Orthomosaics derived from consumer grade digital cameras on board unmanned aerial vehicles (UAVs) are increasingly being used for biodiversity monitoring and remote sensing of the environment. To have lasting quantitative value, remotely sensed imagery should be calibrated to physical units of reflectance. Radiometric calibration improves the quality of raw imagery for consistent quantitative analysis and comparison across different calibrated imagery. Moreover, calibrating remotely sensed imagery to units of reflectance improves its usefulness for deriving quantitative biochemical and biophysical metrics. Notwithstanding the existing radiometric calibration procedures for correcting single images, studies on radiometric calibration of UAV-derived orthomosaics remain scarce. In particular, this study presents a cost- and time-efficient radiometric calibration framework for designing calibration targets, checking scene illumination uniformity, converting orthomosaic digital numbers to units of reflectance, and accuracy assessment using in situ mean reflectance measurements (i.e. the average reflectance in a particular waveband). The empirical line method was adopted for the development of radiometric calibration prediction equations using mean reflectance values measured in only one spot within a 97 ha orthomosaic for three wavebands, i.e. red, green and blue of the Sony NEX-7 camera. A scene illumination uniformity check experiment was conducted to establish whether 10 randomly distributed regions within the orthomosaic experienced similar atmospheric and illumination conditions. This methodological framework was tested in a relatively flat terrain semi-arid woodland that is invaded by Harrisia pomanensis (the Midnight Lady). The scene illumination uniformity check results showed that at a 95% confidence interval, the prediction equations developed using mean reflectance values measured from only one spot within the scene can be used to calibrate the entire 97 ha RGB orthomosaic. Furthermore, the radiometric calibration accuracy assessment results showed a correlation coefficient r value of 0.977 (p < 0.01) between measured and estimated reflectance values with an overall root mean square error of 0.063. These findings suggest that given the entire scene being mapped is experiencing similar atmospheric and illumination conditions, then prediction equations developed using mean reflectance values measured in only one spot within the scene can be used to calibrate the entire orthomosaic in semi-arid woodlands. The proposed methodological framework can potentially be tested and adapted for use in large-scale crop mapping and monitoring in precision agriculture, land-use/land-cover classification as well as plant species delimitation, particularly for mapping widespread invasive alien plants such as H. pomanensis.