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Floodplain Forest Mapping with Sentinel-2 Imagery: Case Study of Naryn River, Kyrgyzstan

  • Akylbek ChymyrovEmail author
  • Florian Betz
  • Ermek Baibagyshov
  • Alishir Kurban
  • Bernd Cyffka
  • Umut Halik
Chapter

Abstract

The article presents the results of studies on the use of Sentinel-2 satellite data and the application of SNAP and ArcGIS software for the classification and mapping of forest cover of the Naryn river floodplain. Available inventory maps of the Kyrgyz Forestry Administration are outdated and do not meet the current requirements and need to be updated with the use of satellite images from different systems. High-resolution Sentinel-2A multispectral imagery has been used to study the supervised forest cover classification of the floodplain areas of the Naryn River in Kyrgyzstan for contributing to forest inventory and general analysis of the floodplain forest ecosystems. Using such high-resolution images in this study was due to the peculiar properties of classification and mapping of small vegetation areas of the unstable floodplains of mountain rivers. Supervised classification was performed using S2A MSI and WorldView-2 satellite images through SNAP software and field investigation data. Level-1C S2A multispectral images are processed to the Level-2A using Sen2Cor for the atmospheric corrections and further classification. The research results show the usefulness of high-resolution Sentinel-2 imagery for land use and land cover classification as well as the best freely available tool for thematic mapping of riparian forests.

Notes

Acknowledgments

This study is part of the project Ecosystem Assessment and Capacity Building along the Central Asian Rivers Tarim and Naryn funded by the Volkswagen Foundation. The authors would like to thank the European Space Agency (ESA) for the provision of Sentinel-2A data and Sentinel Application Platform.

References

  1. Akkartal A, Türüdü O, Erbek FS (2004) Analysis of changes in vegetation biomass using multitemporal and multisensor satellite data. In: ISPRS archives—volume XXXV part B8, pp 181–185Google Scholar
  2. Bartuś T (2014) Raster images generalization in the context of research on the structure of landscape and geodiversity. Geol Geophys Environ 40(3):271–284CrossRefGoogle Scholar
  3. Betz F, Cyffka B, Halik U, Kurban A, Chymyrov A, Baibagyshov E, Ding J (2015) GIS for environmental management along the Central Asian rivers Tarim and Naryn. In: Proceedings of the GIS in Central Asia conference—GISCA 2015 “geospatial management of land, water and resources”, May 14–16, 2015, TIIM, Tashkent, Uzbekistan, pp 13–18.Google Scholar
  4. Betz F, Rauschenberger J, Lauermann M, Cyffka B (2016) Using GIS and remote sensing for assessing riparian ecosystems along the Naryn River, Kyrgyzstan. Int J Geoinform 12(4):25–30Google Scholar
  5. Betz F, Lauermann M, Cyffka B (2018) Delineation of the riparian zone in data-scarce regions using fuzzy membership functions: an evaluation based on the case of the Naryn River in Kyrgyzstan. Geomorphology 306:170–181.  https://doi.org/10.1016/j.geomorph.2018.01.024CrossRefGoogle Scholar
  6. Chymyrov A, Baibagyshov E, Degembaeva N, Ismailov T, Urmambetova T, Aiypov B (2016) Study of the Naryn river floodplain forest ecosystem by using very high-resolution satellite imagery. Forest ecosystems under climate change: biological productivity and remote monitoring. Compendium of research papers/Executive editor Prof. E.A. Kurbanov. Volga State University of Technology, Yoshkar-Ola, pp 100–106Google Scholar
  7. Clerici N, Weissteiner CJ, Paracchini ML, Boschetti L, Baraldi A, Strob P (2013) Pan-European distribution modelling of stream riparian zones based on multi-source Earth Observation data. Ecological Indicators 24:211–223CrossRefGoogle Scholar
  8. Drusch M, Del Bello U, Carlier S, Colin O, Fernandez A, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P (2012) Sentinel-2: ESA’s optical high resolution mission for scientific observations of ocean, cryosphere and land. Remote Sens Environ 120:91–101CrossRefGoogle Scholar
  9. ESA (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services (ESA SP-1322/2 March 2012)Google Scholar
  10. ESA (2017a) Copernicus open access hub. https://scihub.copernicus.eu/dhus. Accessed 10 May 2017b
  11. ESA (2017b) Sentinel application platform (SNAP). http://step.esa.int/main/download. Accessed 10 May 2017
  12. ESA (2017c) Sen2Cor. http://step.esa.int/main/third-party-plugins-2/sen2cor. Accessed 10 May 2017
  13. Gomez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: a review. ISPRS J Photogramm Remote Sens 116:55–72CrossRefGoogle Scholar
  14. Hupp CR, Osterkamp WR (1996) Riparian vegetation and fluvial geomorphic processes. Geomorphology 14:277–295CrossRefGoogle Scholar
  15. Karthe D, Chalov S, Borchardt D (2015) Water resources and their management in Central Asia in the early twenty first century: status, challenges and future prospects. Environ Earth Sci 73(2):487–499CrossRefGoogle Scholar
  16. MA (Millenium Ecosystem Assessment) (2005) Ecosystems and human well-being: synthesis. Island Press, Washington, DCGoogle Scholar
  17. Ozdogan M, Kurban A (2012) The effects of spatial resolution on vegetation area estimates in the lower Tarim basin, NW China. In: Chen J, Wan S, Henebry G, Qi J, Gutman G, Sun G, Kappas M (eds) Dryland East Asia (DEA): land dynamics amid social and climate change. HEP-De Gruyter, BerlinGoogle Scholar
  18. Radoux J, Chome G, Jaques DC, Waldner F, Bellemans N, Matton N, Lamarche C, d’Andrimont R, Defourny P (2016) Sentinel-2’s potential for sub-pixel landscape feature detection. Remote Sens 8:488.  https://doi.org/10.3390/rs8060488CrossRefGoogle Scholar
  19. Vorobyev O, Kurbanov E, Gubaev A, Demisheva E, Воробьев ОН (2015) Method of stepwise classification of satellite images for the thematic mapping of forest cover. Bull Volga State Univ Technol Russia 4(28):57–72Google Scholar
  20. Zhu Z, Wang S, Woodcock CE (2015) Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sens Environ 159:269–277CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Akylbek Chymyrov
    • 1
    Email author
  • Florian Betz
    • 2
  • Ermek Baibagyshov
    • 3
  • Alishir Kurban
    • 4
  • Bernd Cyffka
    • 2
  • Umut Halik
    • 5
  1. 1.Department of Geodesy and GeoinformaticsKyrgyz State University of Construction, Transport and ArchitectureBishkekKyrgyzstan
  2. 2.Faculty of Mathematics and GeographyCatholic University Eichstaett-IngolstadtIngolstadtGermany
  3. 3.Department of EcologyNaryn State UniversityNarynKyrgyzstan
  4. 4.Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesBeijingChina
  5. 5.Key Laboratory of Oasis Ecology, College of Resources & Environmental Sciences, Xinjiang UniversityUrumqi, XinjiangChina

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