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Classification and inventory of freshwater wetlands and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using high-resolution satellite imagery

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Abstract

Flowing through Mongolia and Russia, the Selenga River is the main tributary to Lake Baikal, the world’s largest and deepest freshwater lake. The massive wetlands of the Selenga River Delta (SRD) on Lake Baikal perform important functions, including maintaining local and regional biodiversity and improving water quality. However, there exists a paucity of habitat and relevant ecological data for monitoring system-level changes. In this study, we characterized the rich habitat heterogeneity of the SRD using advanced 8-band multispectral satellite imagery coupled with a vegetation association algorithm and relatively extensive field surveys to analyze the spatial pattern of the system at multiple scales, from habitat-specific (e.g., Dense Floating Vascular habitats [Nymphoides sp.]) to coarser scales (e.g., Emergent Herbaceous; Aquatic Bed; Unconsolidated Bottom). We achieved an overall classification accuracy of 86.5 % for 22 wetland and aquatic habitat classes at the finest scale and greater than 91 % accuracy for broad vegetation and aquatic classes at more generalized scales. Our study provides the first detailed multi-scale characterization of the SRD for the conservation and management of the system and establishes baseline information for future change detection analyses.

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References

  • Adam E, Mutanga O, Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol Manage 18(3):281–296

    Article  Google Scholar 

  • Arzandeh S, Wang J (2002) Texture evaluation of RADARSAT imagery for wetland mapping. Can J Remote Sens 28(5):653–666

    Article  Google Scholar 

  • Ball GH, Hall DJ (1965) ISODATA, a novel method of data analysis and pattern classification. DTIC Document

  • Beydeman IN (1981) Vegetation Ecology in the Selenga River Delta. 2nd edn. Nauka Publishing House, Novosibirsk

  • Bharati MH, Liu JJ, MacGregor JF (2004) Image texture analysis: methods and comparisons. Chemometr Intell Lab Syst 72(1):57–71

    Article  CAS  Google Scholar 

  • Chepinoga V (2012) Wetland vegetation database of Baikal Siberia (WETBS). Biodivers Ecol 4:311. doi:10.7809/b-e.00107

    Article  Google Scholar 

  • Cook BD, Bolstad PV, Næsset E, Anderson RS, Garrigues S, Morisette JT, Nickeson J, Davis KJ (2009) Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations. Remote Sens Environ 113(11):2366–2379

    Article  Google Scholar 

  • Cowardin LM (1979) Classification of wetlands and deepwater habitats of the United States. U.S. Fish and Wildlife Service, Washington D.C, FWS/OBS-79-31

  • Dechka J, Franklin S, Watmough M, Bennett R, Ingstrup D (2002) Classification of wetland habitat and vegetation communities using multi-temporal Ikonos imagery in southern Saskatchewan. Can J Remote Sens 28(5):679–685

    Article  Google Scholar 

  • DigitalGlobe (2010) The Benefits of the 8 Spectral Bands of WorldView-2 (White paper). http://www.digitalglobe.com

  • Dribault Y, Chokmani K, Bernier M (2012) Monitoring seasonal hydrological dynamics of minerotrophic peatlands using multi-date GeoEye-1 very high resolution imagery and object-based classification. Remote Sens 4(7):1887–1912

    Article  Google Scholar 

  • Dufrêne M, Legendre P (1997) Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 67(3):345–366

    Google Scholar 

  • Emmerton CA, Lesack LF, Marsh P (2007) Lake abundance, potential water storage, and habitat distribution in the Mackenzie River Delta, western Canadian Arctic. Water Resour Res 43 (5)

  • Euliss NH, Gleason RA, Olness A, McDougal RL, Murkin HR, Robarts RD, Bourbonniere RA, Warner BG (2006) North American prairie wetlands are important nonforested land-based carbon storage sites. Sci Total Environ 361:179–188

    Article  CAS  PubMed  Google Scholar 

  • Foody GM, Campbell N, Trodd N, Wood T (1992) Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogramm Eng Remote Sens 58(9):1335–1341

    Google Scholar 

  • Foti R, del Jesus M, Rinaldo A, Rodriquez-Iturbe I (2012) Hydroperiod regime controls the organization of plant species in wetlands. Proc Natl Acad Sci 109(48):19596–19600

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Franklin SE, Peddle DR (1990) Classification of SPOT HRV imagery and texture features. Remote Sens 11(3):551–556

