[Ashutosh S., 2012, Monitoring forests: A new paradigm of remote sensing & GIS based change detection. “Journal of Geographic Information Systems” Vol. 4, pp. 470–478.10.4236/jgis.2012.45051]Search in Google Scholar
[Bannari A., Morin D., Bonn F., Huete A.R., 1995, A review of vegetation indices. “Remote Sensing Review” Vol. 13, no. 1–2, pp. 95–120.10.1080/02757259509532298]Search in Google Scholar
[Beger M., Moreno J., Johannessen J., Levelt P., Hanssen R., 2012, ESA’s Sentinel missions in support of earth system science. “Remote Sensing of Environment” Vol. 120, pp. 84–90.10.1016/j.rse.2011.07.023]Search in Google Scholar
[Billingsley F.C., 1984, Remote sensing for monitoring vegetation: an emphasis on satellites. In: The Role of Terrestrial Vegetation in the Global Carbon Cycle. Edited by G.M. Woodwell. New York: John Wiley and Sons, pp. 161–180.]Search in Google Scholar
[Bösche N.K., Rogaß C., Mielke C., Kaufmann H., 2014, Hyperspectral digital image analysis and geochemical analysis of a rare earth elements mineralized intrusive complex (Fen carbonatite Complex in Telemark Region, Norway. In: Proceedings of 34th EARSeL Symposium, pp. 4.1–4.6, DOI: 10.12760/03-2014-07.10.12760/03-2014-07]Open DOISearch in Google Scholar
[Braun A., Weinmann M., Keller S., Muller R., Reinartz P., Hinz S., 2015, EnMAP contest: developing and comparing classification approaches for the environmental mapping and analysis programme – dataset and first results. “Remote Sensing and Spatial Information Sciences” Vol. XL-3/W3, pp. 169–175.10.5194/isprsarchives-XL-3-W3-169-2015]Search in Google Scholar
[Braun-Blanquet J., Chou Y.T., 1947, Carte des groupements végétaux de la France, region nordouest de Montpellier. Station internationale de geobotanique mediterraneenne et alpine, Montpellier.]Search in Google Scholar
[Buddenbaum H., Rock G., Hill J., Werner W., 2015, Measuring stress reactions of beech seedlings with PRI, fluorescence, temperatures and emissivity from VNIR and thermal field imaging spectroscopy. “European Journal of Remote Sensing” Vol. 48, pp. 263–282.10.5721/EuJRS20154815]Search in Google Scholar
[Buddenbaum H., Stern O., Paschmionka B., Hass E., Gattung T., Stoffels J., Hill J., Werner W., 2015, Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings. “International Journal of Remote Sensing” Vol. 36, pp. 4590–4605.10.1080/01431161.2015.1084435]Search in Google Scholar
[Burai P., Deak B., Valko O., Tomor T., 2016, Classification of Herbaceous vegetation using hyperspectral imagery. “Remote Sensing” Vol. 7, no. 2, pp. 2046–2066.10.3390/rs70202046]Search in Google Scholar
[Campbell J., Wynne R., 2011, Introduction to remote sensing. New York: The Guilford Presss, pp. 317–360.]Search in Google Scholar
[Congalton R.G., 1991, A review of assessing the accuracy of classifications of remotely sensed data. “Remote Sensing of Environment” Vol. 37, pp. 35–46.10.1016/0034-4257(91)90048-B]Search in Google Scholar
[Delegido J., Verrelst J., Alonso L., Moreno J., 2011, Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. “Sensor” Vol. 11, no. 7, pp. 7063–7081.10.3390/s110707063323168022164004]Search in Google Scholar
[Dirnböck T., Dullinger S., Gottfried M., Ginzler C., Grabherr G., 2003, Mapping alpine vegetation based on image analysis, topographic variables and Canonical Correspondence Analysis. “Applied Vegetation Science” Vol. 6, no. 1, pp. 85–96.10.1111/j.1654-109X.2003.tb00567.x]Search in Google Scholar
[Dotzler S., Hill J., Buddenbaum H., Stoffels J., 2015, The potential of EnMAP and Sentinel-2 data for detecting drought stress phenomena in deciduous forest communities. “Remote Sensing” Vol. 7, no. 10, pp. 14227–14258.10.3390/rs71014227]Search in Google Scholar
