Skip to main content

Mapping of submerged aquatic vegetation in rivers from very high-resolution image data, using object-based image analysis combined with expert knowledge

Abstract

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high-resolution image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus peltatus, Callitriche obtusangula, Potamogeton natans L., Sparganium emersum R. and Potamogeton crispus L., were classified from the data using object-based image analysis and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image resulted in 53% overall accuracy. These consistent results not only show promise for species-level mapping in such biodiverse environments but also prompt a discussion on assessment of classification accuracy.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Source Visser et al. (2015)

Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  • Anders, N. S., A. C. Seijmonsbergen & W. Bouten, 2011. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sensing of Environment 115: 2976–2985.

    Article  Google Scholar 

  • Anderson, K. & K. J. Gaston, 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11: 138–146.

    Article  Google Scholar 

  • Arvor, D., L. Durieux, S. Andrés & M.-A. Laporte, 2013. Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing 82: 125–137.

    Article  Google Scholar 

  • Belgiu, M., I. Tomljenovic, T. Lampoltshammer, T. Blaschke & B. Höfle, 2014. Ontology-based classification of building types detected from airborne laser scanning data. Remote Sensing 6: 1347–1366.

    Article  Google Scholar 

  • Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65: 2–16.

    Article  Google Scholar 

  • Blaschke, T., K. Johansen & D. Tiede, 2011. Object based image analysis for vegetation mapping and monitoring. In Weng, Qihao (ed.), Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications. CRC Press, Taylor and Francis, Boca Raton, FL: 141–266.

    Google Scholar 

  • Blaschke, T., G. J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Queiroz Feitosa, F. van der Meer, H. van der Werff, F. van Coillie & D. Tiede, 2014. Geographic object-based image analysis – towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing 87: 180–191.

    Article  PubMed  PubMed Central  Google Scholar 

  • d’Oleire-Oltmanns, S., I. Marzolff, D. Tiede & T. Blaschke, 2014. Detection of gully-affected areas by applying object-based image analysis (OBIA) in the region of Taroudannt, Morocco. Remote Sensing 6: 8287–8309.

    Article  Google Scholar 

  • Definiens AG, 2007. Definiens Developer 7 – Reference Book. http://www.ecognition.cc/download/ReferenceBook.pdf.

  • Drǎguţ, L., D. Tiede & S. R. Levick, 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science 24: 859–871.

    Article  Google Scholar 

  • Dronova, I., 2015. Object-based image analysis in wetland research: a review. Remote Sensing 7: 6380–6413.

    Article  Google Scholar 

  • Dronova, I., P. Gong, N. E. Clinton, L. Wang, W. Fu, S. Qi & Y. Liu, 2012. Landscape analysis of wetland plant functional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sensing of Environment 127: 357–369.

    Article  Google Scholar 

  • Foody, G. M., 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment 80: 185–201.

    Article  Google Scholar 

  • Hauet, A., M. Muste & H.-C. Ho, 2009. Digital mapping of riverine waterway hydrodynamic and geomorphic features. Earth Surface Processes and Landforms 34: 242–252.

    Article  Google Scholar 

  • Husson, E., O. Hagner & F. Ecke, 2014. Unmanned aircraft systems help to map aquatic vegetation. Applied Vegetation Science 17: 567–577.

    Article  Google Scholar 

  • Kay, S., J. D. Hedley & S. Lavender, 2009. Sun glint correction of high and low spatial resolution images of aquatic scenes: a review of methods for visible and near-infrared wavelengths. Remote Sensing 1: 697–730.

    Article  Google Scholar 

  • Klemas, V., 2013. Remote sensing of coastal wetland biomass: an overview. Journal of Coastal Research 290: 1016–1028.

    Article  Google Scholar 

  • Laliberte, A. S., A. Rango, J. E. Herrick & E. L. Fredrickson, 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments 69: 1–14.

    Article  Google Scholar 

  • Laliberte, A. S., M. A. Goforth, C. M. Steele & A. Rango, 2011. Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments. Remote Sensing 3: 2529–2551.

    Article  Google Scholar 

  • Li, X. & G. Shao, 2014. object-based land-cover mapping with high resolution aerial photography at a county scale in midwestern USA. Remote Sensing 6: 11372–11390.

    Article  Google Scholar 

  • Lucas, M. & J. Goodman, 2014. Linking coral reef remote sensing and field ecology: it’s a matter of scale. Journal of Marine Science and Engineering 3: 1–20.

