Welcome to the Khaled bin Sultan Living Oceans Foundation Reef Maps Geographic Information System. We are confident that you will find it to be a treasure trove of useful information for scientific analyses, education and resource management applications. These coral reef map products are the result of years of work by an extended team of dedicated scientists and many months of field research in remote regions of the world. We are particularly grateful to our long-standing partnership with Dr. Sam Purkis’ remote sensing lab at the NOVA Southeastern University Oceanographic Center. From the satellite acquisition process, to ground-truthing field work, to creating the habitat maps and bathymetry products, Dr. Purkis’ lab is world-class. Additionally, this magnificent web application was created by an outstanding project management team from Geographic Information Services, Inc (GISi). GIS, Inc. was an absolute pleasure to work with on this exciting project. We also place high value on our relationship with DigitalGlobe, Inc. They have been our primary satellite imagery source provider and enthusiastically support our coral reef mapping mission. Finally, we’re indebted to His Royal Highness Prince Khaled bin Sultan for his ocean leadership, his dedicated support to ocean conservation and his generous funding of the Living Oceans Foundation. The freely accessible mapping products represented by this web utility are the fruits of Prince Khaled’s vision, passion for ocean conservation, and generosity.

End User License Agreement

By using this Khaled bin Sultan Living Oceans Foundation Reef Maps Geographic Information System (KSLOF Reef Maps GIS) or downloading associated data, you agree to be bound by the terms of the EULA agreement which will be displayed when you begin the download data process. In the spirit of our public benefit role, and to preserve the integrity of our intellectual property, you are obligated to acknowledge, in writing, that the data or files subject to the EULA License Agreement are used courtesy of the Khaled bin Sultan Living Oceans Foundation in all derivative products created from our data and maps.

Why Do We Map Coral Reefs

The world’s coral reefs are in serious decline. The reasons for this modern “coral reef crisis” are multifold and complex. Overharvesting of coral reef resources, sedimentation, and pollution have been chronic sources of stress on coral reefs for hundreds of years. Coral bleaching and ocean acidification, resulting from our rapidly changing climate, are and will continue to put massive stress on these deteriorating ecosystems. There is a sense of urgency in creating a global, high resolution inventory of coral reefs. After all, natural resource management (i.e. conservation) must be supported by comprehensive knowledge of the resources one desires to manage. Traditional boat-based and scuba surveys used to accomplish this objective are inefficient and insufficient (labor intensive, slow and exorbitantly expensive). Remote sensing (using airborne and space optical sensors) has emerged and has matured as a technology which is efficient, effective, and relatively cost effective. We have embraced remote sensing as a cornerstone of our coral reef survey methodology.

Optical Remote Sensing

Remote sensing techniques enable the study of poorly accessible areas and compliment in situ diver observations. In particular, an optical approach to coral reef mapping has the ability to characterize incredibly large areas with relatively little field effort. The products presented in this KSLOF Reef Maps GIS were primarily derived from the passive multispectral QuickBird and WorldView 2 satellites and, to a lesser extent, from the CASI and AISA Eagle airborne hyperspectral sensors. The QuickBird and WorldView-2 satellites (DigitalGlobe, Inc.) are able to rapidly acquire very high resolution, multispectral imagery over large areas, thus making them very suitable to the large-scale mapping projects. The spatial resolution of QuickBird is approximately 60 cm (pan-chromatic, 1 band) and 2.4 m (4-band multispectral: Red, Blue, Green, and Near Infra-red) spanning a wavelength range of 450-900 nm. Worldview-02 extends the capabilities of QuickBird by covering a slightly larger spectrum (400-1050 nm) using an additional 4 bands (Coastal, Yellow, Red-edge, and Near Infra-red 2) with finer spatial resolutions of 46 cm and 2 m for the pan-chromatic and multispectral bands, respectively. Additionally, both satellites' swath widths, the width of each acquisition, covers more than 16 km at nadir (i.e., when looking straight down). The majority of maps presented in the KSLOF Reef Maps GIS were derived from QuickBird and WorldView-2 imagery. The Seychelles (Amirantes Group) and Red Sea Farasan Islands maps were produced using CASI 550 (Itres, Inc.) hyperspectral data, collected using the Golden Eye seaplane.


