Abstract

Intertidal sediments are critically important in controlling intertidal mudflat microphytobenthic primary productivity and the functioning of intertidal ecosystems. This paper demonstrates the possibility of deriving different intertidal sediment properties from coarse-to-medium resolution remote sensing imagery. Supervised and image based classification methods were used to map different substrate types based on the Spectral Angle Mapper (SAM) algorithm. The algorithm characterized different sediment properties from remote sensing data based on field collected and image-extracted endmembers. The results demonstrate that, different substrate types can be derived from coarse-to-medium resolution images using SAM algorithm. Supervised and image-based classification methods performed well in deriving intertidal sediment properties. From the results, sand sediments cover a wide area in extent than clay whereas Normalized Difference Vegetation Index (NDVI) validation results indicate that, clay sediments have higher NDVI values as compared to sand sediments. We conclude that, intertidal sediment properties can be successfully derived from coarse-to-medium resolution satellite imagery.

Key words: Endmember, microphytobenthos, spectral signature, substrates, trios ramses, wadden sea.