GIS-based modeling to evaluate aquatic habitats in the lower Mississippi River, southeastern United States

Andrew C. Miller; Mark D. Farr; Michael Bishop; Donald Williams

doi:10.1117/12.514290

24 February 2004 GIS-based modeling to evaluate aquatic habitats in the lower Mississippi River, southeastern United States

Andrew C. Miller, Mark D. Farr, Michael Bishop, Donald Williams

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Proceedings Volume 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V; (2004) https://doi.org/10.1117/12.514290
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

In a study conducted for the U.S. Army Corps of Engineers, Mississippi Valley Division (MVD), we used ArcGIS software to interpolate, analyze, and display spatially explicit data describing fish and physical habitat factors (bathymetry, current velocity, and substratum) associated with a dike notching project in Bondurant towhead secondary channel in the lower Mississippi River between River Miles 390 - 394. Data were collected throughout project areas using hydroacoustic equipment. We used ArcGIS to interpolate coverages of each physical habitat variable, which were then compared with fish distribution data to determine patterns of habitat association. After analyzing data from several locations, we concluded that bathymetry, water velocity, and substrate composition were most variable in areas immediately behind dike notches. However, the habitat diversity associated with notches was limited throughout the remaining portion of each project location. Data collected from throughout the side channel were analyzed. Habitat diversity (i.e., bathymetry, current velocity, and substratum) was greatest in areas of immediate proximity with the notched dike. However, the lack of pre-notching data precluded a direct quantification of how dike-notching activities changed habitat quality.

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Andrew C. Miller, Mark D. Farr, Michael Bishop, and Donald Williams "GIS-based modeling to evaluate aquatic habitats in the lower Mississippi River, southeastern United States", Proc. SPIE 5232, Remote Sensing for Agriculture, Ecosystems, and Hydrology V, (24 February 2004); https://doi.org/10.1117/12.514290

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KEYWORDS

Animal model studies

Raster graphics

Geographic information systems

Data modeling

Data analysis

Tin

Biological research

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