HR: 0800h
AN: B41A-0175    [Abstracts]
TI: Assessment of Septic System Performance Using Remote Sensing Technology
AU: Patterson, A
EM: ahpatters@radford.edu
AF: University of Mississippi, Department of Geology and Geological Engineering, 118 Carrier Hall, University, MS 38677 United States
AU: * Kuszmaul, J S
EM: kuszmaul@olemiss.edu
AF: University of Mississippi, Department of Geology and Geological Engineering, 118 Carrier Hall, University, MS 38677 United States
AU: Harvey, C
EM: charvey@nvisionsolutions.com
AF: NVision Solutions, Inc., Building 1103, Rm. 147C, Stennis Space Center, MS 39529 United States
AB: Failing and improperly managed septic systems can affect water quality and cause health problems for individuals, community residents, and wildlife. Early detection of septic system leakage and failure can limit the extent off-site contamination. State and county health agencies are typically responsible for permitting and regulating septic systems, and they rely on onsite inspection to identify malfunctioning systems. External symptoms which occur over an improperly functioning septic system can include lush or greener growth of vegetation, distress of vegetation, excessive soil moisture levels, or pooling of surface effluent. The use of remote sensing technologies coupled with attainable permit records to identify these features will enable the appropriate agencies to target problem areas without extensive field inspection. High-resolution thermal and color-infrared imagery were acquired in May 2005 for a study area in Jackson County, Mississippi, adjacent to the Gulf of Mexico. Within this coastal neighborhood known to have significant septic system failures, volunteers supplied information regarding the function of their systems by completing a survey and allowing access to their property. For each of 36 data locations, a septic system score was calculated to indicate the level of system performance. Potential predictors of system performance were derived from data obtained from installation records and data extracted from imagery. Linear regression analyses of the dataset identified the significant predictors of septic system performance, and two models have been developed and proposed for the prioritization of problem septic systems by regulatory agencies. The Drain Field Model was developed using linear regression. Vegetative Index and Normalized Differential Vegetative Index were identified as the best predictors of system performance. The model considers the maximum values of the VI and NDVI within each drain field and calculates a score for each system. The score is then, based on a threshold value, converted to 1 (suggests that system should be investigated) or 0. The efficiency of the model is 86% within the systems used in this study. The Hot Spot Model requires less input data from the user. This model highlights 'suspect' areas that should be investigated (hot spots) by recognizing clusters of pixels within a range of values for a particular vegetative index. Tree clusters, etc. may also be identified as suspect areas and must be recognized and rejected by the user. This model did not identify all of the problem areas within the study area; however, 92% of the areas identified as suspect were, in fact, observed problem areas.
DE: 0478 Pollution: urban, regional and global (0345, 4251)
DE: 0480 Remote sensing
DE: 0496 Water quality
SC: Biogeosciences [B]
MN: Fall Meeting 2005