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An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis

2015, Master of Arts, University of Toledo, Geography.
Subsurface drainage from agricultural fields in the Maumee River watershed is suspected to adversely impact the water quality and contribute to the formation of harmful algal blooms (HABs) in Lake Erie. In early August of 2014, a HAB developed in the western Lake Erie Basin that resulted in over 400,000 people being unable to drink their tap water due to the presence of a toxin from the bloom. HAB development in Lake Erie is aided by excess nutrients from agricultural fields, which are transported through subsurface tile and enter the watershed. Compounding the issue within the Maumee watershed, the trend within the watershed has been to increase the installation of tile drains in both total extent and density. Due to the immense area of drained fields, there is a need to establish an accurate and effective technique to monitor subsurface farmland tile installations and their associated impacts.
This thesis aimed at developing an automated method in order to identify subsurface tile locations from high resolution aerial imagery by applying an object-based image analysis (OBIA) approach utilizing eCognition. This process was accomplished through a set of algorithms and image filters, which segment and classify image objects by their spectral and geometric characteristics. The algorithms utilized were based on the relative location
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of image objects and pixels, in order to maximize the robustness and transferability of the final rule-set. These algorithms were coupled with convolution and histogram image filters to generate results for a 10km² study area located within Clay Township in Ottawa County, Ohio.
The eCognition results were compared to previously collected tile locations from an associated project that applied heads-up digitizing of aerial photography to map field tile. The heads-up digitized locations were used as a baseline for the accuracy assessment. The accuracy assessment generated a range of agreement values from 67.20% - 71.20%, and an average agreement of 69.76%. The confusion matrices calculated a range of kappa values from 0.273 - 0.416 with an overall K value of 0.382, considered fair in strength of agreement. This thesis provides a step forward in the ability to automatically identify and extract tile drains, and will assist future research in subsurface agricultural drainage modeling.
Kevin Czajkowski (Committee Chair)
Patrick Lawrence (Committee Member)
Dan Hammel (Committee Member)
April Ames (Committee Member)
78 p.

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Johansen, R. (2015). An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Johansen, Richard. "An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis." Electronic Thesis or Dissertation. University of Toledo, 2015. OhioLINK Electronic Theses and Dissertations Center. 21 Nov 2017.

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Johansen, Richard "An Automated Approach to Agricultural Tile Drain Detection and Extraction Utilizing High Resolution Aerial Imagery and Object-Based Image Analysis." Electronic Thesis or Dissertation. University of Toledo, 2015. https://etd.ohiolink.edu/

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