[Dragozi E., Gitas I.Z., Stavrakoudis D.G., Theocharis J.B., 2014, Burned area mapping using Support Vector Machines and the FuzCoC feature selection method on VHR IKONOS imagery. “Remote Sensing” Vol. 6, no. 12, pp. 12005–12036.10.3390/rs61212005]Search in Google Scholar
[Feng Q., Gong J., Liu J., Li Y., 2015, Flood mapping based on multiple endmember spectral mixture analysis and Random Forest classifier – the case of Yuyao, China. “Remote Sensing“ Vol. 7, pp. 12539−12562.10.3390/rs70912539]Search in Google Scholar
[Gartizia M., Alados C., Perez-Cabello F., Bueno C., 2013, Improving the accuracy of vegetation classifications in mountainous areas. A case study in Spanish Pyrenees. “Mountain Research and Development” Vol. 33, no. 1, pp. 63–74.10.1659/MRD-JOURNAL-D-12-00011.1]Search in Google Scholar
[Humbolt, A. von, Bonpland A., 1895, Geographie des plantes equinoxiales: tableau physique des Andes et pays voisins. In: Essai sur la géographie des plantes, Paris: Levrault, Schoell et Co.]Search in Google Scholar
[Immitzer M., Vuolo F., Atzberger C., 2016, First experience with Sentinel-2 Data for crop and tree species classifications in central Europe. “Remote Sensing” Vol. 8, no. 3, pp. 166–193.10.3390/rs8030166]Search in Google Scholar
[Jarocińska A., Zagajewski B., 2008, Korelacje naziemnych i lotniczych teledetekcyjnych wskaźników roślinności dla zlewni Bystrzanki. „Teledetekcja Środowiska” T. 40, pp. 100–125.]Search in Google Scholar
[Jarocińska A., Zagajewski B., 2009, Remote sensing tools for analyzing state and condition of vegetation. “Annals of Geomatics”, Polish Association for Spatial Information, Vol. 7, no. 2, pp. 47–54.]Search in Google Scholar
[Jarocińska A., Kacprzyk M., Marcinkowska-Ochtyra A., Ochtyra A., Zagajewski B., Meuleman K., 2016, The application of APEX images in the assessment of the state of non-forest vegetation in the Karkonosze Mountains. “Miscellanea Geographica – Regional Studies on Development” Vol. 20, no. 1, pp. 21–27.10.1515/mgrsd-2016-0009]Search in Google Scholar
[Jensen J.R., 1983, Biophysical remote sensing – review article. “Annals of the Association of American Geographers” Vol. 73, no. 1, pp. 111−132.10.1111/j.1467-8306.1983.tb01399.x]Search in Google Scholar
[Kaufmann H., Forster S., Wulf H., Segl K., Guanter L., Bochow M., Heiden U., Muller A., Heldens W., Scheneidehan T., Leitão P.J., van der Linden S., Hostert P., Hill J., Buddenbaum H., Mauser W., Hank T., Krasemann H., Rottgers R., Oppelt N., Heim B., 2012, EnMAP Technical Report, GFZ Data Services. Potsdam, pp. 1–44.]Search in Google Scholar
[Khorram S., Nelson S., Koch F., van der Wiele C., 2012, Remote sensing. New York: Springer US, pp. 1−37.10.1007/978-1-4614-3103-9_1]Search in Google Scholar
[Küchler A., Zonneveld I., 1988, Vegetation mapping. Berlin: Springer.10.1007/978-94-009-3083-4]Search in Google Scholar
[Kupková L., Cervená L., Suhá R., Jakesová L., Zagajewski B., Brezina S., Alberchtova J., 2017, Classification of tundra in the Karkonose Mountains National Park, using APEX, AISA Dual and Sentinel-2A Data. “European Journal of Remote Sensing” Vol. 50, no. 1, pp. 29–46.10.1080/22797254.2017.1274573]Search in Google Scholar
[Kycko M., Zagajewski B., Zwijacz-Kozica M., Cierniewski J., Romanowska E., Orłowska K., Ochtyra A., Jarocińska A., 2017, Assessment of hyperspectral remote sensing for analyzing the impact of human trampling on Alpine wards. “Mountain Research and Development” Vol. 37, no. 1, pp. 66–74.10.1659/MRD-JOURNAL-D-15-00050.1]Search in Google Scholar
[Leitão P., Schwieder M., Suess S., Okujeni A., Galvão L., Linden S., Hostert P., 2015, Monitoring natural ecosystem and ecological gradients: perspectives with EnMAP. “Remote Sensing” Vol. 7, no. 10, pp. 13098–13119.10.3390/rs71013098]Search in Google Scholar
