{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T06:05:17Z","timestamp":1782453917199,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007636","name":"Deutsche Bundesstiftung Umwelt","doi-asserted-by":"publisher","award":["34948\/02"],"award-info":[{"award-number":["34948\/02"]}],"id":[{"id":"10.13039\/100007636","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semi-natural grasslands contribute highly to biodiversity and other ecosystem services, but they are at risk by the spread of invasive plant species, which alter their habitat structure. Large area grassland monitoring can be a powerful tool to manage invaded ecosystems. Therefore, WorldView-3 multispectral sensor data was utilized to train multiple machine learning algorithms in an automatic machine learning workflow called \u2018H2O AutoML\u2019 to detect L. polyphyllus in a nature protection grassland ecosystem. Different degree of L. polyphyllus cover was collected on 3 \u00d7 3 m2 reference plots, and multispectral bands, indices, and texture features were used in a feature selection process to identify the most promising classification model and machine learning algorithm based on mean per class error, log loss, and AUC metrics. The best performance was achieved with a binary classification of lupin-free vs. fully invaded 3 \u00d7 3 m2 plot classification with a set of 7 features out of 763. The findings reveal that L. polyphyllus detection from WorldView-3 sensor data is limited to large dominant spots and not recommendable for lower plant coverage, especially single plant detection. Further research is needed to clarify if different phenological stages of L. polyphyllus as well as time series increase classification performance.<\/jats:p>","DOI":"10.3390\/rs13214333","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T23:52:35Z","timestamp":1635465155000},"page":"4333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Potentials and Limitations of WorldView-3 Data for the Detection of Invasive Lupinus polyphyllus Lindl. in Semi-Natural Grasslands"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8215-975X","authenticated-orcid":false,"given":"Damian","family":"Schulze-Br\u00fcninghoff","sequence":"first","affiliation":[{"name":"Grassland Science and Renewable Plant Resources, Universit\u00e4t Kassel, Steinstra\u00dfe 19, D-37213 Witzenhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2840-7086","authenticated-orcid":false,"given":"Michael","family":"Wachendorf","sequence":"additional","affiliation":[{"name":"Grassland Science and Renewable Plant Resources, Universit\u00e4t Kassel, Steinstra\u00dfe 19, D-37213 Witzenhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Astor","sequence":"additional","affiliation":[{"name":"Grassland Science and Renewable Plant Resources, Universit\u00e4t Kassel, Steinstra\u00dfe 19, D-37213 Witzenhausen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1111\/j.1654-1103.2012.01400.x","article-title":"Plant species richness: The world records","volume":"23","author":"Wilson","year":"2012","journal-title":"J. Veg. Sci."},{"key":"ref_2","unstructured":"Leadley, P., Pereira, H., Alkemade, R., Alkemade, R., JF, F.-M., Proenca, V., Scharlemann, J., and Walpole, M. (2010). Biodiversity Scenarios: Projections of 21st Century Change in Biodiversity and Associated Ecosystem Services, Secretariat of the Convention on Biological Diversity."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1007\/s10531-010-9850-9","article-title":"Towards an assessment of multiple ecosystem processes and services via functional traits","volume":"19","author":"Lavorel","year":"2010","journal-title":"Biodivers. Conserv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gross, J., Woodley, S., Welling, L.A., and Watson, J.E.M. (2016). Adapting to Climate Change: Guidance for Protected Area Managers and Planners. Best Practice Protected Area Guidelines Series No. 24, International Union for Conservation of Nature (IUCN).","DOI":"10.2305\/IUCN.CH.2017.PAG.24.en"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms14435","article-title":"No saturation in the accumulation of alien species worldwide","volume":"8","author":"Seebens","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pejchar, L., and Mooney, H. (2010). The Impact of Invasive Alien Species on Ecosystem Services and Human Well-being. Bioinvasions Glob. Ecol. Econ. Manag. Policy, 24.","DOI":"10.1016\/j.tree.2009.03.016"},{"key":"ref_7","first-page":"101","article-title":"Alien flora of Europe: Species diversity, temporal trends, geographical patterns and research needs","volume":"80","author":"Lambdon","year":"2008","journal-title":"Preslia"},{"key":"ref_8","unstructured":"Fremstad, E. (2021, June 29). NOBANIS\u2014Invasive Alien Species Fact Sheet\u2014Lupinus polyphyllus. Available online: \/www.nobanis.org."