Ground-truthing is the collection of the field data needed to accurately interpret remotely-sensed data. The data collection process involved a survey vessel visiting representative locations throughout a study site to gather photographs and videos to characterize the seafloor while a single-beam acoustic sounder simultaneously acquired depth estimates. The geographic position of each video, photograph, and depth sounding was assigned via differential GPS at the time of collection. In the lab, map producers used the field data to calibrate computer algorithms that derive seafloor characteristics from the satellite and hyperspectral imagery and to validate the interpretation and analysis of the geospatial products. The single-beam sonar was mounted vertically to a survey vessel and provided depth soundings directly below a vessel. The transponder emitted a sound-pulse five times per second, collecting depth information with an accuracy of ± 0.2 m to a max operating depth of 60 m. A more advanced hydrographic survey-grade acoustic sounder was used beginning in 2009. The new sounder also operated at 200 kHz, although it emitted an acoustic pulse ten times per second and delivered an accuracy of ± 0.1 m to an operating depth of 200 m. These devices were linked to a high accuracy dGPS unit, and run continuously during the ground-truthing process. The survey vessel’s track was determined by the pre-acquired satellite imagery to ensure representation of habitat types and a comprehensive range of depth values across a variety of seafloor types. Divers and surveyors documented the benthic communities and geologic structures using a Remote Operated Vehicle (ROV), a drop-cam, and scuba surveys. The drop-cam, a camera tethered to the survey vessel and plugged in a computer, enabled videos of the seafloor to be gathered by the ground-truthing team across a large geographic extent. At each location, an operator lowered the camera over the side of the survey vessel to a meter above the seafloor. A second team member viewed the camera's live feed on the laptop and directed the operator in repositioning the drop-cam to enable an optimal recording of the seafloor. Videos have the time, locations, and date of recording stamped into them, and they ranged in length from 10 seconds to 2 minutes depending on the benthos' intricacy. Videos and photographs acquired by divers provided greater detail on the benthos than the drop-cam videos though fewer locations could be visited in a day. The dive team surveyed fish and benthic communities using standard sampling protocols, such as photo-transects. During their surveys, team members acquired high definition videos and photographs documenting the seafloor character.

Classification Scheme

Benthic habitat maps display the distribution and extent of different seafloor communities within a study site. Each class represents a distinct habitat defined by gross topographic structure, biotic community, and sediment/hardground composition. Considering the geographic scope of our work, the classification scheme needed to be both coarse enough to facilitate rapid mapping, yet detailed enough to capture the range of habitats encountered throughout the study regions. Videos, photographs, and field notes collected during surveys documented the encountered habitat classes and allowed team members to define the distinguishing characteristics of each class. The knowledge gathered from the field enabled producers to train computer algorithms to classify pixels with a remotely-sensed image based on their spectral properties. An additional step of contextual editing built upon the algorithms' initial classification.


Light emitted by the Sun penetrates the water column illuminating the seafloor and providing a source of light for photosynthesis. While traveling through the water column from the water's surface to the seafloor, light is absorbed and scattered (i.e., attenuated) by water, dissolved minerals, organic matter, and phytoplankton. Further, attenuation occurs at different rates with red wavelengths (~700 nm) degrading quicker than green (~600 nm) and blue (~450 nm) light. This behavior of light within the water column is both a blessing and a curse. The different attenuation rates between spectral bands in multispectral imagery allows water depth in each pixel to be estimated by comparing each band's brightness. Simultaneously, light attenuation limits observation of the seafloor to areas shallower than 30 m (~100 ft) in the clearest water, such as those found on coral reefs. Bathymetric maps are visual representations of the seafloor topography. They were derived using a combination of physical and statistical models. The physical models estimate water properties within a scene allowing each spectral value to represent a real-world value, such as radiance or reflectance. The brightness values for each image band are compared with the depth values gathered during ground-truthing surveys creating a statistical model to predict water depth. The model predicts depths throughout the entire image including the areas where water depth was not recorded while surveying.

A GIS-ready map product

A geographic information system (GIS) is a software application for capturing, displaying, storing, managing and analyzing digital geo-spatial data. Habitat classifications were converted to GIS-ready vector-based map products using remote sensing and GIS software. Pixel-based products, termed ‘rasters’, were converted into vector-based data. Under this system of storage, clumps of adjacent pixels that comprise a single patch of habitat are grouped as a single vector shape or polygon. Because only information relating to the boundary coordinates of the polygon is stored, such data is less intensive and easier to use for a number of applications. So called ‘shape files’ are easily integrated with web-based geographic media for distribution. Attributes may be subsequently appended to a habitat polygon. Aside from a description of the relevant habitat class, such attributes might include geometric measures, for example, area or perimeter of the habitat patch; measures of environmental context, such as distance from shore or distance from an urban center; measures of human use, for instance fishing pressure across the habitat patch or recreational scuba; localized environmental data, including meteorological measurements, water depth across the polygon, water temperature, or results from fine-resolution seafloor survey. Coastal systems are also the venue for many layers of human use: fisheries, tourism, mineral and oil extraction, commercial sea traffic, to name but a few. Based on observations from around the world, it is recognized that terrestrial processes, both human and natural, affect coastal and offshore systems. Sediments and pollution from inland runoff, along with increased nutrient loads are being delivered to ocean. Because GIS supports both a data storage and data exploration, information from such seemingly disparate sources can be brought together and analyzed within their true spatial context. A GIS-ready habitat map represents the precursor to a more synoptic view of the marine system. It allows for more enhanced ecological investigation, but beyond this improved marine spatial planning and management. In short, anything that can be measured and appended with a spatial coordinate can be brought into a GIS. As a GIS-ready product, the marine habitat data presented in this Atlas are primed for more in-depth exploration.