[Locherer M., Hank T., Danner M., Mauser W., 2015, Retrieval of seasonal leaf area index from simulated EnMAP data through optimized LUT-Based inversion of the PROSAIL model. “Remote Sensing” Vol. 7, no. 8, pp. 10321–10346.10.3390/rs70810321]Search in Google Scholar
[Marcinkowska A., Zagajewski B., Ochtyra A., Jarocińska A., Raczko E., Kupková L., Stych P., Meuleman K., 2014, Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines. “Miscellanea Geographica” Vol. 18, no. 2, pp. 23–29.10.2478/mgrsd-2014-0007]Search in Google Scholar
[Marcinkowska-Ochtyra A., Zagajewski B., Ochtyra A., Jarocińska A., Wojtuń B., Rogass C., Mielke C., Lavender S., 2017, Subalpine and alpine vegetation classification based on hyperspectral APEX and simulated EnMAP images. “International Journal of Remote Sensing” Vol. 38, no. 7, pp. 1839–1864.10.1080/01431161.2016.1274447]Open DOISearch in Google Scholar
[Martius C.F.P., 1858, Flora brasiliensis. Leipzig: Oldenburg Verlag.]Search in Google Scholar
[Mielke C., Muedi T., Papenfuß A., Bösche N., Rogaß C., Gauert C., Altenberger U., de Wit M., 2016, Multi- and hyperspectral spaceborne remote sensing of the Aggeneys base metal sulphide mineral deposit sites in the Lower Orange River region, South Africa. “South African Journal of Geology” Vol. 119, no. 1, pp. 63–76.10.2113/gssajg.119.1.63]Search in Google Scholar
[Nink S., Hill J., Buddenbaum H., Stoffels J., Sachtleber T., Langshausen J., 2015, Assessing the suitability of future multi- and hyperspectral satellite systems for mapping the spatial distribution of Norway spruce timber volume. “Remote Sensing” Vol. 7, pp. 12009–12040.10.3390/rs70912009]Search in Google Scholar
[Ochtyra A., Zagajewski B., Kozłowska A., Marcinkowska-Ochtyra A., Jarocińska A., 2016, Ocena kondycji drzewostanów Tatrzańskiego Parku Narodowego za pomocą metody drzewa decyzyjnego oraz wielospektralnych obrazów satelitarnych Landsat 5 TM. „Sylwan” T. 160, nr 1, pp. 256–264.]Search in Google Scholar
[Pedrotti F., 1967, Carta fitosociologica della vegetazione de Montelago. Camerino: Instituto di Botanica, Universita di Camerino.]Search in Google Scholar
[Pesaresi M., Corbane C., Julea A., Florczyk A., Syrris V., Soille P., 2016, Assessment of the added-value of Sentinel-2 for detecting built-up areas. “Remote Sensing” Vol. 8, pp. 299–316.10.3390/rs8040299]Search in Google Scholar
[Quattrochi D.A., Luvall J.C., 1999, Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications. “Landscape Ecology” Vol. 14, no. 6, pp. 577–598.10.1023/A:1008168910634]Open DOISearch in Google Scholar
[Raczko E., Zagajewski B., 2017, Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images. “European Journal of Remote Sensing” Vol. 50, no. 1, pp. 144–154.10.1080/22797254.2017.1299557]Search in Google Scholar
[Schmid E., 1940, Die Vegetationskartierung der Schweiz im Masstab 1:200,000. Geobotanisches Forschungsinstitut Rübel in Zürich, Bericht für das Jahr 1939, pp. 76−85.]Search in Google Scholar
[Schouw J.F., 1823, Grundzige einer allgemeinen Pflanzengeographie (mit Atlas). Berlin.10.1515/9783111580531]Search in Google Scholar
[Sendtner O., 1854, Die Vegetationsverhältnisse Südbayerns nach den Grundsätzen der Pflanzengeographie und mit Bezugnahme auf die Landescultur geschildert. München.]Search in Google Scholar
[Shweider M., Leitão P., Suess S., Senf C., Hostert P., 2014, Estimating fractional shrub cover using simulated EnMAP Data: a comparision of three machine learning tehniques. “Remote Sensing” Vol. 6, no. 4, pp. 3427–3445.10.3390/rs6043427]Search in Google Scholar
[Siegmann B., Jarmer T., Beyer F., Ehlers M., 2015, The potential of pan-sharpened EnMAP data for the assessment of wheat LAI. “Remote Sensing” Vol. 7, no. 10, pp. 12737–12762.10.3390/rs71012737]Search in Google Scholar
[Stoffels, J., Sachtleber, T., Mader, S., Buddenbaum, H., Stern, O., Langshausen, J., Dietz, J., 2015, Satellite-based derivationof high-resolution forest information layers for operational forest management. “Forests“ Vol. 6, pp. 1982–2013.10.3390/f6061982]Search in Google Scholar
