Coral reefs in the Xisha Islands （also known as the Paracel Islands in English）, South China Sea, have experienced dramatic declines in coral cover. However, the current regional scale hard coral distribution of geomorphic and ecological zones, essential for reefs management in the context of global warming and ocean acidification, is not well documented. We analyzed data from field surveys, Landsat-8 and GF-1 images to map the distribution of hard coral within geomorphic zones and reef fiat ecological zones. In situ surveys conducted in June 2014 on nine reefs provided a complete picture of reef status with regard to live coral diversity, evenness of coral cover and reef health （live versus dead cover） for the Xisha Islands. Mean coral cover was 12.5% in 2014 and damaged reefs seemed to show signs of recovery. Coral cover in sheltered habitats such as lagoon patch reefs and biotic dense zones of reef flats was higher, but there were large regional differences and low diversity. In contrast, the more exposed reef slopes had high coral diversity, along with high and more equal distributions of coral cover. Mean hard coral cover of other zones was 〈10%. The total Xisha reef system was estimated to cover 1 060 km2, and the emergent reefs covered -787 km2. Hard corals of emergent reefs were considered to cover 97 km2. The biotic dense zone of the reef flat was a very common zone on all simple atolls, especially the broader northern reef flats. The total cover of live and dead coral can reach above 70% in this zone, showing an equilibrium between live and dead coral as opposed to coral and algae. This information regarding the spatial distribution of hard coral can support and inform the management of Xisha reef ecosystems.
The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many fields including fisheries,the military,and industry.Satellite-derived SST images have been used widely for front detection,although these data are susceptible to influence by many objective factors such as clouds,which can cause missing data and a reduction in front detection accuracy.However,front detection in a single SST image cannot fully reflect its temporal variability and therefore,the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time.In this paper,an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more effectively.Through experiments based on MODIS Terra and Aqua data,we verified that fronts could be distinguished better by using the proposed algorithm.Additionally through its use,we analyzed the monthly variations of fronts in the Bohai,Yellow,and East China Seas,based on Terra data from 2000 to 2013.
