{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T01:26:19Z","timestamp":1768008379576,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,2,12]],"date-time":"2015-02-12T00:00:00Z","timestamp":1423699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data  in this complex situation. To reach the highest classification accuracy, we tested  traditional image classifiers (maximum likelihood classifier\u2014MLC), machine learning algorithms (support vector machine\u2014SVM, random forest\u2014RF) and feature extraction  (minimum noise fraction (MNF)-transformation) on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400\u20131000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2\u201315 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%), but the use of a smaller training dataset (10 training pixels) significantly reduced the accuracy of classification (52.56%). Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC. SVM was not sensitive in the training sample size, which makes it an adequate tool when only a limited number of training pixels are available for some classes.<\/jats:p>","DOI":"10.3390\/rs70202046","type":"journal-article","created":{"date-parts":[[2015,2,12]],"date-time":"2015-02-12T12:08:24Z","timestamp":1423742904000},"page":"2046-2066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery"],"prefix":"10.3390","volume":"7","author":[{"given":"P\u00e9ter","family":"Burai","sequence":"first","affiliation":[{"name":"Research Institute of Remote Sensing and Rural Development, Karoly Robert College,  H-3200 Gy\u00f6ngy\u00f6s, M\u00e1trai \u00fat 36, Hungary"}]},{"given":"Bal\u00e1zs","family":"De\u00e1k","sequence":"additional","affiliation":[{"name":"MTA-DE Biodiversity and Ecosystem Services Research Group, P.O. Box 71,  H-4010 Debrecen, Hungary"}]},{"given":"Orsolya","family":"Valk\u00f3","sequence":"additional","affiliation":[{"name":"MTA-DE Biodiversity and Ecosystem Services Research Group, P.O. Box 71,  H-4010 Debrecen, Hungary"}]},{"given":"Tam\u00e1s","family":"Tomor","sequence":"additional","affiliation":[{"name":"Research Institute of Remote Sensing and Rural Development, Karoly Robert College,  H-3200 Gy\u00f6ngy\u00f6s, M\u00e1trai \u00fat 36, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2015,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.tree.2011.08.006","article-title":"Biodiversity and ecosystem services: A multilayered relationship","volume":"27","author":"Mace","year":"2012","journal-title":"Trends Ecol. Evol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.1007\/s10531-008-9395-3","article-title":"Habitat monitoring in Europe: A description of current practices","volume":"17","author":"Lengyel","year":"2008","journal-title":"Biodivers. Conserv."},{"key":"ref_3","first-page":"1","article-title":"Mapping aquatic vegetation of the Rakamaz-Tiszanagyfalui Nagy-Morotva using hyperspectral imagery","volume":"4","author":"Burai","year":"2010","journal-title":"Acta Geogr. Debr. Landsc. Environ. Ser."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3390\/rs6010209","article-title":"New tree cover percentage map in Eurasia at 500 m resolution using MODIS data","volume":"6","author":"Kobayashi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"716","DOI":"10.3390\/rs6010716","article-title":"Empirical modelling of vegetation abundance from airborne hyperspectral data for upland Peatland restoration monitoring","volume":"6","author":"Cole","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2014.02.013","article-title":"Regional-scale mapping of tree cover, height and main phenological tree types using airborne laser scanning data","volume":"147","author":"Alexander","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8056","DOI":"10.3390\/rs6098056","article-title":"Categorizing grassland vegetation with full-waveform airborne laser scanning: A feasibility study for detecting Natura 2000 habitat types","volume":"6","author":"Zlinszky","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"521","DOI":"10.3390\/rs6010521","article-title":"Peat mapping associations of airborne radiometric survey data","volume":"6","author":"Beamish","year":"2014","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.3390\/rs6065325","article-title":"Circa 2010 thirty meter resolution forest map for China","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TGRS.2008.916480","article-title":"Fusion of hyperspectral and LiDAR remote sensing data for classification of complex forest areas","volume":"46","author":"Dalponte","year":"2008","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2011). Hyperspectral Remote Sensing of Vegetation, Taylor and Francis.","DOI":"10.1201\/b11222-3"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetlands Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_14","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. Cons."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.isprsjprs.2013.06.008","article-title":"A protocol for improving mapping and assessing of seagrass abundance along the West Central Coast of Florida using Landsat TM and EO-1 ALI\/Hyperion images","volume":"83","author":"Pu","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2014.05.021","article-title":"Assessment of ecophysiology of lake shore reed vegetation based on chlorophyll fluorescence, field spectroscopy and hyperspectral airborne imagery","volume":"157","author":"Stratoulias","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.3390\/s90604869","article-title":"Applications of remote sensing to alien invasive plant studies","volume":"9","author":"Huang","year":"2009","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"612","DOI":"10.3390\/rs5020612","article-title":"Remote distinction of a noxious weed (musk thistle: Carduus nutans) using airborne hyperspectral imagery and the Support Vector Machine Classifier","volume":"5","author":"Mirik","year":"2013","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Landgrebe, D.A. (2003). Signal Theory Methods in Multispectral Remote Sensing, John Wiley & Sons.","DOI":"10.1002\/0471723800"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1556\/ABot.50.2008.Suppl.5","article-title":"Distribution of the Hungarian (semi-) natural habitats I. Marshes and grasslands","volume":"50","year":"2008","journal-title":"Acta Bot. Hung."