Long Bay hypoxia study: A collaborative and multidisciplinary approach

The nearshore waters along the Myrtle Beach area are oceanographically referred to as Long Bay. Long Bay is the last in a series of semi-circular indentations located along the South Atlantic seaboard. The Bay extends for approximately 150 km from the Cape Fear River in North Carolina to Winyah Bay in South Carolina and has a number of small inlets (Figure 1). This region of the S.C. coast, commonly referred to as the “Grand Strand,” has a significant tourism base that accounts for a substantial portion of the South Carolina economy (i.e., 40% of the state’s total in 2002) (TIAA 2003). In 2004, the Grand Strand had an estimated 13.2 million visitors of which 90% went to the beach (MBCC 2006). In addition, Long Bay supports a shore-based hook and line fishery comprised of anglers fishing from recreational fishing piers, the beach, and small recreational boats just offshore. (PDF contains 4 pages)

Collaborative training program in coastal management in the Philippines: a local initiative with a global perspective

A collaborative project in developing a broad-based coastal management training program in the Philippines is being undertaken by a group of government and nongovernment agencies. It addresses the lack of expertise in planning an implementation for coastal management in the country. The process will be documented to serve as a guide in starting and maintaining the process of collaborative training in coastal management in the region. Other training initiatives are outlined including regional and global efforts.

Real-time climate monitoring using an AVHRR-based vegetation index

A normalized difference vegetation index (NDVI) has been produced and archived on a 1° latitude by 1° longitude grid between 55°S and 75°N. The many sources of data errors in the NDVI include cloud contamination, scan angle biases, changes in solar zenith angle, and sensor degradation. Week-to-week variability, primarily caused by cloud contamination and scan angle biases, can be minimized by temporally filtering the data. Orbital drift and sensor degradation introduces interannual variability into the dataset. These trends make the usefulness of a long-term climatology uncertain and limit the usefulness of the NDVI. Elimination of these problems should produce an index that can be used for climate monitoring.