This article examines several major bathymetry mapping methods and describes an experimental procedure of
determining seabed bathymetry from multi-source passive remote sensing data. Issues to be addressed include how to
deal with less desirable spectral data quality and the absence of in-situ water depth measurements. A case study was
presented using DigitalGlobe QuickBird and Landsat-7 ETM+ multispectral images of different dates and spatial
resolutions to determine water depth for the Beilun Estuary, China. The preliminary results have led to three findings.
First, it was feasible to use the tidal water line derived from the near-infrared bands as a good approximation of water
surface when observed tidal data is absent. Second, the reflectance ratio transform model developed by Stumpf et al.
was proven suitable for spectrally-based water depth estimation when in-situ data is absent. Finally, the data quality
problem caused by thin clouds could be effectively removed by fusing remote sensing images of two different sources.
This paper presents an unsuccessful attempt to identify different mangrove species from the DigitalGlobe's QuickBird high-resolution multispectral image data for a coastal estuary located in the north of South China Sea. A conventional supervised classification was conducted with 102 signatures trained for five cover classes, with 32 of the signatures being used to separate up to five mangrove species. The results indicated that spectral characteristics alone as provided by the QuickBird's four spectral bands were not sufficient for the discrimination among mangrove species, other information such as textual and structural characteristics of mangrove species would be needed to enhance the discrimination power. In addition, the confusion between upland forests and mangroves render a removal of uplands from the classification process. Finally, the shadow effect within the mangrove patches suggested the use of NDVI in the future classification attempts.