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Description: The success of a Louisiana coastal restoration effort depends on locating sufficient volumes of sand and mixed sediment that are suitable for placement on beaches and dunes, and for creating/nourishing marshes. Thus, locating potential borrow sites with suitable sediment resources that are extractable at acceptable costs is crucial to the success of restoration goals (e.g., Finkl and Khalil, 2005). Sand and sediment resources in Louisiana are limited but crucial for barrier island and marsh restoration. In addition, knowledge of sediment budget and inventory is essential for regional sediment management (Khalil, 2012). To help facilitate the identification and management of nearshore, offshore and riverine sediment resources, the Coastal Protection and Restoration Authority (CPRA) developed the LouisianA SAnd Resources Database (LASARD). This database is used to manage, archive, and maintain geological, geophysical, geotechnical and other related data pertaining to the exploration of sand/sediment in various environments (Khalil et al., 2010). In LASARD, the geoscientific and related data acquired for ecosystem restoration are archived, populated, and maintained on a GIS platform. Once standardized, LASARD data are made available to users through the CPRA publically accessible spatial viewer. The overall objective of LASARD is to centralize relevant data from various sources for better project coordination and to facilitate future planning for delineating and utilizing sediment resources for a sustainable ecosystem restoration in coastal Louisiana (Khalil et al, 2010). Data collected over the course of sand and sediment search investigations were identified for incorporation into the LASARD database. The identification of sediment resources and final design of borrow areas is achieved through the integration of geophysical surveys and geotechnical investigations. Each data type incorporated into LASARD plays a unique role in delineating sediment resources and finally designing a borrow area. The resulting data are analyzed to identify the most compatible sediment for a specific restoration project while avoiding potential cultural resources, existing infrastructure and environmental impacts.

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