Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

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Effective assessment of environmental changes requires an update of vegetation maps as it is an indicator of both local and global development. It is therefore important to formulate methods which would ensure constant monitoring. It can be achieved with the use of satellite data which makes the analysis of hard-to-reach areas such as alpine ecosystems easier.

Every year, more new satellite data is available. Its spatial, spectral, time, and radiometric resolution is improving as well. Despite significant achievements in terms of the methodology of image classification, there is still the need to improve it. It results from the changing needs of spatial data users, availability of new kinds of satellite sensors, and development of classification algorithms. The article focuses on the application of Sentinel-2 and hyperspectral EnMAP images to the classification of alpine plants of the Karkonosze (Giant) Mountains according to the: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood (ML) algorithms. The effects of their work is a set of maps of alpine and subalpine vegetation as well as classification error matrices. The achieved results are satisfactory as the overall accuracy of classification with the SVM method has reached 82% for Sentinel-2 data and 83% for EnMAP data, which confirms the applicability of image data to the monitoring of alpine plants.

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  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Campbell J. Wynne R. 2011 Introduction to remote sensing. New York: The Guilford Presss pp. 317–360.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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 YuyaoChina. “Remote Sensing“ Vol. 7 pp. 12539−12562.

  • 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.

  • 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.

  • 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.

  • Jarocińska A. Zagajewski B. 2008 Korelacje naziemnych i lotniczych teledetekcyjnych wskaźników roślinności dla zlewni Bystrzanki. „Teledetekcja Środowiska” T. 40 pp. 100–125.

  • 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.

  • 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.

  • Jensen J.R. 1983 Biophysical remote sensing – review article. “Annals of the Association of American Geographers” Vol. 73 no. 1 pp. 111−132.

  • 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.

  • Khorram S. Nelson S. Koch F. van der Wiele C. 2012 Remote sensing. New York: Springer US pp. 1−37.

  • Küchler A. Zonneveld I. 1988 Vegetation mapping. Berlin: Springer.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Martius C.F.P. 1858 Flora brasiliensis. Leipzig: Oldenburg Verlag.

  • 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.

  • 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.

  • 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.

  • Pedrotti F. 1967 Carta fitosociologica della vegetazione de Montelago. Camerino: Instituto di Botanica Universita di Camerino.

  • 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.

  • 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.

  • 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.

  • Schmid E. 1940 Die Vegetationskartierung der Schweiz im Masstab 1:200000. Geobotanisches Forschungsinstitut Rübel in Zürich Bericht für das Jahr 1939 pp. 76−85.

  • Schouw J.F. 1823 Grundzige einer allgemeinen Pflanzengeographie (mit Atlas). Berlin.

  • Sendtner O. 1854 Die Vegetationsverhältnisse Südbayerns nach den Grundsätzen der Pflanzengeographie und mit Bezugnahme auf die Landescultur geschildert. München.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Thales Alenia Space 2016 Sentinel-2 Products specification document (PSD). European Space Agency (ESA) pp. 41–53 (access 6.01.2017).

  • 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.

  • Tomczak J. 2013 Wprowadzenie do sztucznej inteligencji (access 01.09.2017).

  • 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.

  • Traganos D. Reinartz P. 2017 Mapping Mediterranean seagrasses with Sentinel-2 imagery. “Marine Pollution Bulletin” (article in print).

  • Vapnik V.N. 1995 The nature of statistical learning theory. New York: Springer.

  • 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.

  • 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.

  • 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.

  • 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.

  • Zagajewski B. 2010 Ocena przydatności sieci neuronowych i danych hiperspektralnych do klasyfikacji roślinności Tatr Wysokich. „Teledetekcja Środowiska” T. 43 113 pp.

  • Zagajewski B. Folbrier A. Kozłowska A. Sobczak M. Wrzesień M. 2005 Zintegrowane pomiary roślinności wysokogórskiej. „Teledetekcja Środowiska” T. 36 pp. 61−68.

  • (access 1.09.2017)

  • (access 1.09.2017)

  • (access 1.09.2017)

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