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Cooperative Projects Underway at MSU Geospatial and Natural Resources Institute 11.14.2002

November 14, 2002
One month after announcing the formation of the Geospatial and Natural Resources Institute (GNRI), Mississippi State University (MSU) today unveiled a list of major cooperative research projects now underway at the new institute.

MSU formed the GNRI by merging the resources and staffs of its Remote Sensing Technologies Center (RSTC), Water Resources Research Institute (WRRI), Visualization Analysis and Imaging Laboratory (VAIL), and the Computational Geospatial Technologies Center (CGTC). The newly formed institute will enhance MSU’s ability to deliver geospatial solutions that blend the high performance computing capabilities of the CGTC and VAIL with the natural resources expertise of RSTC and WRRI.

These four organizations were involved in a broad spectrum of research, extension and educational programs that included developing, validating and applying remote sensing and geospatial technologies. These programs will continue under the auspices of the GNRI. Major endeavors include the following:

  • With the support of Mississippi’s Department of Environmental Quality (MDEQ), the GNRI is exploring methodologies to identify and validate the correlation between remotely sensed land use/land cover and agricultural activity data and probable non-point-source water quality impairment.
  • The GNRI has a role in several projects working with county governments, state legislators, Mississippi’s congressional delegation and private businesses to help plan surface water impoundments to enhance economic development opportunities. These efforts emphasize improving the local quality of life and will be integrated into Mississippi’s emerging environmental and natural resources watershed management efforts.
  • Originally a program under the WRRI, GNRI administers MSU’s participation in a multi-state, multi-university, multi-federal agency Gulf Coast Cooperative Ecosystem Studies Unit (GC-CESU). CESUs provide a new approach to develop and deliver research, technical assistance and education to support existing federal agency programs and management activities.
  • The GNRI has developed a technique using geospatial information products to manage detrimental insect populations in cotton. The precision technique reduces the cost of cotton production and has less severe environmental impacts than traditional methods of crop production. Research into this program is continuing.
  • The National Consortium on Remote Sensing in Transportation – Environmental Assessments (NCRST-E) is one of four consortia established by NASA and the U.S. Department of Transportation to lead the application of remote sensing and geospatial technologies in transportation. The primary mission of the consortium is to develop and promote the use of these technologies by transportation decision-makers and environmental assessment specialists to measure, monitor, and assess environmental conditions in relation to transportation infrastructure.
  • In forest management, the registration of LIDAR data and field measurements remains an issue in geospatial applications. To characterize the registration problems, GNRI researchers created a spatially immersive virtual environment that allows an analyst to simultaneously display surfaces constructed from remote-sensed data and field measurements. By seeing the results of registration efforts in three dimensions, the analyst is able to observe misregistration both horizontally and vertically. Based on the success of this work, the research team commenced the development of a virtual forest to study its utility in using remotely sensed data to study silvicultural parameters.
  • The spread of highly competitive detrimental species, known as invasive species, is of concern to our nation's human, livestock, and wildlife health as well as to environmental and economic stability. The GNRI is developing methods to use remotely sensed imagery to monitor the spread of invasive species. Researchers have developed automated target recognition algorithms to accurately discriminate invasive vegetative species from surrounding, subtly different vegetation. This capability, along with characterization of habitat for wildlife and disease vectors, can provide early warning of potential catastrophic infestations.