## 4 Discussion  
After applying the Taguchi DOE Method to optimize the photogrammetric flights that are developed with UAVs to generate the information required for the metric virtualization of archaeological sites, several interpretations regarding the results obtained can be presented:  
* Had Taguchi DOE Method been selected, the noise and control parameters or factors have been selected. During the development of this research, the different levels among which the control parameters range were developed. Had the height of flight (15-45-75 m), the forward (60-70-80%) and lateral (60-70-80%) overlaps, and the inclination of the camera with respect to the nadir (0-30-60\({}^{\circ}\)) been selected, an experiment with nine photogrammetric flights was proposed. According to the accuracy of each model, the one with the highest variability, and therefore, the one that can be considered as optimal, and the incidence of each factor were determined. * The results support that the most suitable parameters for the flights applied for the generation of models that allow a subsequent metric virtualization of objects/ territories in similar archaeological sites to the one considered in this research are a height of flight of 15 m, forward and lateral overlaps of 80% (it must be noticed that the variation in the variability of both recoveries for flights with 70% and 80% is very little, which allows adopting the first value to reduce costs while maintaining quality), and an inclination of the sensor of 0\({}^{\circ}\) (nadiral). These results become the methodological proposal for the dimensioning of photogrammetric flights with UAVs whose aim is the generation of accurate 3D models with low-cost models. This is the main scientific contribution of this research, since none have been found during the bibliographical review. This highlights the relevance of this proposal. * In addition to the foregoing, this research reports the influence of each parameter, which implies that the main factors that condition the variability are the angle of inclination of the sensor (its influence is 16.4 times higher than that of the height of flight, which is less incident factor), the forward overlap (3.4 times), and the lateral overlap (1.3 times higher with respect to the influence of the height of flight). This indicates the parameters that should stop being considered as variables, given their great influence on the accuracy. More specifically, the inclination of the sensor should not be applied as a variable any longer, and the flights should be restricted to the nadiral ones. This is the other relevant contribution of this research, as a very commonly applied technique among UAVs pilots is the application of flights with sensor inclinations that allow acquiring details in the vertical plane (building facades, etc.). This improves the visual aspect of the model, as it avoids gaps, but clearly affects the accuracy in a negative way. The influence of the other three factors, height of flight, and forward and lateral overlaps is much more alike, and their variabilities are smaller, so not considering them is not adequate. * Time requirements and their relation with the design of the flight should be noticed. The comparison among the flights that have been developed shows that the differences between those with the best and the worst metric qualities (flights 4 and 5; Table 5) are just 23 images, 1 min of flight, and 23 min of data processing. The best flight in terms of metric performance required 58 images, 10 min of acquisition and 95 min of processing. All the foregoing implies that the difference in the total amount of time needed for the generation of the worst model is only 22.85% lower than that needed to get the best one within the initial set. In addition to the foregoing, the difference in terms of effort between the flights with the highest and lowest requirements (flights 8 and 3; Table 5) is wide.