Freshwater marsh classification in the Lower Paraná River floodplain: an object-based approach on multitemporal X-band COSMO-SkyMed data

Rafael Grimson; Natalia Soledad Morandeira; Maira Patricia Gayol; Patricia Kandus

doi:10.1117/1.JRS.13.014531

28 March 2019 Freshwater marsh classification in the Lower Paraná River floodplain: an object-based approach on multitemporal X-band COSMO-SkyMed data

Rafael Grimson, Natalia Soledad Morandeira, Maira Patricia Gayol, Patricia Kandus

Author Affiliations +

Funded by: Nacional de Promoción Científica y Tecnológica (National Agency for Science and Technology), Neotropical Grassland Conservancy, Comisión Nacional de Actividades Espaciales, Agenzia Spaziale Italiana (Italian Space Agency)

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 014531 (March 2019). https://doi.org/10.1117/1.JRS.13.014531

Abstract

A multitemporal approach to discriminate freshwater macrophyte vegetation types in the Lower Paraná River floodplain is addressed. During a low-intensity flood pulse, seven X-band HH COSMO-SkyMed HImage images are acquired, covering a nine-month period. Scenes are segmented with a mean-shift segmentation algorithm. Objects are classified with an expectation maximization algorithm into clusters with different temporal signatures and are assigned to six information classes: water, bulrush marshes, short broad-leaf marshes, tall broad-leaf marshes, short grasslands and grass marshes, and tall grasslands and grass marshes. Class interpretation is based on backscatter dynamics, with focus on their correlation with hydrometric water level measured in the Paraná River and/or with the floodplain area covered by water as estimated with a normalized difference vegetation index threshold criterion. The obtained product has a global accuracy of 75.4% and a kappa index of 67.2%. We point out the usefulness of X-band for flood monitoring and macrophyte vegetation type discrimination. However, we find limitations for the discrimination between high-biomass vegetation targets, such as tall broad-leaf marshes and tall grasslands. In a mosaic of herbaceous wetlands, the knowledge on the relation between vegetation and floods is essential for interpreting and predicting how backscattering coefficients and other synthetic aperture radar-derivated parameters vary with flooding.

Citation Download Citation

Rafael Grimson, Natalia Soledad Morandeira, Maira Patricia Gayol, and Patricia Kandus "Freshwater marsh classification in the Lower Paraná River floodplain: an object-based approach on multitemporal X-band COSMO-SkyMed data," Journal of Applied Remote Sensing 13(1), 014531 (28 March 2019). https://doi.org/10.1117/1.JRS.13.014531

Received: 23 May 2018; Accepted: 8 March 2019; Published: 28 March 2019

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