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1111\/jbi.12801","article-title":"Dominance has a biogeographical component: Do plants tend to exert stronger impacts in their invaded rather than native range?","volume":"44","author":"Hejda","year":"2017","journal-title":"J. Biogeogr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.biocon.2006.06.015","article-title":"Flora and lepidoptera fauna adversely affected by invasive Lupinus polyphyllus along road verges","volume":"133","author":"Valtonen","year":"2006","journal-title":"Biol. Conserv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/BF01048612","article-title":"Results and experience from amelioration trials in Scots pine (Pinus sylvestris L.) forests of Northeastern Bavaria","volume":"27","author":"Rehfuess","year":"1991","journal-title":"Fertil. Res."},{"key":"ref_12","first-page":"167","article-title":"Ausbreitungsvektoren und Ausbreitungswege der invasiven Stauden-Lupine im UNESCO Biosph\u00e4renreservat Rh\u00f6n","volume":"527","author":"Klinger","year":"2019","journal-title":"BfN-Skripten"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/rec.13311","article-title":"Seed bank offers potential for active restoration of mountain meadows","volume":"29","author":"Ludewig","year":"2021","journal-title":"Restor. Ecol."},{"key":"ref_14","first-page":"102211","article-title":"Assessing Worldview-3 multispectral imaging abilities to map the tree diversity in semi-arid parklands","volume":"93","author":"Lelong","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Choudhury, M.A.M., Marcheggiani, E., Galli, A., Modica, G., and Somers, B. (2021). Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data. Forests, 12.","DOI":"10.3390\/f12060692"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4443","DOI":"10.1109\/JSTARS.2020.3013663","article-title":"Leveraging High-Resolution Satellite Imagery and Gradient Boosting for Invasive Weed Mapping","volume":"13","author":"Shendryk","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","first-page":"1","article-title":"Timing is important: Unmanned aircraft vs. Satellite imagery in plant invasion monitoring","volume":"8","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ecoinf.2016.11.005","article-title":"Performance of one-class classifiers for invasive species mapping using airborne imaging spectroscopy","volume":"37","author":"Skowronek","year":"2017","journal-title":"Ecol. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.rse.2019.03.025","article-title":"UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data","volume":"227","author":"Kattenborn","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jensen, T., Hass, F.S., Akbar, M.S., Petersen, P.H., and Arsanjani, J.J. (2020). Employing machine learning for detection of invasive species using sentinel-2 and aviris data: The case of Kudzu in the United States. Sustainability, 12.","DOI":"10.3390\/su12093544"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1007\/s10530-019-02012-x","article-title":"Applying landscape structure analysis to assess the spatio-temporal distribution of an invasive legume in the Rh\u00f6n UNESCO Biosphere Reserve","volume":"21","author":"Klinger","year":"2019","journal-title":"Biol. Invasions"},{"key":"ref_22","first-page":"391","article-title":"Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images","volume":"88","author":"Wijesingha","year":"2020","journal-title":"PFG\u2014J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_23","first-page":"1","article-title":"Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review","volume":"12","author":"Strebel","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"McEvoy, J.F., Hall, G.P., and McDonald, P.G. (2016). Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: Disturbance effects and species recognition. PeerJ., 2016.","DOI":"10.7717\/peerj.1831"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lyons, M., Brandis, K., Callaghan, C., McCann, J., Mills, C., Ryall, S., and Kingsford, R. (2017). Bird interactions with drones, from individuals to large colonies. bioRxiv.","DOI":"10.1101\/109926"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Israel, M., and Reinhard, A. (2017, January 13\u201316). Detecting nests of lapwing birds with the aid of a small unmanned aerial vehicle with thermal camera. Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA.","DOI":"10.1109\/ICUAS.2017.7991393"},{"key":"ref_27","unstructured":"Volz, H. (2003). Ursachen und Auswirkungen der Ausbreitung von Lupinus polyphyllus Lindl. im Bergwiesen\u00f6kosystem der Rh\u00f6n und Ma\u00dfnahmen zu Seiner Regulierung, Justus-Liebig-Universit\u00e4t Gie\u00dfen."