AbstractsGeography &GIS

The purpose of this research is to develop a method that will identify subsurface tile drainage systems in agricultural areas. Subsurface tile drainage systems allow ground water to drain out of a field in order to control the water level but they also allow nutrients such as nitrogen and phosphorous to be drained into surrounding waterways, affecting the water quality in negative ways. These subsurface drainage systems are common in the Midwest because they were used as the primary land drainage strategy when developing the land for agricultural uses. Many of the tile drain locations are not known because of the age of the systems, change in land ownership, or the lack of documentation during installation. Due to research that indicates their potential impact on surface water and new developments in sustainable agriculture practices, it is important to locate and document the existence of subsurface tile drainage systems. This research project focused on Wood County which is a predominantly agricultural area located in Northwest Ohio and covers a large portion of the Maumee River Watershed. Aerial photographs of Wood County with one meter spatial resolution collected through the United States Department of Agriculture’s National Agricultural Imagery Program were used for this research. Moisture retained in soil that is not drained shows up as dark in the imagery while drier soil, such as that above the subsurface tile drainage lines, has a lighter reflectance. Remote sensing software was used to extract the edges between light and dark soils that indicate the presence of subsurface tile drainage systems. The results of the detection process showed the most discernable tile patterns in the 2005 imagery, with similar results in the 2006 imagery. The tile lines were detected evenly across all eleven areas of interest in Wood County which was expected. The process was only able to validate 13.5 percent of detected tile drains, leaving room for additional research to increase the accuracy. Crop cover, tillage practice, and soil classification were analyzed in relation to the presence of subsurface tile drainage systems to create a holistic perception of when and where tile drains can be detected. Soybean fields yielded the highest amount of tile drain lines with corn fields in a close second. Tile detection in relation to tillage practices was overwhelmingly biased towards fields that were not tilled. Tile line detection on soil classifications was consistent throughout the three years of imagery, matching the soil type predictions based on drainage characteristics.