This work assessed the suitability of hyperspectral data for estimating mudflat surface characteristics related to stability. Due to the inaccessibility of intertidal areas, precise ground-based measurements of mudflat stability are difficult to conduct. Remote sensing can provide full spatial coverage and non-intrusive measurement. As stability changes on mudflats are linked to subtle differences in mudflat surface characteristics, they can potentially be mapped by hyperspectral data. Hyperspectral images were collected along with near contemporary ground measurements. An unsupervised classification gave a map which confirmed that a channel bar was mainly sand whereas soft mud dominated an adjacent embayment. Multiple regression analysis was used to relate surface characteristics to hyperspectral data to construct regression equations. Erosion shear stress was estimated directly from the hyperspectral data and also by a relationship with the surface characteristics. The results of the thematic class map matched well with the known situation at the site during image acquisition. The maps of surface characteristics highlighted the additional information that can be extracted from hyperspectral data. Stability maps, based on the erosion shear stress, can be used as a basis for predicting the likely future behaviour in this dynamic environment and will be of use for coastal zone management.
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