Date of Award
Master of Science (MS)
Division of Agriculture (SAFES)
Bulent A. Koc
John P. Chastain
Little literature exists on measuring agricultural buildings with data collected from an Unmanned Aerial Vehicle (UAV) mounted aerial cameras. Survey grade tools produce highly accurate results, but with high financial and temporal costs. Satellite imagery is readily available and relatively low-cost but has low spatial and temporal resolution. Unmanned Aerial Vehicles are emerging as a balance between these traditional methods for measuring and monitoring natural and constructed environments. The objective of this study was to compare the accuracy of building measurements in the orthophotos generated from satellite and UAV imagery based on control measurements without Ground Control Points (GCP’s) or on-board survey-grade georeferencing. The rooftops of 31 broiler houses located in Oconee and Anderson Counties (South Carolina, USA) were evaluated for solar energy applications. Building plan dimensions were acquired and building heights were independently hand-measured. A DJI Mavic Pro UAV flew following a traditional double grid flight path at 69-meter altitude with a 4K-resolution camera angle of -80° from the horizon with a 70% to 80% overlap. The captured images were processed using Agisoft Photoscan Professional digital photogrammetry software. Orthophotos of the study areas were generated from the acquired 3D image sequences using Structure from Motion (SfM) techniques. Building rooftop overhang obscured building footprint in aerial imagery. To accurately measure building dimensions, 0.91 m was subtracted from building roof width and 0.61 m was subtracted from roof length based on observations of roof overhangs from poultry buildings.
The actual building widths and lengths ranged from 10.8 to 184.0 m and the mean measurement error using the UAV-derived orthophotos was 0.69% for all planar dimensions. The average error for building length was 1.66 ± 0.48 m and the average error for widths was 0.047 ± 0.13 m. Building sidewall, side entrance and peak heights ranged from 1.9 to 5.6 m and the mean error was 0.06 ± 0.04 m, or 1.2% mean error. The results proved that using consumer-grade UAV’s and photogrammetric SfM could create accurate DSM and orthomosaics of a study area at efficient use of economic and temporal resources without the use of survey grade equipment or GCPs.
When compared to the horizontal accuracy of the same building measurements taken from readily available satellite imagery, the results were mixed. The mean error in satellite images was -0.36%. The average length error was -0.46 ± 0.49 m and -0.44 ± 0.14 m for building widths. It was not possible to measure building heights using satellite image analysis. The satellite orthomosaics were more accurate for length predictions and the UAV orthomosaics were more accurate for width predictions. This disparity was likely due to flight altitude, camera field of view, and building shape. The satellite imagery had low cost and ease of access that allowed a convenient determination of structural orientation and planimetric dimensions. However, the UAV provided dependably current data, vertical dimensions, and had higher absolute accuracy useful for combining with GIS data layers from other sources. With an average flight time of 5.4 min/ha and an average GSD of 4.84 cm/pi, the results obtained from a relatively inexpensive UAV mounted camera and image analysis demonstrated sufficient accuracy for planning and monitoring purposes in agricultural applications.
The primary challenge faced by energy suppliers is forecasting and supplying hourly peak demand. Generating supply at peak demand and efficiently distributing to remote customers are vital supply-side load management practices for controlling supplier cost. This research sought to determine if poultry farms could function as rurally distributed, peak-demand photovoltaic (PV) power plants to sparsely populated areas. Unmanned Aerial Vehicles (UAV) and satellite imagery were used to examine 88 poultry farms. The typical farm consisted of four poultry houses, each 15.2 meters by 152.4 meters, oriented East/West, with a rooftop slope of 22.6º and a suitable rooftop area of 1,254 m2. The average rooftop supply of all farms was calculated and grouped into key supply categories of seasonal peak, shoulder, base, and energy. The average supply from a farm of typical size was 496 kW/hr during peak periods, 279 kW/hr during summer shoulder periods, and a contribution to base load of 425 kW/hr during summer months. The average rooftop supply estimated for all 88 farms was 59.2 MW/h during summer peak, a contribution to summer base load of 47.0 mW/hr, and total annual energy supply of 127.3 GWh/yr. Calculations of facility demand and energy use were in the range of 10-20% of gross hourly rooftop supply across time categories. This resulted in a net peak demand reduction potential of 51.6 MW/h (83%), and an annual net supply of 109.4 GWh (86%) to the grid. In light of distribution costs, the twenty-seven farms located further than 3.28 km from existing transmission lines proved the most valuable in peak demand reduction and distributing energy to rural areas. Results suggest a promising potential for distributed PV adoption for peak-shaving.
Anderson, Patrick, "Evaluating Photovoltaics in a Peak-Shaving Supply Management Role in Rural Communities" (2020). All Theses. 3380.