Drainage Ditch Berm Delineation Using Lidar Data: A Case Study of Waseca County, Minnesota

Abstract

Within a drainage system, drainage ditches are designed to improve existing natural drainage. Although drainage ditches are mostly engineered, they can also be part of natural watercourses. For environmental sustainability, in many places there are guidelines to establish vegetative buffer strips along the boundary of drainage ditches. In this landscape planning study, a geospatial modeling framework was established to identify these drainage system landforms and the boundary that separates these landforms from their surrounding areas across Waseca County in south-central Minnesota. By employing almost 2000 GPS spot elevation measurements from five ditch systems and one-meter Light Detection and Ranging (LiDAR) derived digital elevation model (DEM) data, the drainage ditch berm polygons were delineated. Eight low light angle hillshade rasters at 45-degree azimuth intervals were used to construct the model. These hillshade rasters were combined to form a composite raster so that the effect of multiple azimuths can be captured during ditch berm delineation. The GPS points identified as the top of the berm were used to extract cell values from the combined hillshade. These cell values were modeled further using statistical distribution graphs. The statistical model derived +0.5 and +1 standard deviation values (cell values 812 and 827, respectively) of the combined hillshade raster were utilized to obtain complete berm polygons. In this semi-automated method, between 67.30% to 79.80% of ditch berm lengths were mapped with an average error that is less than the resolution of the DEM. Demarcation of these boundaries are important for local governments in Minnesota and throughout the world, as it could help guide land–water management and aid sustainable agriculture.

Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning

“…We demonstrate that our approach of combining deep learning with high-density ALS data to map drainage ditches has accuracy equal to or higher than that of previous studies. However, the studies by Graves et al (2020), Kiss et al (2015), Larson andTrivedi (2011), Rapinel et al (2015), Stanislawski et al (2018) reported only recall, which made them difficult to evaluate. Additional metrics on false positives would be helpful for a comparison with our results.…”