The high importance of bathymetric character for many processes on reefs means that high-resolution bathymetric models are commonly needed by marine scientists and coastal managers. Empirical and optimisation methods provide two approaches for deriving bathymetry from multispectral satellite imagery, which have been refined and widely applied to coral reefs over the last decade. This paper compares these two approaches by means of a geographical error analysis for two sites on the Great Barrier Reef: Lizard Island (a continental island fringing reef) and Sykes Reef (a planar platform reef). The geographical distributions of model residuals (i.e., the difference between modelled and measured water depths) are mapped, and their spatial autocorrelation is calculated as a basis for comparing the performance of the bathymetric models. Comparisons reveal consistent geographical properties of errors arising from both models, including the tendency for positive residuals (i.e., an under-prediction of depth) in shallower areas and negative residuals in deeper areas (i.e., an over-prediction of depth) and the presence of spatial autocorrelation in model errors. A spatial error model is used to generate more reliable estimates of bathymetry by quantifying the spatial structure (autocorrelation) of model error and incorporating this into an improved regression model. Spatial error models improve bathymetric estimates derived from both methods.

Coral reef environments support high levels of marine biodiversity, they are important sites for coastal habitation and they provide a range of goods and ecosystem services such as nearshore fisheries, economic revenue from tourism and breeding sites for seabirds and turtles. Mapping is a fundamental activity that underpins our understanding of coral reef environments and helps to shape policies in resource management and conservation. This is particularly the case for quantifying the area of landcover types associated with reef environments, including coral patches, seagrasses and mangroves, but also for monitoring how these change over time and modelling how spatial patterns apparent on reefs are related to environmental drivers. Field techniques and aerial photography have historically played a crucial role in mapping coral reef environments, which has recently seen a transition toward the processing of satellite remote sensing images. This paper examines a series of maps produced of Low Isles, the most mapped island on the Great Barrier Reef, to review historical methods for mapping coral reefs because of the critical importance of understanding how past maps were made, which determines appropriate uses to which they can be put. Recent advances and future opportunities for the application of mapping technologies to coral reefs are also evaluated, including the use of unmanned aerial vehicle (UAV) platforms for airborne surveys, delivery of information through web-based platforms and improvements in the quality of information for making and presenting maps. Maps have transformed the way we have responded to both historic and contemporary coral reef problems. This timely review communicates how maps, and the fast growing technologies that are employed to produce them, are central to our understanding of coral reef environments. Recent advances that may drive exciting new environmental management tools are identified.

Spurs and grooves (SaGs) are a common and important feature of coral reef fore slopes worldwide. However, they are difficult to access and hence their morphodynamics and formation are poorly understood. We use remote sensing, with extensive ground truthing, to measure SaG morphometrics and environmental factors at 11,430 grooves across 17 reefs in the southern Great Barrier Reef, Australia. We revealed strong positive correlations between groove length, orientation and wave exposure with longer, more closely-spaced grooves oriented easterly reflecting the dominant swell regime. Wave exposure was found to be the most important factor controlling SaG distribution and morphology. Gradient of the upper reef slope was also an important limiting factor, with SaGs less likely to develop in steeply sloping (> 5°) areas. We used a subset of the morphometric data (11 reefs) to statistically define four classes of SaG. This classification scheme was tested on the remaining six reefs. SaGs in the four classes differ in morphology, groove substrate and coral cover. These differences provide insights into SaG formation mechanisms with implications to reef platform growth and evolution. We hypothesize SaG formation is dominated by coral growth processes at two classes and erosion processes at one class. A fourth class may represent relic features formed earlier in the Holocene transgression. The classes are comparable with SaGs elsewhere, suggesting the classification could be applied globally with the addition of new classes if necessary. While further research is required, we show remotely sensed SaG morphometrics can provide useful insights into reef platform evolution

Hydrographers have traditionally referred to the nearshore area as the ‘white ribbon’ area due to the challenges associated with the collection of elevation data (elevation hereafter refers to both topography and bathymetry) in this highly dynamic transitional zone between terrestrial and marine environments. Accordingly, available information in this zone is typically characterized by a range of data sets from disparate sources. In this article, we propose a framework to fill the white ribbon area of a coral reef system by integrating multiple elevation data sets acquired by a suite of remote-sensing technologies into a seamless digital elevation model (DEM). A range of data sets are integrated, including field-collected global positioning system (GPS) elevation points, topographic and bathymetric light detecting and ranging (lidar), single and multibeam echosoundings, nautical charts, and bathymetry derived from optical remote-sensing imagery. The proposed framework ranks data reliability internally, thereby avoiding the requirements to quantify absolute error and results in a high-resolution, seamless product. Nested within this approach is an effective spatially explicit technique for improving the accuracy of bathymetry estimates derived empirically from optical satellite imagery through modelling the spatial structure of residuals. The approach was applied to data collected on and around Lizard Island in northern Australia. Collectively, the framework holds promise for filling the white ribbon zone in coastal areas characterized by similar data availability scenarios.