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UTILIZATION OF LANDSCAPE INDICATORS TO MODEL WATER QUALITY
Citation:
Smith, J H., J D. Wickham, D. Norton, T G. Wade, AND K B. Jones. UTILIZATION OF LANDSCAPE INDICATORS TO MODEL WATER QUALITY. Presented at International Association of Landscape Ecology (IALE), Tempe, AZ, April 25, 2001.
Impact/Purpose:
Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.
Description:
Many water-bodies within the United States are contaminated by, non-point source (NFS) pollution, which is defined as those materials posing a threat to water quality arising from a number of individual sources and diffused through hydrologic processes. One such NPS pollutant is fecal coliform, which is derived from animal wastes, including humans, and is most often associated with urban and agricultural areas. It is postulated that by utilizing land cover indicators those water-bodies that may be at risk of fecal coliform contamination may be identified. This study utilized land cover indicators derived from the Multi-Resolution Land Characterization (MRLC) project to analyze fecal coliform contamination in South Carolina, Also utilized were fourteen digit Hydrologic unit code (HUC) watersheds of the state, a digital elevation model, and test point data stating whether; the fecal coliform levels exceeded levels assigned in section 3 03(d) of the Clean Water Act. Proportions of the various land covers were identified within the lndividual watersheds and then analyzed using a logistic regression. The results reveal that watersheds with large proportions of urban land Cover and agriculture on steep slopes had a very high probability of being impaired. This information will allow managers to make, knowledgeable decisions on assigning conservation resources and assessing impacts of future land cover changes