Alteration and Remediation of Coastal Wetland Ecosystems in the Danube Delta: A Remote-Sensing Approach

  • Simona NiculescuEmail author
  • Cédric Lardeux
  • Jenica Hanganu
Part of the Coastal Research Library book series (COASTALRL, volume 21)


Wetlands are important and valuable ecosystems; yet, since 1900, more than 50% of wetlands have been lost worldwide. An example of altered and partially restored coastal wetlands is the Danube Delta in Romania. Over time, human intervention has manifested itself in more than one-quarter of the entire Danube surface. This intervention was brutal and has rendered ecosystem restoration very difficult. Studies for rehabilitation/re-vegetation were begun immediately after the Danube Delta was declared a Biosphere Reservation in 1990. Remote sensing offers accurate methods for detecting changes in restored wetlands. Vegetation change detection is a powerful indicator of restoration success. The restoration projects use vegetative cover as an important indicator of restoration success. To follow the evolution of the vegetation cover of the restored areas, images obtained by radar and optical satellites, such as Sentinel-1 and Sentinel-2, have been used. The sensitivity of such sensors to the landscape depends on the wavelength of the radar or optical detection system and, for radar data, on polarization. Combining these types of data, which are associated with the density and size of the vegetation, is particularly relevant for the classification of wetland vegetation. In addition, the high temporal acquisition frequencies used by Sentinel-1, which are not sensitive to cloud cover, allow the use of temporal signatures of different land covers. Thus, to better understand the signatures of the different study classes, we analyze the polarimetric and temporal signatures of Sentinel-1 data. In a second phase, we perform classifications based on the Random Forest supervised classification algorithm involving the entire Sentinel-1 time series, proceeding through a Sentinel-2 collection and finally involving combinations of Sentinel-1 and -2 data. The supervised classifier used is the Random Forest algorithm that is available in the OrfeoToolbox (version 5.6) free software. Random Forest is an ensemble learning technique that builds upon multiple decision trees and is particularly relevant when combining different types of indicators. The results of this study relate to the use of combinations of data from different satellite sensors (multi-date Sentinel-1, Sentinel-2) to improve the accuracy of recognition and mapping of major vegetation classes in the restoring areas of the Danube Delta. First, the data from each sensor are classified and analyzed. The results obtained in the first step show quite good classification performance for only one Sentinel-2 data (87.5% mean accuracy), in contrast to the very good results obtained using the Sentinel-1 time series (95.7% mean accuracy). The combination of Sentinel-1 time series and optical data from Sentinel-2 improved the performance of the classification (97.1%).


Coastal wetlands Danube delta Alteration and remediation of ecosystems Remote sensing Synergy of radar time series Sentinel-1 and optical image Sentinel-2 



This work was supported by the CNES.