    Article  Google Scholar 

  • Lucieer, A., Z. Malenovský, T. Veness & L. Wallace, 2014. HyperUAS-Imaging Spectroscopy from a Multirotor Unmanned Aircraft System. Journal of Field Robotics 31: 571–590.

    Article  Google Scholar 

  • Luscier, J. D., W. L. Thompson, J. M. Wilson, B. E. Gorham & L. D. Drǎguţ, 2006. Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots. Frontiers in Ecology and the Environment 4: 408–413.

    Article  Google Scholar 

  • Mui, A., Y. He & Q. Weng, 2015. An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 109: 30–46.

    Article  Google Scholar 

  • Olofsson, P., G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock & M. A. Wulder, 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148: 42–57.

    Article  Google Scholar 

  • Radoux, J., P. Bogaert, D. Fasbender & P. Defourny, 2011. Thematic accuracy assessment of geographic object-based image classification. International Journal of Geographical Information Science 25: 895–911.

    Article  Google Scholar 

  • Rampi, L. P., J. F. Knight & K. C. Pelletier, 2014. Wetland mapping in the upper midwest United States. Photogrammetric Engineering & Remote Sensing 80: 439–448.

    Article  Google Scholar 

  • Roelfsema, C. M., M. Lyons, E. M. Kovacs, P. Maxwell, M. I. Saunders, J. Samper-Villarreal & S. R. Phinn, 2014. Multi-temporal mapping of seagrass cover, species and biomass: a semi-automated object based image analysis approach. Remote Sensing of Environment 150: 172–187.

    Article  Google Scholar 

  • Tiede, D., S. Lang, F. Albrecht & D. Hölbling, 2010a. Object-based class modeling for cadastre-constrained delineation of geo-objects. Photogrammetric Engineering & Remote Sensing 76: 193–202.

    Article  Google Scholar 

  • Tiede, D., S. Lang, D. Hölbling & P. Füreder, 2010b. Transferability of OBIA rule sets for IDP camp analysis in Darfur. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-4/C7.

  • Verhoeven, G., 2008. Imaging the invisible using modified digital still cameras for straightforward and low-cost archaeological near-infrared photography. Journal of Archaeological Science 35: 3087–3100.

    Article  Google Scholar 

  • Verhoeven, G. J. J., 2010. It’s all about the format – unleashing the power of RAW aerial photography. International Journal of Remote Sensing 31: 2009–2042.

    Article  Google Scholar 

  • Visser, F., C. Wallis & A. M. Sinnott, 2013. Optical remote sensing of submerged aquatic vegetation: opportunities for shallow clearwater streams. Limnologica – Ecology and Management of Inland Waters 43: 388–398.

    Article  Google Scholar 

  • Visser, F., K. Buis, V. Verschoren & P. Meire, 2015. Depth estimation of submerged aquatic vegetation in clear water streams using low-altitude optical remote sensing. Sensors 15: 25287–25312.

    Article  PubMed  PubMed Central  Google Scholar 

  • Woodget, A. S., P. C. Carbonneau, F. Visser & I. Maddock, 2015. Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surface Processes and Landforms 40: 47–64.

    Article  Google Scholar 

Download references

Acknowledgements

Funding for this project was provided by the FWO (Fund for Scientific Research)—Flanders (Belgium)—(G.0290.10) via the multidisciplinary research project ‘Linking optical imaging techniques and 2D-modelling for studying spatial heterogeneity in vegetated streams and rivers’ (Antwerp University, Ghent University, 2010–2013) and the FWO Scientific Research Community ‘Functioning of river ecosystems by plant–flow–sediment interactions’. V.V. thanks the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen) for personal research funding. J.S. is a postdoctoral fellow of FWO (Project No. 12H8616 N).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fleur Visser.

Additional information

Guest editors: M. T. O’Hare, F. C. Aguiar, E. S. Bakker & K. A. Wood / Plants in Aquatic Systems – a 21st Century Perspective

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Visser, F., Buis, K., Verschoren, V. et al. Mapping of submerged aquatic vegetation in rivers from very high-resolution image data, using object-based image analysis combined with expert knowledge. Hydrobiologia 812, 157–175 (2018). https://doi.org/10.1007/s10750-016-2928-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10750-016-2928-y

Keywords

  • Macrophytes
  • OBIA
  • Remote sensing
  • VHR image data
  • Knowledge-based