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s10530-020-02371-w","article-title":"Invasive legume affects species and functional composition of mountain meadow plant communities","volume":"23","author":"Hansen","year":"2020","journal-title":"Biol. Invasions"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1111\/2041-210X.13473","article-title":"Detecting plant species in the field with deep learning and drone technology","volume":"11","author":"James","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2562","DOI":"10.1002\/ece3.4919","article-title":"Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem","volume":"9","author":"Shiferaw","year":"2019","journal-title":"Ecol. Evol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Demarchi, L., Kania, A., Ciezkowski, W., Pi\u00f3rkowski, H., O\u015bwiecimska-Piasko, Z., and Chorma\u0144ski, J. (2020). Recursive feature elimination and random forest classification of natura 2000 grasslands in lowland river valleys of poland based on airborne hyperspectral and LiDAR data fusion. Remote Sens., 12.","DOI":"10.3390\/rs12111842"},{"key":"ref_32","unstructured":"(2017). Leica Geosystems Leica ScanStation P30\/P40, Leica Geosystems AG."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40965-017-0031-6","article-title":"Orfeo ToolBox: Open source processing of remote sensing images","volume":"2","author":"Grizonnet","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_35","unstructured":"OTB Development Team (2021, June 29). The Orfeo ToolBox Cookbook, a Guide for Non-Developers Updated for OTB-3.14. Available online: http:\/\/sossvr1.liberaintentio.com\/otb\/OTBCookBook.pdf."},{"key":"ref_36","first-page":"1","article-title":"The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures","volume":"15","author":"Wachendorf","year":"2020","journal-title":"PLoS ONE"},{"key":"ref_37","unstructured":"Genuer, R., Poggi, J.-M., and Tuleau-Malot, C. (2021, June 29). VSURF: Variable Selection Using Random Forests. Available online: https:\/\/cran.r-project.org\/package=VSURF."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2019.05.028","article-title":"A comparison of random forest variable selection methods for classification prediction modeling","volume":"134","author":"Speiser","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_39","unstructured":"LeDell, E., and Poirier, S. (2020, January 12\u201318). H2O AutoML: Scalable Automatic Machine Learning. Proceedings of the 7th ICML Workshop on Automated Machine Learning, Vienna, Austria."},{"key":"ref_40","unstructured":"(2021, June 29). H2O.ai R Interface for H2O. Available online: https:\/\/github.com\/h2oai\/h2o-3."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1142\/S0129065797000227","article-title":"Data mining of inputs: Analysing magnitude and functional measures","volume":"8","author":"Gedeon","year":"1997","journal-title":"Int. J. Neural Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/13658816.2020.1808897","article-title":"A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping","volume":"35","author":"Fang","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Vasilakos, C., Kavroudakis, D., and Georganta, A. (2020). Machine learning classification ensemble of multitemporal Sentinel-2 images: The case of a mixed mediterranean ecosystem. Remote Sens., 12.","DOI":"10.3390\/rs12122005"},{"key":"ref_44","first-page":"38","article-title":"Predicting tropical dry forest successional attributes from space: Is the key hidden in image texture?","volume":"7","author":"Meave","year":"2012","journal-title":"PLoS ONE"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11273-020-09719-y","article-title":"Application of airborne hyperspectral data for mapping of invasive alien Spiraea tomentosa L.: A serious threat to peat bog plant communities","volume":"28","author":"Niedzielko","year":"2020","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Moeckel, T., Safari, H., Reddersen, B., Fricke, T., and Wachendorf, M. (2017). Fusion of ultrasonic and spectral sensor data for improving the estimation of biomass in grasslands with heterogeneous sward structure. Remote Sens., 9.","DOI":"10.3390\/rs9010098"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"301","DOI":"10.5194\/jsss-5-301-2016","article-title":"Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system","volume":"5","author":"Safari","year":"2016","journal-title":"J. Sensors Sens. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"F\u00f6rster, M., Schmidt, T., Wolf, R., Kleinschmit, B., Fassnacht, F.E., Cabezas, J., and Kattenborn, T. (2017, January 27\u201329). Detecting the spread of invasive species in central Chile with a Sentinel-2 time-series. Proceedings of the 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Bruges, Belgium.","DOI":"10.1109\/Multi-Temp.2017.8035216"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/JSTARS.2012.2203796","article-title":"Invasive Species Mapping in Hawaiian Rainforests Using Multi-Temporal Hyperion Spaceborne Imaging Spectroscopy","volume":"6","author":"Somers","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"370","DOI":"10.2307\/2344614","article-title":"Generalized Linear Models","volume":"135","author":"Nelder","year":"1972","journal-title":"J. R. Stat. Soc. Ser. A"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4333\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:22:06Z","timestamp":1760167326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":54,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214333"],"URL":"https:\/\/doi.org\/10.3390\/rs13214333","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,28]]}}}