  1. Allen EB (2003) New directions and growth of restoration ecology. Restor Ecol 11:1–2CrossRefGoogle Scholar
  2. Antipa GR (1914a) Cateva probleme stiintifice si economice privitoare la Delta Dunarii. Analele Academiei Romane-Memoriile Sectiunii Stiintifice XXXVI(6):61–134Google Scholar
  3. Antipa GR (1914b) Delta Dunarii. Editura Ceres, BucurestiGoogle Scholar
  4. Apan AA, Raine SR, Paterson MS (2002) Mapping and analysis of changes in the riparian landscape structure of the Lockyer Valley catchment, Queensland, Australia. Landsc Urban Plan 59:43–57CrossRefGoogle Scholar
  5. Aronson J (2010) Restauration, réhabilitation, réaffectation. Ce que cachent les mots. Le dossier. Ecologie de la restauration. Espaces naturels 29:22–23Google Scholar
  6. Aronson J, Alexander S (2013) Ecosystem restoration is now a global priority: time to roll up our sleeves. Restor Ecol 21:293–296CrossRefGoogle Scholar
  7. Baillarin SJ, Meygret A, Dechoz C, Petrucci B, Lacherade S, Tremas T, Isola C, Martimort P, Spoto F (2012) Sentinel-2 level 1 products and image processing performances. In: 2012 IEEE international geoscience and remote sensing symposium, pp 7003–7006Google Scholar
  8. Bethemont J (1975) Le delta du Danube et son intégration dans l’espace économique roumain. Rev Géog Lyon 50(1.) 1975:77–95CrossRefGoogle Scholar
  9. Bourgeau-Chavez LL, Smith KB, Brunzell SM, Kasischke ES, Romanowicz EA, Richardson CJ (2005) Remote monitoring of regional inundation patterns and hydroperiod in the greater everglades using synthetic aperture radar. Wetlands 25:176–191CrossRefGoogle Scholar
  10. Bouzille J-B (2007) Gestion des habitats naturels et biodiversité. Lavoisier, ParisGoogle Scholar
  11. Ciocârlan V (1994) Flora Deltei Dunarii, Cormophyta. Editura Ceres, BucurestiGoogle Scholar
  12. Clewell AF, AronsonJ (2013) Ecological restoration, second edition: principles, values, and structure of an emerging profession, 2nd edn. Island Press, Washington, DCCrossRefGoogle Scholar
  13. Costantza R, d’Arge R, de Groot R, Farber S, Grasso M, Hannon B, Limburg K, Naeem S, O'Neill RV, Paruelo J, Raskin RG, Sutton P, van den Belt M (1997) The value of the world’s ecosystem services and natural capital. Nature 387:253–260CrossRefGoogle Scholar
  14. Dahl TE, U.S. Fish and Wildlife Service, National Wetlands Inventory Group (Saint Petersburg, Florida) (1990) Wetlands losses in the United States, 1780s to 1980s. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DCGoogle Scholar
  15. Dodds WK, Wilson KC, Rehmeier RL, Knight GL, Wiggam S, Falke JA, Dalgleish HJ, Bertrand KN (2008) Comparing ecosystem goods and services provided by restored and native lands. Bioscience 58(9):837–845CrossRefGoogle Scholar
  16. Egan D, Howell EA (eds) (2001) Conservation ecology: the historical ecology handbook: a restorationist’s guide to reference ecosystems. Island Press, Washington, DCGoogle Scholar
  17. Ellison AM (2000) Mangrove restoration: do we know enough? Restor Ecol 8:219–229CrossRefGoogle Scholar
  18. Gastescu P, Stiuca R (2008) Delta Dunarii: Rezervatie a biosferei. CD Press, BucharestGoogle Scholar
  19. Guo Q, Psuty NP (1997) Flood-tide deltaic wetlands: detection of their sequential spatial evolution. Photogramm Eng Remote Sens 63:273–280Google Scholar
  20. Hanganu J, Dubyna D, Zhmud E, Grigoraş I, Menke U, Drost H, Ştefan N, Sărbu I (2002) Vegetation of the biosphere reserve Danube Delta, with transboundary vegetation map on a 1:150,000 scale. RIZA rapport, LelystadGoogle Scholar
  21. Harttera J, Ryan SJ (2010) Top-down or bottom-up? Decentralization, natural resource management, and usufruct rights in the forests and wetlands of west-ern Uganda. Land Use Policy 27:815–826CrossRefGoogle Scholar
  22. Heinl M, Neuenschwander A, Sliva J, Vanderpost C (2006) Interactions between fire and flooding in a southern Africa floodplain system (Okavango Delta, Botswana). Landsc Ecol 21:699–709CrossRefGoogle Scholar
  23. Hinkle RL, Mitsch WJ (2005) Salt marsh vegetation recovery at salt hay farm wetland restoration sites on Delaware Bay. Ecol Eng 25:240–251CrossRefGoogle Scholar
  24. Hütt C, Koppe W, Miao Y, Bareth G (2016) Best accuracy land use/land cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sens 8:684CrossRefGoogle Scholar
  25. Inglada J, Vincent A, Arias M, Marais-Sicre C (2016) Improved early crop type identification by joint use of high temporal resolution SAR and optical image time series. Remote Sens 8:362CrossRefGoogle Scholar
  26. Jarzemsky RD, Burchell MR II, Evans RO (2013) The impact of manipulating sur-face topography on the hydrologic restoration of a forested coastal wetland. Ecol Eng 58:35–43CrossRefGoogle Scholar
  27. Jensen JR (2000) Remote sensing of the environment: an earth resource perspective, 2nd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  28. Jensen J, Rutchey K, Koch M, Narumalani S (1995) Inland wetland change detection in the everglades water conservation area 2A using a time series of normalized remotely sensed data. J Photogramm Eng Remote Sens 61(2):199–209Google Scholar
  29. Junk WJ, An SQ, Finlayson CM, Gopal B, Kvet J, Mitchell SA, Mitsch WJ, Robarts RD (2012) Current state of knowledge regarding the world’s wetlands and their future under global climate change: a synthesis. Aquat Sci 1:151–167Google Scholar
  30. Klemas V (2013) Using remote sensing to select and monitor wetland restoration sites: an overview. J Coast Res 29(4):958–970CrossRefGoogle Scholar
