Spectral unmixing ofcompact airborne spectrographic imager (CASI) data for quantifying sub-pixel proportions of biophysical parameters in a coastal dune system

Abstract

In environmentally sensitive and large coastal dune systems, identification and mapping of favourable (sandy), and unfavourable (scrub rich) habitats form the key to coastal conservation and management. In highly mixed floral environments, however, such an identification is difficult with low resolution multispectral imagery. In such cases, spectral unmixing is useful to resolve the “mixed pixel” effects. The Kenfig NNR (National Nature Reserve), South Wales. UK, bestowed with high biodiversity, is facing loss of successionally young slack habitats due to dune stabilisation and vegetation succession. To map such habitats, linear spectral unmixing of airborne MSS (CASI-Compact Airborne Spectrographic Imager) data was performed using the Constrained Least Square (CLS) method, and the sub-pixel proportions of the spectral end members viz. sand, vegetation and shade/moisture were defined. Comparison of the estimated fractions with the growth forms of the dominant vegetation species Salix repens (Creeping willow) with classified digital aerial photographs shows a positive correlation, thus giving us confidence in the mixture modelling technique. Apart from aiding in conservation management, such a fuzzy classification of multi-date imagery helps to delineate sandy and vegetated areas for change detection and landscape/ habitat succession studies.