    Article  Google Scholar 

  • Gyninova A, Korsunov V (2006) The soil cover of the Selenga delta area in the Baikal region. Eurasian Soil Sci 39(3):243–250

    Article  Google Scholar 

  • Hampton SE, Izmest E, Lyubov R, Moore MV, Katz SL, Dennis B, Silow EA (2008) Sixty years of environmental change in the world’s largest freshwater lake–Lake Baikal, Siberia. Glob Change Biol 14(8):1947–1958

    Article  Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. Syst Man Cybern IEEE Trans 3(6):610–621

    Article  Google Scholar 

  • Hodgson M, Jensen J, Mackey H Jr, Coulter M (1987) Remote sensing of wetland habitat: a wood stork example. Photogramm Eng Remote Sens 53(8):1075–1080

    Google Scholar 

  • Hui Y, Rongqun Z, Xianwen L (2009) Classification of wetland from TM imageries based on decision tree. WSEAS Trans Info Sci Appl 6(7):1155–1164

    Google Scholar 

  • Ilyicheva E (2008) Dynamics of the Selenga river network and delta structure. Geogr Nat Res 29(4):343–347

    Article  Google Scholar 

  • Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall Inc, Upper Saddle River

    Google Scholar 

  • Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Kandus P, Malvárez AI (2004) Vegetation patterns and change analysis in the lower delta islands of the Paraná River (Argentina). Wetlands 24(3):620–632

    Article  Google Scholar 

  • Khazheeva Z, Tulokhonov A, Yao R, Hu W (2008) Seasonal and spatial distribution of heavy metals in the Selenga River Delta. J Geog Sci 18(3):319–327

    Article  Google Scholar 

  • Konovalova TI (2002) Mapping Geosystems in the Selenga River Delta. Mapp Sci Remote Sens 39(4):295–302

    Google Scholar 

  • Korytny L, Il’icheva E, Pavlov M, Amosova IY (2012) Hydrologo-morphological approach to regionalization of the Selenga river basin. Geogr Nat Resour 33(3):212–217

    Article  Google Scholar 

  • Leibowitz SG (2003) Isolated wetlands and their functions: an ecological perspective. Wetlands 23(3):517–531

    Article  Google Scholar 

  • Materka A, Strzelecki M (1998) Texture analysis methods-a review. Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels:9–11

  • McClain ME, Boyer EW, Dent LC, Gergel SE, Grimm NB, Groffman PM, Hart SC, Harvey JW, Johnston CA, Mayorga E, McDowell WH, Pinay G (2003) Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems 6:301–312

    Article  CAS  Google Scholar 

  • McCune B, Grace JB, Urban DL (2002) Analysis of ecological communities, vol 28. MjM software design, Gleneden Beach

    Google Scholar 

  • Mitsch W, Gosselink J (2000) Wetlands, 3rd edn. John Wiley and Sons, New York

    Google Scholar 

  • Moore MV, Hampton SE, Izmest’Eva LR, Silow EA, Peshkova EV, Pavlov BK (2009) Climate change and the world’s “Sacred Sea”-Lake Baikal, Siberia. Bioscience 59(5):405–417

    Article  Google Scholar 

  • Mueller MH, van der Valk AG (2002) The potential role of ducks in wetland seed dispersal. Wetlands 22(1):170–178

    Article  Google Scholar 

  • Narumalani S, Jensen JR, Burkhalter S, Althausen JD, Mackey H (1997) Aquatic macrophyte modeling using GIS and logistic multiple regression. Photogramm Eng Remote Sens 63(1):41–49

    Google Scholar 

  • Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetlands Ecol Manage 10(5):381–402

    Article  Google Scholar 

  • Potemkina T (2004) Hydrological-Morphological Zoning of the Mouth Zone of the Selenga River. Water Resour 31(1):11–16

    Article  CAS  Google Scholar 

  • Ramsar Convention of Wetlands (2012) The Ramsar Convention Definition of “Wetland” and Classification System for Wetland Type. http://www.ramsar.org/cda/ramsar/display/main/main.jsp?zn=ramsar&cp=1-26-7621235_4000_0. Accessed 10 feb 2012

  • Ramsar Convention on Wetlands Factsheet (1997) Information Sheet on Ramsar Wetlands, http://www.wetlands.org/RSIS/_COP9Directory/Directory/ris/2RU018en.pdf. Accessed on 15 Sept 2012, vol 2012. vol September 15, 2012