  31. Lang MW, McCarty GW (2008) Remote sensing data for regional wetland mapping in the United States: trends and future prospects. In: Russo RE (ed) Wetlands: ecology, conservation and restoration. Nova, Hauppauge, pp 1–40Google Scholar
  32. Michener WK, Houhoulis PF (1997) Detection of vegetation changes associated with extensive flooding in a forested ecosystem. Photogramm Eng Remote Sens 63:1363–1374Google Scholar
  33. Mitsch WJ, Gosselink JG (2007) Wetlands. Wiley, Hoboken, pp 270–382Google Scholar
  34. Munteanu I (1979) Cercetări privind însusirile fizice si chimice ale unor soluri submerse din Delta Dunării în regim îndiguit si desecat pentru prevenirea degradării acestora prin folosire agricolă, Raport nr. 1201/5/1979. Bucuresti, ICPAGoogle Scholar
  35. Niculescu S, Lardeux C, Hanganu J, Mercier G, David L (2015a) Change detection of floodable in Danube delta by radar images. Nat Hazards 78(3):1899–1916CrossRefGoogle Scholar
  36. Niculescu S, Pécaud D, Michèle-Guillou E, Soare P, David L (2015b) Quel développement durable pour le delta du Danube? Enquête à Pardina. VertigO 15(1):2–26Google Scholar
  37. Niculescu S, Lardeux C, Grigoras I, Hanganu J, David L (2016) Synergy between LiDAR, RADARSAT-2, and Spot-5 images for the detection and mapping of wetland vegetation in the Danube delta. IEEE J Sel Top Appl Earth Obs Remote Sens 9:3651–3666CrossRefGoogle Scholar
  38. Novo EMLM, Costa MPF, Mantovani JE, Lima IBT (2002) Relationship between macrophyte stand variables and radar backscatter at L and C band, Tucurui reservoir, Brasil. Int J Remote Sens 23:1241–1260CrossRefGoogle Scholar
  39. Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetl Ecol Manag 10:381–402CrossRefGoogle Scholar
  40. Papa F, Prigent C, Durand F, Rossow WB (2006) Wetland dynamics using a suite of satellite observations: a case study of application and evaluation for the Indian Subcontinent. Geophys Res Lett 33:4Google Scholar
  41. Perrow MR, Davy AJ (2002) Handbook of ecological restoration. Cambridge University Press, Cambridge, UKCrossRefGoogle Scholar
  42. Phinn SR, Stow DA, Mouwerik DV (1999) Remotely sensed estimates of vegetation structural characteristics in restored wetlands, Southern California. Photogramm Eng Remote Sens 65:485–493Google Scholar
  43. Ramsey EW III, Nelson GA, Sapkota SK (1998) Classifying coastal resources by integrating optical and radar imagery and color infrared photography. Mangrove Salt Marshes 2:109–119CrossRefGoogle Scholar
  44. Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104CrossRefGoogle Scholar
  45. Rosso PH, Ustin SL, Hastings A (2005) Use of lidar to study changes associated with Spartina invasion in San Francisco Bay marshes. Remote Sens Environ 100:295–306CrossRefGoogle Scholar
  46. Russell G. D., Hawkins C. P., et O’Neill M. P. (1997) The role of GIS in selecting sites for Riparian restoration based on hyderology and land use, Restor Ecol 5, p. 56–68.CrossRefGoogle Scholar
  47. Shuman CS, Ambrose RF (2003) A comparison of remote sensing and ground-based methods for monitoring wetland restoration success. Restor Ecol 11:325–333CrossRefGoogle Scholar
  48. Simenstad C, Reed D, Ford M (2006) When is restoration not? Incorporating landscape-scale processes to restore self-sustaining ecosystems in coastal wetland restoration. Ecol Eng 26:27–39CrossRefGoogle Scholar
  49. Suding KN, Hobbs RJ (2009) Threshold models in restoration and conservation: a developing framework. Trends Ecol Evol 24:271–279CrossRefGoogle Scholar
  50. Temperton VM, Hobbs RJ, Nuttle T, Halle S (2013) Assembly rules and restoration ecology: bridging the gap between theory and practice. Island Press, Washington, DCGoogle Scholar
  51. Thayer GW (ed) (1992) Restoring the Nation’s marine environment. Maryland Seagrant Program, 728pGoogle Scholar
  52. Turner RE, Lewis RR III (1996) Hydrologic restoration of coastal wetlands. Wetl Ecol Manag 4:65–72CrossRefGoogle Scholar
  53. Tuxen KA, Schile LM, Kelly M, Siegel SW (2008) Vegetation colonization in a restoring tidal marsh: a remote sensing approach. Restor Ecol 16:313–323CrossRefGoogle Scholar
  54. Walker LR, del Moral R (2003) Primary succession and ecosystem rehabilitation. Cambridge University Press, New York, 423pCrossRefGoogle Scholar
  55. White D, Fenessy S (2005) Modeling the suitability of wetlandrestoration scale. Ecol Eng 24:359–377CrossRefGoogle Scholar
  56. White PS, Walker JL (1997) Approximating nature’s variation: selecting and using reference information in restoration ecology. Restor Ecol 5:338–349CrossRefGoogle Scholar
  57. Wilcox DA, Whillans TH (1999) Techniques for restoration of disturbed coastal wetlands of the Great Lakes. Wetlands 19:835–857CrossRefGoogle Scholar
  58. Wortley L, Hero JM, Howes M (2013) Evaluating ecological restoration success: a review of the literature. Restor Ecol 21:537–543CrossRefGoogle Scholar
  59. Zedler JB (2000) Progress in wetland restoration ecology. Trends Ecol Evol 15:402–407CrossRefGoogle Scholar
  60. Zedler JB, Kercher S (2005) Wetland resources: status, trends, ecosystem services, and restorability. Annu Rev Environ Resour 30:39–74CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Simona Niculescu
    • 1
    Email author
  • Cédric Lardeux
    • 2
  • Jenica Hanganu
    • 3
  1. 1.Laboratoire LETG-Brest, Géomer, UMR 6554 CNRS, IUEM-UBOPlouzanéFrance
  2. 2.Office National des ForêtsParis Cedex 12France
  3. 3.Danube Delta National Institute for Research and DevelopmentTulceaRomania

Personalised recommendations