Classification and mapping of saltmarsh vegetation combining multispectral images with field data. (5th May 2020)

• Record Type: Journal Article
• Authors: Yeo, Samantha; Lafon, Virginie; Alard, Didier; Curti, Cécile; Dehouck, Aurélie; Benot, Marie-Lise
• Is Part Of: Estuarine, coastal and shelf science. Volume 236(2020)
• Publication Date: 2020-05-05
• Subjects: Arcachon bay -- Multispectral data -- Multi-temporal image classification -- Phytosociology -- Saltmarsh zonation -- Southwestern France -- Vegetation mapping -- Wetland remote sensing; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2020.106643
• Languages: English
• ISSNs: 0272-7714

Abstract

Abstract: Salt marshes are areas of high conservation value encompassing diverse ecological gradients responsible for creating unique vegetation structure and composition. In complement to the large body of studies developing vegetation mapping methods through the use of remote sensing data, we tested for the possibility of developing a cost-effective method to map salt marsh vegetation as a basis for monitoring a French Nature Reserve. Using classical multivariate ordination and cluster analyses, accurate and ecologically relevant vegetation groups matching existing typologies were determined from a vegetation database collected for management and conservation rather than mapping purposes. This resulted in six distinct vegetation groups, which were mapped through the combination of the NIR spectral band and radiometric indices (NDVI and NDWI) derived from multispectral 2 m-resolution satellite images (Pleiades images). The addition of a LiDAR-derived digital elevation model (DEM) layer was also tested. The final classified map of the reserve based only on optical layers had an overall accuracy of 75.5% (Kappa coefficient of 0.71), with varying success between the different vegetation groups. The addition of the DEM slightly decreased map accuracy to 73.6% (Kappa of 0.68). Decreasing the number of records used for map training had detectable negative effects on map accuracy. This study demonstrated that using already existing field observations combined with only a few spectral bands and radiometric indices from multi-temporal multispectral images with a high spatial resolution can be used as a basis to aid in vegetation classification and mapping of saltmarsh habitats, with the goal of monitoring their dynamics. Highlights: A combination of field and remote sensing data was used to map saltmarsh vegetation. Field vegetation records were not initially designed in mapping purpose. Rather easily acquired and processed multi-temporal multispectral imagery was used. Decreasing the number of records for map training decreased map accuracy. The addition of the DEM slightly decreased map accuracy.