Nonpoint-source (NPS) pollution is a major cause of surface water quality degradation due to the transport of chemicals, nutrients, and sediments into lakes and streams. Vegetative buffers comprise several effective landscape best management practices (BMPs) that include vegetative filter strips (VFS) and grassed waterways. However, some BMPs are less effective due to concentrated surface flow, improper cropland-to-VFS area ratios, and surface flowpaths that partially or completely bypass vegetative buffers. The overall objective of this study was to quantify the accuracy of simulated flowpaths relative to observed and global positioning system (GPS)-assisted ground-truthed surface flowpaths for improved placement of VFS and other vegetative buffers to effectively intercept surface runoff. This study was conducted on three research sites in Rock Creek watershed in central Iowa. Geographic information system (GIS) software was used for flowpath hydrologic modeling and geospatial map comparison analysis. Digital elevation model (DEM) datasets were used for flowpath simulation and included internet-available USGS 30 m × 30 m grid (typically used to design and site VFS buffers) and light detection and ranging (LiDAR) 5 m × 5 m grid DEMs. Results from this study indicate that the LiDAR 5 m × 5 m DEM generated significantly more accurate simulated flowpaths than the USGS 30 m × 30 m DEM. These results quantitatively underscore the efficacy of using high-resolution LiDAR DEM data to more accurately determine how well surface flowpaths are intercepted by VFS and other vegetative buffers. These results also demonstrate the benefits of coupling highresolution aerial imagery with quantitative geospatial map comparison data to improve visualization and comparison of field-scale and watershed-scale hydrologic and terrestrial attributes. Ultimately, the results and procedures from this study will be applied to the development of a novel cloud-based, user-interactive, virtual-reality decision support (DS) tool that can be used to remotely assess hydrologic landscape conditions, prescribe improvements to existing BMPs, and determine new sites for enhanced BMP placement and functionality within a high-resolution 3-D imagery environment.