Remote sensing of coral reefs and their physical environment.

Censsis Seabed: Diverse Approaches for Imaging Shallow and Deep Coral Reefs

The main task of the Center for Subsurface Sensing and Imaging Systems (CenSSIS), funded by the National Science Foundation, is to develop appropriate techniques to separate complex subsurface signals. This multi-university Engineering Research Center aims to revolutionize the existing technology for detecting and imaging objects under different layers, including underwater. The University of P...

Water Column Correction for Coral Reef Studies by Remote Sensing

Human activity and natural climate trends constitute a major threat to coral reefs worldwide. Models predict a significant reduction in reef spatial extension together with a decline in biodiversity in the relatively near future. In this context, monitoring programs to detect changes in reef ecosystems are essential. In recent years, coral reef mapping using remote sensing data has benefited fr...

Classification of remote Pacific coral reefs by physical oceanographic environment

[1] The oceanographic environment is a key element in structuring coral reef ecosystems by setting the range of physical and chemical conditions in which coral reef-builders live. A cluster analysis of physical and chemical oceanographic data is used to classify coral habitats in the remote tropical and subtropical Pacific Ocean based on average temperature, temperature seasonal cycle, nutrient...

Remote Sensing of Coral Reefs for Monitoring and Management: A Review

Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementa...

Comment on “Modelling susceptibility of coral reefs to environmental stress-using remote sensing data and GIS models”, authors Maina, Venus, McClanahan, and Ateweberhan