quantitative Bedeutung in der Sedimentkaskade proglazialer Geosysteme (Kaunertal, Tirol). [Ph.D. Thesis, Friedrich-Alexander-Universit\u00e4t Erlangen]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Efthymiadis, D., Jones, P.D., Briffa, K.R., B\u00f6hm, R., and Maugeri, M. (2007). Influence of large-scale atmospheric circulation on climate variability in the Greater Alpine Region of Europe. J. Geophys. Res., 112.","DOI":"10.1029\/2006JD008021"},{"key":"ref_19","unstructured":"DJI (2021, December 19). Phantom 4 Pro V2.0 Technische Daten. Available online: https:\/\/www.dji.com\/at\/phantom-4-pro-v2\/specs."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1093\/treephys\/7.1-2-3-4.33","article-title":"Exploring the relationship between reflectance red edge and chlorophyll content in slash pine","volume":"7","author":"Curran","year":"1990","journal-title":"Tree Physiol."},{"key":"ref_21","unstructured":"EUMeTrain (2021, December 17). Monitoring Vegetation from Space. Available online: http:\/\/www.eumetrain.org\/data\/3\/36\/navmenu.php?page=3.2.3."},{"key":"ref_22","unstructured":"MAPIR (2021, December 19). OCN Filter Improves Results Compared to RGN Filter. Available online: https:\/\/www.mapir.camera\/pages\/ocn-filter-improves-contrast-compared-to-rgn-filter."},{"key":"ref_23","unstructured":"MAPIR (2022, January 10). Survey3 Camera Datasheet. Available online: https:\/\/drive.google.com\/file\/d\/10gIzOjWVNoG9dvZwmAUG9fVqkEZHXEur\/view."},{"key":"ref_24","unstructured":"Noble, T., and Matthews, N. (2017). Unmanned Aircraft Systems Data Post Processing. Structure from Motion Photogrammetry, USGS National UAS Project Office."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9655","DOI":"10.3390\/rs70809655","article-title":"An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms","volume":"7","author":"Colditz","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105686","DOI":"10.1016\/j.compag.2020.105686","article-title":"Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data","volume":"177","author":"Daryaei","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","first-page":"42","article-title":"Urban growth and environmental impacts in jing-jin-ji, the yangtze, river delta and the pearl river delta","volume":"30","author":"Haas","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2014.04.010","article-title":"Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data","volume":"149","author":"Comber","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1109\/TGRS.2002.802476","article-title":"Multiple classifiers applied to multisource remote sensing data","volume":"40","author":"Briem","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1080\/01431161.2011.602651","article-title":"Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data","volume":"33","author":"Miao","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5166","DOI":"10.1080\/01431161.2013.788261","article-title":"Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests","volume":"34","author":"Guan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.isprsjprs.2012.01.005","article-title":"Oil spill feature selection and classification using decision tree forest on SAR image data","volume":"68","author":"Topouzelis","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","unstructured":"ESRI (2022, January 12). An Overview of the Segmentation and Classification Toolset. Available online: https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/spatial-analyst\/an-overview-of-the-segmentation-and-classification-tools.htm."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2010.09.007","article-title":"A fuzzy topology-based maximum likelihood classification","volume":"66","author":"Liu","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","unstructured":"Humboldt State University (2022, January 23). Accuracy Metrics. Introduction to Remote Sensing. Available online: http:\/\/gsp.humboldt.edu\/olm_2019\/courses\/GSP_216_Online\/lesson6-2\/metrics.html."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"de Castro, A.I., Shi, Y., Maja, J.M., and Pe\u00f1a, J.M. (2021). UAVs for vegetation monitoring: Overview and recent scientific contributions. Remote Sens., 13.","DOI":"10.3390\/rs13112139"},{"key":"ref_39","first-page":"6","article-title":"Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors","volume":"38","author":"Suarez","year":"2009","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"740","article-title":"Mapping mosaic virus in sugarcane based on hyperspectral images","volume":"10","author":"Imai","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Toma, A., and Sandric, I. (2022, January 23\u201327). Mapping Flooded Areas Using Sentinel-1 Radar Satellite Imagery Series through Machine Learning and Deep Learning Methods. Proceedings of the EGU General Assembly 2022, Vienna, Austria.","DOI":"10.5194\/egusphere-egu22-2947"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:45:14Z","timestamp":1760143514000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":43,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194919"],"URL":"https:\/\/doi.org\/10.3390\/rs14194919","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]}}}