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A SEMI-AUTOMATED APPROACH FOR DETECTING AND LOCATING SWINE ANIMAL FEEDING OPERATIONS OVER REGIONAL AREAS
Citation:
Garofalo, D AND D B. Jennings. A SEMI-AUTOMATED APPROACH FOR DETECTING AND LOCATING SWINE ANIMAL FEEDING OPERATIONS OVER REGIONAL AREAS. Presented at EPA Science Forum 2004, Washington, DC, June 1-3, 2004.
Impact/Purpose:
The objectives of this task are to:
Assess new remote sensing technology for applicability to landscape characterization; Integrate multiple sensor systems data for improved landscape characterization;
Coordinate future technological needs with other agencies' sensor development programs;
Apply existing remote sensing systems to varied landscape characterization needs; and
Conduct remote sensing applications research for habitat suitability, water resources, and terrestrial condition indicators.
Description:
Surface runoff from animal feeding operations (AFO's) and its infiltration into ground water can
pose a number of risks to water quality mainly because of the amount of animal manure and wastewater they produce. Excess nutrients generated by livestock facilities can lead to algal blooms and anoxic water conditions, shellfish bed contamination, loss of water recreation activities, and possibly fish kills and human health dangers. Developing a cost-effective approach for locating and studying existing AFO's over a broad regional area is a first step to determining the spatial relationships between these facilities and water quality. A system which is capable of identifying and inventorying existing facilities and determining their geographic location and distribution with regard to other landscape features such as drainage, geology, soils, slope, and vegetation, should provide important insight for assisting farmers and planners in reducing the environmental risks associated with existing and future animal feeding operations, respectively.
Our research focused on the use of a Geographic Information System (GIS) and high spatial resolution IKONOS sateJlite data for semi-automatically detecting animal feeding operations in an area of Duplin County, North Carolina. Our results show that single-date, high resolution satellite remote sensing data (IKONOS 4-meter, multi-spectral data) combined with GIS-based semi-automated image processing and geometric analysis of swine animal feeding operation features and geography can yield overall detection accuracies of 76% for hog barns and 79% for lagoons.
Our study area is a portion of Duplin County, North Carolina. The county has experienced problems of excessive nutrients in its waterways and of the 1,359 farms in the county (based on the 1992 Census of Agriculture survey), nearly one quarter, 338, raise hogs or pigs. In total, more than one million hogs and pigs are raised in Duplin County annually.