  • Research Systems Inc. (2007) ENVI Version 4.4 User’s Guide. ITT Visual Information Solutions

  • Richards JA, Jia X (1999) Remote sensing digital image analysis, vol 3. Springer, New York

    Book  Google Scholar 

  • Rusinek OT, Ufimtsev GF, Fialkov VA (2011) Baikal: A Scientific Tour of Lake Baikal. (Translated to English by Kaplunenko Y M.) Geo Academic Publishing House, Novosibirsk

  • Stehman SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62(1):77–89

    Article  Google Scholar 

  • Story M, Congalton RG (1986) Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sens 52(3):397–399

    Google Scholar 

  • Timm BC, McGarigal K (2012) Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests. Remote Sens Environ 127:106–117

    Article  Google Scholar 

  • Tiner R (2009) Overview of the NWI Program, Status report for the National Wetlands Inventory Program: 2009

  • Trofimova IE (2002) Typification and mapping of climate patterns within Baikalian mountain-andhollow system. Geography and Natural Resources (in Russian) 2:53–61

    Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150

    Article  Google Scholar 

  • Tulochonov AK, Plusnin AM (2008) The Selenga River Delta-Natural Biofilter and Indicator of the Condition of Lake Baikal. Publishing House of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk (in Russian)

    Google Scholar 

  • United Nations Educational S, and Cultural Organization (UNESCO) (1997) Convention Concerning the Protection of the World Cultural and Natural Heritage. World Heritage Committee. Proceedings of the 20th 713 Session, Merida. 02–07 December 1996. WHC-96/CONF.201/21

  • Vostokova EA, Gunin PD (2005) The Ecosystems of Selenga basin. Nauka Publishing House, Moscow (in Russian)

    Google Scholar 

  • Wilen B, Bates M (1995) The US fish and wildlife service’s national Wetlands inventory project. Plant Ecol 118(1):153–169

    Article  Google Scholar 

  • Yamagata Y, Yasuoka Y Classification of wetland vegetation by texture analysis methods using ERS-1 and JERS-1 images. In: Geoscience and Remote Sensing Symposium, 1993. IGARSS’93. Better Understanding of Earth Environment., International, 1993. IEEE, pp 1614–1616

  • Zak MR, Cabido M (2009) Spatial patterns of the Chaco vegetation of central Argentina: integration of remote sensing and phytosociology. Appl Veg Sci 5(2):213–226

    Article  Google Scholar 

Download references

Acknowledgments

The U.S. EPA Office of Research and Development partially funded and collaborated in the research described here under contract EP-D-06-096 to Dynamac Corporation. The research conducted was also partially funded by the U.S. Department of State, Biochemical Redirect Program with support from Eun Joo Yi and Patrick Russo of the International Science and Technology Center, Moscow, Russia. We appreciate the technical review and feedback provided by Ken Bailey, U.S. EPA, and the programmatic support of Doug Steele, U.S. EPA. Qiusheng Wu of the University of Cincinnati and the Dynamac Corporation provided technical assistance and support. We thank Alexey I. Trepeznikov of the Murzino settlement, Russia, for his hospitality, conviviality, and knowledge of the SRD. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA.

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Authors and Affiliations

  1. US Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, USA

    Charles R. Lane & Bradley C. Autrey

  2. Institute of General and Experimental Biology, Russian Academy of Sciences, Ulan-Ude, Russia

    Oleg Anenkhonov

  3. Dynamac Corporation c/o United States Environmental Protection Agency, Cincinnati, OH, USA

    Hongxing Liu

  4. Department of Geography, University of Cincinnati, Cincinnati, OH, USA

    Hongxing Liu

  5. Department of Botany, Irkutsk State University, Irkutsk, Russia

    Victor Chepinoga

  6. Russian Academy of Sciences, Laboratory of Biogeography, V.B. Sochava Institute of Geography, Irkutsk, Russia

    Victor Chepinoga

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  1. Charles R. Lane
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Correspondence to Charles R. Lane.

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Lane, C.R., Anenkhonov, O., Liu, H. et al. Classification and inventory of freshwater wetlands and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using high-resolution satellite imagery. Wetlands Ecol Manage 23, 195–214 (2015). https://doi.org/10.1007/s11273-014-9369-z

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