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Identification of mangrove assemblages using very high resolution IKONOS satellite images
Van Hiel, E. (2002). Identification of mangrove assemblages using very high resolution IKONOS satellite images. MSc Thesis. VUB: Brussel. 99 pp.

Thesis info:
    Vrije Universiteit Brussel; Faculteit Wetenschappen & Bio-ingenieurswetenschappen; Vakgroep Biologie; Ecology and Biodiversity; Laboratorium voor Algemene Plantkunde en Natuurbeheer (APNA), more

Available in  Author 
    VLIZ: Non-open access 247981
Document type: Dissertation

Keywords
    Mangroves; Satellite imagery; ISW, Sri Lanka [Marine Regions]; Marine; Brackish water

Author  Top 
  • Van Hiel, E.

Abstract
    The aim of this diploma work was to make a preliminary assessment on the applicability of Ikonos satellite images in mangrove vegetation dynamics studies as a substitute and complement for aerial photographs. If at least as much information can be extracted from the images as from the aerial photographs, a valuable tool of high temporal and spatial resolution is available. The probable constraint is the lower spatial resolution of the Ikonos satellite images in comparison with aerial photographs: the panchromatic image has a spatial resolution of 4 meter and the co-registered multispectral images in the blue, green, red and near infrared part of the spectrum have a spatial resolution of 1 meter, in comparison with a spatial resolution of 30 to 40 cm for the black and white aerial photographs used before by APNA. We performed a preliminary visual analysis of the images to delineate categories in the mangrove forest and some automated supervised and unsupervised classifications. We compared the results with materials available from previous studies, which are based on the visual analysis of a black and white aerial photograph of 1994 and on fieldwork: a land-use and a vegetation map and transects with data from a fieldwork campaign in 1998. The results show that the combination of lower spatial resolution and spectral data (Ikonos satellite images) seems as valuable as the combination of high spatial resolution and panchromatic data (black and white aerial photographs) to extract information by visual analysis. Moreover, thanks to the digital character of the image data, automated classifications may be performed that, in view of the results of the preliminary classifications performed in this study, may be a valuable complementary tool next to visual analysis.

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