## 5 Discussion
The analysis of the orthoimage based on photointerpretation is a relatively complicated, time-consuming process that can be subject to experience. Visual analysis is the only alternative when dealing with a qualitative 3D thermal model, which is conditioned by the type of sensor used. In the case of quantitative models, semi-automatic analysis based on reclassifications with GIS tools can speed up the work, reducing time, favoring the identification of anomalies, and avoiding reliance on the interpretation of the naked human eye. GIS tools are one of the main alternatives used for the management of UAV photogrammetry-derived products. In this case, the software has been used for the correct visualization of the orthoimages and the georeferencing of the anomalies identified, as well as of the LFG collection wells, favoring the model analysis. However, the potential of this tool can be enhanced when using quantitative models, where the potential of the functions that it offers can be exploited in order to automatically identify thermal anomalies based on temperature. The validation of the results was carried out through a combination of photointerpretation of the thermal and RGB representations of the landfill, field visits, and information about the state of the facility. Although the characterization of the anomalies, their justification, and their correction are not the main objective of this study, more detailed and concise results can be obtained by using complementary sensors, such as methane concentration meters, boreholes for core recovery, and subsurface temperature probes. However, the use of these alternatives is much more expensive, contrasting with the low-cost profile of the proposed UAVs. The proposed method can be used as a decision-making tool in landfill management, providing complementary information when investing in tests involving more accurate and expensive profiling equipment. The results obtained enable the implementation of 4D models in thermal control applications as a more complete tool than the heat maps proposed in previous publications. The proposed alternative enables the establishment of a methodology for the identification of thermal anomalies using only one type of sensor. In this way, the necessary resources and information capture can be optimized compared to alternatives that propose the use of different sensors [19]. The proposed UAV terminal imaging application facilitates progress in the field of drone technology, providing a more efficient solution for the thermal monitoring of landfills compared to techniques based on hand-held sensors [13] or satellite imagery [3; 35], where spatial resolution is compromised. Unlike other investigations, the obtained representations also enable the determination of possible relations between the anomaly and LFG emissions based on more complete 3D models than those alternatives based on the interpretation of isolated UAV images [37] or 2D representations, such as heat maps [1] or ortho-thermal images [1], enabling the interpretation of altimetric information for better understanding and decision making. Finally, unlike the rest of the reviewedresearch [25], the methodology of photogrammetric processing used to generate a 4D model is detailed, facilitating its application in other case studies. The methodology is based on the interpretation of a single model based on one flight. The implementation of several periodic flights over time would allow the generation of dynamic models, showing the evolution of the landfill temperature over time, as well as a control of the anomalies identified for a better monitoring of the temperature. The results obtained from the analysis of the model and the current situation of the LFG collection system have made it possible to identify areas where the thermal anomaly lacks a simple a priori explanation.