[Stratoulias D., Balzter H., Zlinszky A., Tóth V.R., 2015, Assessment of ecophysiology of lake shore reed vegetation based on chlorophyll fluorescence, field spectroscopy and hyperspectral airborne imagery. “Remote Sensing of Environment” Vol. 157, pp. 72–84.10.1016/j.rse.2014.05.021]Search in Google Scholar
[Suchá R., Jakešová L., Kupková L., Červená L., 2016, Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data. “AUC Geographica” Vol. 51, no. 1, pp. 113–129.10.14712/23361980.2016.10]Search in Google Scholar
[Suess S., van der Linden S., Okujeni A., Leitão P., Shweider M., Hostert P., 2015, Using class probabilities to map gradual transitions in shrub vegetation from simulated EnMAP data. “Remote Sensing” Vol. 7, no. 8, pp. 10668–10688.10.3390/rs70810668]Search in Google Scholar
[Thales Alenia Space, 2016, Sentinel-2 Products specification document (PSD). European Space Agency (ESA), https://sentinel.esa.int/documents/247904/685211/Sentinel-2-Product-Specifications-Document, pp. 41–53 (access 6.01.2017).]Search in Google Scholar
[Tobler M., Cochard R., Edwards P., 2003, The impact of cattle ranching on large-scale vegetation patterns in a coastal savanna in Tanzania. “Journal of Applied Ecology” Vol. 40, no. 3, pp. 430–444.10.1046/j.1365-2664.2003.00816.x]Open DOISearch in Google Scholar
[Tomczak J., 2013, Wprowadzenie do sztucznej inteligencji, https://www.ii.pwr.edu.pl/~tomczak/PDF/si1.pdf (access 01.09.2017).]Search in Google Scholar
[Topaloğlu R., Sertel E., Musaoglu N., 2016, Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover/use mapping. “The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences” Vol. XLI-B8, pp. 1055–1059.10.5194/isprs-archives-XLI-B8-1055-2016]Search in Google Scholar
[Traganos D., Reinartz P., 2017, Mapping Mediterranean seagrasses with Sentinel-2 imagery. “Marine Pollution Bulletin” (article in print).10.1016/j.marpolbul.2017.06.07528676173]Open DOISearch in Google Scholar
[Vapnik, V.N., 1995, The nature of statistical learning theory. New York: Springer.10.1007/978-1-4757-2440-0]Search in Google Scholar
[Wijaya A., Gloaguen R., 2007, Comparison of multi-source data support vector machine classification for mapping of forest cover. In: Geoscience and Remote Sensing Symposium 2007. IGARSS 2007. IEEE International, pp. 1275–1278.10.1109/IGARSS.2007.4423038]Search in Google Scholar
[Wojtuń B., Żołnierz L., 2002, Plan ochrony ekosystemów nieleśnych – inwentaryzacja zbiorowisk. W: Plan Ochrony Karkonoskiego Parku Narodowego. Brzeg: Biuro Urządzania Lasu i Geodezji Leśnej, Oddział w Brzegu, pp. 67 and 2 maps.]Search in Google Scholar
[Xie Y., Sha Z., Yu M., 2008, Remote sensing imaginery in vegetation mapping: a review. “Journal of Plant Ecology” Vol. 1, no. 1, pp. 9–23.10.1093/jpe/rtm005]Search in Google Scholar
[Yokoya N., Cheung-Wai Chan J., Segl K., 2016, Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated EnMAP and Sentinel-2 images. “Remtote Sensing” Vol. 8, no. 3, pp. 172–190.10.3390/rs8030172]Search in Google Scholar
[Zagajewski B., 2010, Ocena przydatności sieci neuronowych i danych hiperspektralnych do klasyfikacji roślinności Tatr Wysokich. „Teledetekcja Środowiska” T. 43, 113 pp.]Search in Google Scholar
[Zagajewski B., Folbrier A., Kozłowska A., Sobczak M., Wrzesień M., 2005, Zintegrowane pomiary roślinności wysokogórskiej. „Teledetekcja Środowiska” T. 36, pp. 61−68.]Search in Google Scholar
[http://www.enmap.org/?q=box_applications (access 1.09.2017)]Search in Google Scholar
[http://land.copernicus.eu/global/products (access 1.09.2017)]Search in Google Scholar
[https://earth.esa.int/web/guest/missions/esa-future-missions/flex (access 1.09.2017)]Search in Google Scholar