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Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis OBIA of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.

A thematic map of benthic habitat was produced for a coral reef in the Republic of Palau, utilizing hydroacoustic data acquired with a BioSonics DT-X echosounder and a single-beam 418 kHz digital transducer. This article describes and assesses a supervised classification scheme that used a series of three discriminant analyses DAs to refine training samples into end-member structural and biological elements utilizing E1′ leading edge of first echo, E1 trailing edge of first echo, E2 complete second echo, fractal dimension first echo shape and depth as predictor variables. Hydroacoustic training samples were assigned to one of six predefined groups based on the plurality of benthic elements sand, sparse submerged aquatic vegetation SAV rubble, pavement, rugose hardbottom, branching coral that were visually estimated from spatially co-located ground-truthing videos. Records that classified incorrectly or failed to exceed a minimum probability of group membership were removed from the training data set until only ‘pure’ end-member records remained. This refinement of ‘mixed’ training samples circumvented the dilemma typically imposed by the benthic heterogeneity of coral reefs, that is either train the acoustic ground discrimination system AGDS on homogeneous benthos and leave the heterogeneous benthos unclassified, or attempt to capture the many ‘mixed’ classes and overwhelm the discriminatory capability of the AGDS. It was made possible by a conjunction of narrow beam width 6.4° and shallow depth 1.2 to 17.5 m, which produced a sonar footprint small enough to resolve the microscale features used to define benthic groups. Survey data classified from the third-pass training DA were found to: i conform to visually apparent contours of satellite imagery, ii agree with the structural and biological delineations of a benthic habitat map BHM created from visual interpretation of IKONOS imagery and iii yield values of benthic cover that agreed closely with independent, contemporaneous video transects. The methodology was proven on a coral reef environment for which high-quality satellite imagery existed, as an example of the potential for single-beam systems to thematically map coral reefs in deep or turbid settings where optical methods are not applicable.

Specular reflection of solar radiation on non-flat water surfaces is a serious confounding factor for benthic remote sensing in shallow-water environments. This problem was recently overcome by Hochberg et al., who provided an effective method for the removal of 'sun glint' from remotely sensed images by utilization of the brightness in a near-infrared (NIR) band. Application of the technique was shown to give an increase in the accuracy of benthic habitat classification. However, as presented, the method is sensitive to outlier pixels, requires a time-consuming masking of land and cloud, and is not formulated in a manner leading to ease of implementation. We present a revised version of the method, which is more robust, does not require masking and can be implemented very simply. The practical approach described here will hopefully expedite the routine adoption of this effective and simple technique throughout the aquatic remote sensing community.

This paper uses the Special Issue of the International Journal of Remote Sensing on Remote Sensing of the Coastal Marine Environment to highlight recent advances in knowledge of remote sensing of the coastal zone and to define a series of priorities where future research into the application should be addressed. Advances were identified in the benefit of high spatial and spectral resolution data and complementary remote sensing techniques (e.g. optical and acoustic, optical and Synthetic Aperture Radar (SAR)). Further benefits are identified in rapid and more frequent data acquisition, faster and more automated processing and a greater sampling intensity over conventional field-based techniques. Issues associated with adoption of remotely sensed data for management are discussed. Research priorities include the need for improved understanding and description of biotope classes and the functional interpretation of biotope maps and continued developments in understanding the radiative transfer properties of coastal environments. New knowledge is required on spatial and temporal variations of water column optical properties and its constituents. Methods for the best approaches to processing hyperspectral data require further investigation, as does the need for further testing of hyperspectral sensors for bottom type discrimination using data obtained at space-borne altitudes. Areas of value which continue to remain poorly investigated include the improvements to be gained from synergistic use of multi-wavelength remote sensing approaches, change detection techniques and multi-temporal comparisons and knowledge-based approaches to improve classification. The importance of specifically dedicated coastal zone sensors is discussed, as is alternative means of deployment (e.g. International Space Station (ISS) and Un-inhabited Aerial Vehicles (UAVs)). The potential role of airborne digital photography for marine mapping is highlighted. The lack of accurate near-shore bathymetric data is identified as a key limitation in the application of geospatial data to coastal environments.

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.

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.