This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R² value of 0.567, accounting for 57 percent of the variation of LAI_SQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar).

We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon PowerShot A800 digital camera mounted on an unmanned aerial system. Image mosaicking and dense point matching were accomplished using Agisoft PhotoScan. The resulting point cloud was classified according to ground/non-ground classes and used to interpolate a high resolution DTM which was both compared to a DTM from an existing lidar dataset and used to model vegetation height for fifteen 20 × 20 m plots. Differences in the interpolated DTM surfaces demonstrate that the SfM surface overestimates lidar-modeled ground height with a mean difference of 0.19 m and standard deviation of 0.66 m. Height estimates obtained solely from SfM products were successful with R² values of 0.91, 0.90, and 0.89 for mean, median, and maximum canopy height, respectively. Use of the lidar DTM in the analyses resulted in R² values of 0.90, 0.89, and 0.89 for mean, median, and maximum canopy height. Our results suggest that SfM-derived point cloud products function as well as lidar data for estimating vegetation canopy height for our specific study area.

The American craft brewing scene has exhibited continued growth over the past several decades fueled by the desire of many patrons to opt for unique, local brews in the place of homogenous national and international brands. Previous research reports that American microbreweries often express neolocalism in the marketing of their products: using local place names, people, events, landscape features, and icons on their labeling and in their names to establish roots with the local environment and culture. By way of qualitatively surveying 1564 microbrewery websites, this paper looks through a neolocal lens to examine microbrewery usage of ethnicity and race in their marketing efforts. Microbreweries are found to express ethnicity and race in their marketing schemes to a limited extent within which ethnicity, more so than race, is demonstrated. Specific examples include references to ethnic ties to the Scots-Irish in Appalachia, specific Native American tribes throughout the country, and Latino, specifically Mexican/Mexican-American, cultural heritage in the American southwest. In addition, findings reflect the demographics of the industry, which is dominated by whites of European descent.