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(2003). Giant glacial grooves detected on Landsat ETM+ satellite imagery. International Journal of Remote Sensing: Vol. 24, No. 5, pp. 905-910.

Although relatively easy to distinguish the spectra of bleached and living coral, once corals have died their skeletons remained bleached (white) for only a short period. Rapid colonisation by algae can give rise to pigmentation that may be similar to that of living coral. Thus, by the time remotely sensed imagery has been acquired, discrimination of live and dead corals is no longer facile. Field measurements of spectral reflectance of live and algal-colonised dead corals (arising from different mortality events) were made in French Polynesia. Derivative analysis revealed wavelengths and slope characteristics that could be used to discriminate between mortality states with an accuracy of ∼ 85%. These results encourage application of hyperspectral remote sensing to quantitatively assess the extent of coral bleaching events.

The work described in this paper is concerned with the analysis of AVHRR data which visually exhibit ship trail clouds. The aim was to devise an automatic means of identifying these features. The existence of ship trail clouds has been known for some time, though the exact mechanism for their formation is not yet fully understood. They are normally associated with low-level stratus and stratocumulus cloud and are thought to be caused by the presence of condensation nuclei emanating from the ship exhaust funnel. Given the right conditions, these trails can be easily seen in both visible and infrared imagery and under calm conditions they can exist for several days. Edge-detection analysis was found to be unsatisfactory for identification purposes and hence a neural approach was adopted. The neural analysis was shown to be very promising. Application of a neural network trained on four images resulted in the detection of the presence of most of the trails in the dataset on which it was trained. Just as impo...

With the increasing utilization of multi-spectral imaging sensors, automatic identification of spectral signatures would be an invaluable facility. This paper describes a detailed investigation into the suitability of singular value decomposition (SVD) for spectral identification. The ability of SVD to distinguish between several different spectra is assessed, as is its stability in the presence of noise and its capacity to identify combinations of spectra correctly. These results are compared with previously-reported work using neural networks and genetic algorithms.

Different approaches to the classification of remotely sensed data of mangroves are reviewed, and five different methodologies identified. Landsat TM, SPOT XS and CASI data of mangroves from the Turks and Caicos Islands, were classified using each method. All classifications of SPOT XS data failed to discriminate satisfactorily between mangrove and non-mangrove vegetation. Classification accuracy of CASI data was higher than Landsat TM for all methods, and more mangrove classes could be discriminated. Merging Landsat TM and SPOT XP data improved visual interpretation of images, but did not enhance discrimination of different mangrove categories. The most accurate combination of sensor and image processing method for mapping the mangroves of the eastern Caribbean islands is identified.

Classification accuracy of coral reefs can be increased significantly by compensation for light attenuation in the water column and contextual editing to account for generic patterns of reef distribution. Both processes are easily implemented and collectively constitute an accruement in accuracy of 22 per cent for airborne multispectral imagery (CASI) and up to 17 per cent for satellite sensor imagery, for each extra days effort using the technique (up to maximum accuracies of 89 and 73 per cent respectively).

(1997). Cover Mapping and measurement of tropical coastal environments with hyperspectral and high spatial resolution data. International Journal of Remote Sensing: Vol. 18, No. 2, pp. 237-242.

With the increasing utilization of multi-spectral imaging sensors, automatic identification of spectral signatures would be an invaluable facility. Conventional, linear approaches have been shown t...

Advanced Very High Resolution Radiometer (AVHRR) data have been examined with a view to assessing their suitability for identifying and mapping the systematic patterns left on the landscape by the glaciation of continental areas. The study was carried out with data from a region of southern Finland. Following a print-based study, digital data were obtained and it was found that band 2 is particularly useful for the recognition of lineation patterns, band 3 for identifying major morainic features and principal component 2 for discriminating radial lineation patterns. The major glacial features for the study area were plotted from the AVHRR data and they confirmed interpretations previously constructed from ground data and aerial survey methods. It is envisaged that AVHRR could be used successfully for mapping glacial geological evidence over a wide area and hence for the reconstruction and modelling of former ice-sheet patterns.