Estimating Rooftop Areas of Poultry Houses Using UAV and Satellite Images

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Poultry production requires electricity for optimal climate control throughout the year. Demand for electricity in poultry production peaks during summer months when solar irradiation is also high. Installing solar photovoltaic (PV) panels on the rooftops of poultry houses has potential for reducing the energy costs by reducing the electricity demand charges of utility companies. The objective of this research was to estimate the rooftop areas of poultry houses for possible PV installation using aerial images acquired with a commercially available low-cost unmanned aerial vehicle (UAV). Overhead images of 31 broiler houses were captured with a UAV to assess their potential for solar energy applications. Building plan dimensions were acquired and building heights were independently measured manually. Images were captured by flying the UAV in a double grid flight path at a 69-m altitude using an onboard 4K camera at an angle of −80° from the horizon with 70% and 80% overlaps. The captured images were processed using Agisoft Photoscan Professional 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 blueprint dimensions of the poultry houses. 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%. When compared to the horizontal accuracy of the same building measurements taken from readily available satellite imagery, 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. The satellite orthomosaics were more accurate for length estimations and the UAV orthomosaics were more accurate for width estimations. This disparity was likely due to the flight altitude, camera field of view, and building shape. The results proved that a low-cost UAV and photogrammetric SfM can be used to create digital surface models and orthomosaics of poultry houses without the need for survey-grade equipment or ground control points.