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Estuarine, Coastal and Shelf Science--Multi-seasonal spectral characteristics analysis of coastal sa.pdf

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Estuarine, Coastal and Shelf Science--Multi-seasonal spectral characteristics analysis of coastal sa

Estuarine, Coastal and Shelf Science 69 (2006) 217e224 /locate/ecssMulti-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China Z.G. Gao, L.Q. Zhang* State Key Laboratory of Estuarine and Coastal Research, Shanghai Key Laboratory for Urbanization and Ecological Restoration, East China Normal University, 3663 Zhongshan Road North, Shanghai, 200062, China Received 4 March 2006; accepted 18 April 2006 Available online 12 June 2006 Abstract Remote sensing technology has become the primary tool for ecological research on a large scale. The spectral characteristics of the salt marsh vegetation canopy, including four main communities, Phragmites australis community, Spartina alterniflora community, Scirpus mariqueter community and Carex scabrifolia community, were measured in the seasons of spring, summer and autumn by a ground FieldSpec Pro JR spectroradiometer, at the Chongming Dongtan Nature Reserve in Shanghai. The spectral data were converted to the reflectance curves, and their first derivative curves between 350 nm and 1000 nm were calculated. The differences in the reflectance and first derivative curves were then analyzed, with particular attention paid to the characteristics in the bands of visible, ‘‘green peak’’, ‘‘red edge’’ and near infrared. The results from this study showed that the different salt marsh communities had different and rather unique spectral characteristics during the three seasons studied. The near-ground spectral reflectance varied with the growing season, community type and its phenology. The discrim- ination ability, in terms of variations in the spectral reflectance, among the four major salt marsh communities was highest in autumn. It is pro- posed that a realistic strategy for classifying the salt marsh vegetation could be adopted by integrating and analyzing their remotely sensed images of multi-seasons. The implications of the results from this study in terms of serving as a tool for monitorin