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1111\/avsc.12017","article-title":"Vegetation diversity of salt-rich grasslands in Southeast Europe","volume":"16","author":"Sopotlieva","year":"2013","journal-title":"Appl. Veg. Sci."},{"key":"ref_23","unstructured":"Zhang, W.J. (2012). Grasslands: Types, Biodiversity and Impacts, Nova Science Publishers Inc."},{"key":"ref_24","unstructured":"Borhidi, A., Kevey, B., and Lendvai, G. (2012). Plant communities of Hungary, Akad\u00e9miai Kiad\u00f3."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1111\/jvs.12027","article-title":"Mechanisms shaping plant biomass and species richness: Plant strategies and litter effect in alkali and loess grasslands","volume":"24","author":"Kelemen","year":"2013","journal-title":"J. Veg. Sci."},{"key":"ref_26","first-page":"693","article-title":"Fine-scale vertical position as an indicator of vegetation in alkali grasslands\u2014Case study based on remotely sensed data","volume":"209","author":"Alexander","year":"2014","journal-title":"Flora- Morphol. Distribut. Funct. Ecol. Plants"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.agee.2013.06.012","article-title":"Environmental factors driving vegetation and seed bank diversity in alkali grasslands","volume":"182","author":"Kelemen","year":"2014","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_28","first-page":"187","article-title":"Solonetz meadow vegetation (Beckmannion eruciformis) in East-Hungary\u2014An alliance driven by moisture and salinity","volume":"34","year":"2014","journal-title":"Tuexenia"},{"key":"ref_29","unstructured":"Shao, B. (2014). Salt Marshes: Ecosystem, Vegetation and Restoration Strategies, Nova Science Publishers Inc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pettorelli, N. (2013). The Normalised Difference Vegetation Index, Oxford University Press.","DOI":"10.1093\/acprof:osobl\/9780199693160.001.0001"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.1080\/014311698214217","article-title":"The NDVI and spectral decomposition for semi-arid vegetation abundance estimation","volume":"19","author":"Hurcom","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","unstructured":"Rabe, A., Jakimow, B., Held, M., van der Linden, S., and Hostert, P. EnMAP-Box. Available online: www.enmap.org."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.3390\/rs3091817","article-title":"Can the future EnMAP mission contribute to urban applications? A literature survey","volume":"3","author":"Heldens","year":"2011","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2012.03.006","article-title":"Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution","volume":"70","author":"Mansour","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Burai, P., Laposi, R., Enyedi, P., Schmotzer, A., and Kozma, B.V. (2011, January 6\u20139). Mapping invasive vegetation using AISA Eagle airborne hyperspectral imagery in the Mid-Ipoly-Valley. Proceedings of the 3rd IEEE GRSS Workshop on Hyperspectral Image and Signal Processing-WHISPERS\u20192011, Lisboa, Portugal.","DOI":"10.1109\/WHISPERS.2011.6080947"},{"key":"ref_36","first-page":"201","article-title":"Inclusion of prior probabilities derived from a nonparametric process into the maximum likelihood classifier","volume":"58","author":"Maselli","year":"1992","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (1999). Remote Sensing Digital Image Analysis, Springer-Verlag.","DOI":"10.1007\/978-3-662-03978-6"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"425","DOI":"10.14358\/PERS.75.4.425","article-title":"Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf, Coast","volume":"75","author":"Yang","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rse.2005.10.014","article-title":"Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest)","volume":"100","author":"Lawrence","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"Vapnick, V.N. (1988). Statistical Learning Theory, John Wiley and Sons Inc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1109\/TGRS.2004.827262","article-title":"Robust support vector method for hyperspectral data classification and knowledge discovery","volume":"42","author":"Moreno","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_45","unstructured":"Chan, J.C.-W., Spanhove, T., Ma, J., Vanden Borre, J., Paelinckx, D., and Canters, F. (July, January 29). Natura 2000 habitat identification and conservation status assessment with superresolution enhanced hyperspectral (CHRIS\/Proba) imagery. Proceedings of GEOBIA 2010 geographic object-based image analysis, Ghent, Belgium."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TGRS.2002.1010900","article-title":"Improved semi-arid community type differentiation with the NOAA AVHRR via exploitation of the directional signal","volume":"40","author":"Chopping","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/2\/2046\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:42:37Z","timestamp":1760215357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/2\/2046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2,12]]},"references-count":46,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,2]]}},"alternative-id":["rs70202046"],"URL":"https:\/\/doi.org\/10.3390\/rs70202046","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,2,12]]}}}