## 5 Discussion  
Once the proposed methodology for thermal control by means of low-cost sensors installed in UAVs has been applied to the case considered, and in view of the results obtained from the analysis and evaluation of the 3D thermal model, the following interpretations of the research carried out can be taken into account. The selection of the sensor conditions the results obtained. The storage format of the thermal images and the type of information that they contain determines the final properties of the model. JPEG or R-JPEG image formats provide false color images that enable qualitative 3D models to be obtained, from which only evaluations based on photointerpretations by the user can be developed, in addition to a quantitative analysis of the isolated images. However, TIFF image formats make it possible to generate quantitative thermal 3D models in which the temperature can be directly determined for each point of the model. The R-JPEG images obtained enable an individual quantitative analysis of each of them, but not the generation of a model with temperature data, resulting in a qualitative thermal 3D model. The selection of the platform conditions the capacity of the resulting model, enabling, in the case of quantitative models, a quasi-automatic analysis by means of GIS tools for the identification of thermal anomalies. The potential of this methodology has to be further evaluated in investigations with greater scope in order to analyze the results when using other more complex equipment; this is beyond the scope of this study, which is focused on a first iteration oriented to the generation and validation of 3D thermal models for the identification of anomalies. Flight planning based on the atmospheric situation may condition the results. Prioritizing times or days of the year with low temperature and low solar radiation will guarantee a greater temperature difference between the anomalies and the environment, and therefore a greater gradient in the image, which facilitates anomaly identification. Similarly, cloudy days reduce the influence of the sun on the geomembranes, coatings, and pipes, keeping them at the temperature of the environment and avoiding their identification as possible pathologies. Despite the satisfactory results, the proposed method for MSW monitoring is also conditioned by the state of the facility during the UAV flight. In addition to the atmospheric and meteorological variables, the constant dumping of debris and its coverage with geotextile materials can modify the conditions, hiding and masking possible anomalies identified before they can be verified and corrected in the field. Therefore, the methodology has to be implemented periodically and frequently in order to act quickly and provide useful and effective solutions. The generation of the thermal 3D models following the proposed workflow, which is based on the combination of blocks of images, is fast and simple, solving the conventional problems of TIR image processing. Despite obtaining a 3D thermal model with the highest quality, detail, precision, and density possible, it should be noted that the resulting thermal orthoimage on which to perform the photointerpretation has a lower resolution compared to the RGB orthoimage. This is due to the lower spatial resolution of the thermal image. However, the GSD of the thermal orthoimage is 13.8 cm/px, which is considered sufficient for this type of application, where very high accuracies are not required. In spite of this, the advantages of having the 3D thermal model enable a 3D global conception of the landfill to be obtained, which also enables evaluation of the altimetry, morphology, and metric magnitudes, such as the volumes of the facility. On the other hand, the 3D thermal model generated by following the proposed methodology offers an accuracy of 1.63 \({}^{\circ}\)C in comparison with ground-truth measurements carried out with a complementary hand-held sensor in the field.