Airborne remote sensing and digital image analysis for habitat mapping in coastal dune systems

• Main Author: Shanmugam, S.
• Published: Swansea University 1998
• Subjects: 550
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638810

This thesis examines the application of digital multispectral remote sensing to identify, map and monitor coastal dune habitats that are under threat due to various environmental factors. In addition to multispectral image classification techniques, ground spectro-radiometry, derivative spectra and linear mixture modelling are used. With the availability, in the near future, of high spatial resolution, hyperspectral images acquired by satellite sensors, these techniques may aid in regular monitoring of coastal dunes as well as other habitats, and therefore help environmental managers to conserve biodiversity. The spectral characteristics and separability of various cover types present in the dune and slack habitats of the Kenfig NNR (National Nature Reserve) south Wales are analysed using high spectral and spatial resolution image data acquired by the CASI (Compact Airborne Spectrographic Imager) sensor. It is shown that only broad categories of the habitats can be separated using these data, and that the overall separability between habitats decreases as the number of habitats considered for analysis increases. In a similar fashion, conventional 'hard' classification techniques result in accurate mapping of only the broader categories of these habitats. Sub-community level information cannot be obtained with acceptable accuracy, due to factors such as overlapping spectra, 'fuzzy' boundaries of the habitats and communities on the ground, and the limitations imposed by the spatial and spectral resolution of the image data used. A better understanding of the spectral and habitat variables is achieved by analysing field spectra, derivative spectra, and classified digital, ground-based photography. Empirical models developed from data designed to simulate the bandsets of the Daedalus ATM (Airborne Thematic Mapper) and CASI airborne sensors suggest that the percentage cover of sand, green biomass and the dominant species and communities may be predicted with an average error estimate of 10%.