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Remote sensing of coastal vegetation: dealing with high species turnover by mapping multiple floristic gradients
Unberath, I.; Vanierschot, L.; Somers, B.; Van De Kerchove, R.; Vanden Borre, J.; Unberath, M.; Feilhauer, H. (2019). Remote sensing of coastal vegetation: dealing with high species turnover by mapping multiple floristic gradients. Applied Vegetation Science 22(4): 534-546. https://dx.doi.org/10.1111/avsc.12446
In: Applied Vegetation Science. Opulus Press: Uppsala. ISSN 1402-2001; e-ISSN 1654-109X
Peer reviewed article  
Available in  Authors 
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Author keywords
    airborne; detrended correspondence analysis; dune; grassland; highspecies turnover; hyperspectral; imaging spectroscopy; isopam; PartialLeast Squares Regression; salt marsh
Authors  Top 
  • Unberath, I.
  • Vanierschot, L.
  • Somers, B.
  • Van De Kerchove, R.
  • Vanden Borre, J.
  • Unberath, M.
  • Feilhauer, H.
Abstract
    Aims

    Mapping gradual transitions in plant species composition via a combination of ordination and regression from remote sensing data is becoming an established approach. However, straightforward analysis of areas with high species turnover rates may result in a loss of information since a high level of generalization is required. In this study, we investigate whether analysis of more homogeneous subsets, in contrast to processing of the complete dataset, is a viable approach to mapping multiple floristic gradients.

    Location

    The coastal nature reserve “Zwin” (Belgium).

    Methods

    The measured dataset is partitioned into more homogeneous subsets based upon species composition using hierarchical classification. The dataset and subsets are then processed separately. First, ordination is performed to extract floristic gradients in plant species composition; second, these gradients are related to airborne hyperspectral remote sensing data through regression models and mapped by projecting these models on image data. Regression validation and Mantel tests are used to compare the results within the study and to other studies.

    Results

    Hierarchical classification resulted in two homogeneous vegetation subsets. Ordination yielded four gradients in the area and all regression models compared favorably to similar studies in other areas with R² values ranging from 0.47 to 0.74. The Mantel test showed that by dividing the dataset into subsets, higher resemblance to the original vegetation data can be achieved.

    Conclusion

    We showed that mapping gradual transitions in plant species composition across multiple subsets sampled from one measured vegetation dataset is a promising approach for retrospective analysis of areas with high species turnover rates. In addition to potential improvements in performance, this complementary analysis enables mapping of additional gradients, suggesting that all conventionally predicted maps remain available, valuable, and necessary for thorough understanding of plant species composition.

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