The emergence of drones (UAVs—Unmanned Aerial Vehicles) has enabled a new form of topographic data acquisition that is fast, flexible, and inexpensive. These unmanned aircraft make it possible to fly over hard-to-reach areas and obtain high-resolution images, which, once processed using photogrammetry, give rise to essential geospatial products: orthophotos, digital elevation models (DEMs/DSMs), point clouds, etc. This density of information is directly linked to specific scientific and professional objectives [1][2], particularly in the study of urban areas, road developments, hydraulic structures or sanitation projects, etc.

However, while drones offer easy access to data, the choice of positioning method for georeferencing images remains crucial for achieving good accuracy. Georeferencing photogrammetric models is traditionally done indirectly, by establishing ground control points (GCPs) over the study area. This operation is often demanding in terms of time, labor, and cost. However, direct georeferencing of these models is constantly evolving thanks to technologies embedded in drones [3]. The DRTK method provides real-time correction from a fixed base station (such as the DRTK2 station) and is appealing due to its simplicity in the field. In contrast, the PPK method, which performs corrections after acquisition, offers greater robustness against signal loss or in noisy environments. The accuracy achieved then depends on multiple factors: terrain type, satellite coverage, quality of the reference bases, etc. [4][5]. The resulting research question is: which method is best suited according to the constraints of the terrain (rugged or relatively flat relief) and the cartographic objectives (2D or 3D).

The objective of this study is to compare the performance of GNSS positioning combined with a drone in PPK, DRTK2 and DRTK2 + GCP mode on two sites chosen for their different morphological characteristics.

The acquired data were subjected to a series of processing steps aimed at producing precise georeferenced photogrammetric deliverables, enabling comparative analysis of different positioning methods.

The results obtained in this study highlight the crucial influence of terrain morphology on the performance of GNSS positioning techniques used in drone mapping. In the flat Mbour 4 area, the data show that PPK generally offers better accuracy with the addition of post-processing.

Furthermore, in rugged terrain, the PPK method is penalised by a deterioration in GNSS signal quality due to occultations, satellite lock loss, and variations in the signal-to-noise ratio. These conditions promote cycle jumps in RINEX files and make ambiguity resolution less reliable, even after post-processing. These limitations explain the lower accuracy of PPK in this type of environment, unlike the DRTK2 + GCP method, which benefits from more stable geometric anchoring thanks to ground control points.

In planimetry in the non-rugged zone, analysis of the results of Oniga et al. [16] with the DJI Phantom 4 Pro v2 near Lasi (Romania) and Eker et al. [4] with the DJI Phantom 4 RTK in western Türkiye, respectively on agricultural land and homogeneous surfaces (road and bare soil) without the use of GCP, yielded RMSE values between 0.02 m and 0.033 m with the PPK method. We observe that our result with the same method under similar conditions, with an RMSE of 0.019 m, is satisfactory and consistent with these results. However, the DRTK2 method gives an RMSE of 0.058 m, 0.012 m less precise than the study by Eker et al., who used the RTK-CORS method with an RMSE of 0.0334 m. This slight difference could be explained by the contribution of the CORS station. As for the DRTK2 + GCP method, it presents an RMSE of 0.022 m, which is within the range of measurements reported by Volvo et al. [18] who studied the RTK mode associated with GCPs on the Grande plage of the southwest of the Ile d’Oléron in France (mission 1: 0.016 m; mission 2: 0.023 m) with respective deviations of 0.006 m and 0.001 m.

Using the PPK altimetry method, we obtained an RMSE of 0.042 m compared to 0.048 m reported by Eker et al. [4], a favorable difference of 0.006 m. The DRTK2 method shows an RMSE of 0.056 m, 0.038 m more precise than that of Eker et al. [4] (0.0943 m). Similarly, the DRTK2 + GCP method gives an RMSE of 0.055 m, 0.039 m more precise than that of Eker et al. [4], with values of 0.0943 m and 0.029 m, and 0.005 m more precise than the work of Volvo et al. [18] (Mission 1: 0.026 m and Mission 2: 0.045 m).

Zhang et al. [17] carried out several flight missions with two different camera-drone systems: a micro-drone equipped with a wide-angle action camera (FOV) and a professional drone equipped with a single-lens digital SLR camera. They reported on homogeneous agricultural areas in the Belgian loess belt (Belgium), an RMSE of 0.03 m in PPK, performance very close to that obtained in our study with a three-dimensional error of 0.046 m, or a difference of 0.016 m. This agreement confirms that PPK fully benefits from the favorable conditions offered by open terrain. Similarly, Miron et al. [19], with the DJI Phantom 4 Pro v2 a flat urban context in Iasi (Romania), analyzed the georeferencing of PPK without GCP using a local base station at a flight altitude of 60 m and obtained an accuracy of 0.037 m. Comparing these results with ours under the same conditions, the observed differences remain 0.009 m lower. The findings of Taddia et al. [20] with an identical drone provide further insight in a coastal context in the northern Adriatic Sea, Italy. Their use of nadir imagery enabled them to achieve a three-dimensional accuracy between 0.034 m and 0.075 m. The RMSE values recorded in this study corroborate ours (0.046 m in 3D), thus confirming the consistency of the observed performance. However, the DRTK 2 method yields an RMSE of 0.079 m, which is more accurate than the study by Eker et al. [4] (0.1001 m), with a difference of 0.021 m. Finally, the DRTK 2 + GCP method has a precision of 0.059 m, less precise compared to the work of Volvo et al. [18] (mission 1 of 0.03 m and mission 2 of 0.055 m) with deviations of 0.029 m and 0.005 m.

In planimetry, in the rugged area, the DRTK2 + GCP method exhibits the best performance with an RMSE of approximately 0.025 m, thus confirming good horizontal accuracy. This improvement results from the integration of ground control points (GCPs), which effectively correct deformations in the photogrammetric model and ensure precise registration of the point cloud. These results are consistent with those of Monjardin-Armenta et al. [3] using the DJI P4, who obtained 0.0507 m in DRTK + GCP mode in Culiacán, Mexico. Although our study shows twice the accuracy, both works clearly demonstrate the decisive influence of GCPs in improving planimetric accuracy. In comparison, the DRTK2 method without GCPs achieves 0.070 m, which remains satisfactory but slightly lower than the literature references, particularly those of Monjardin-Armenta et al. (0.052 m), Zeybek et al. [21] with the DJI Phantom 4 RTK (0.038 m in the Merkez district, Türkiye) and Kim et al. [5] (0.03 m in Gyeongsangnam-do, South Korea). The PPK method, on the other hand, provides an RMSE of 0.065 m, reflecting good overall performance, with deviations of 0.03 and 0.04 m respectively from the results of Zeybek et al. [21] and Kovanič et al. [22] using an identical drone (0.031 m in eastern Slovakia), reflecting the influence of GNSS signal quality and local conditions.

In altimetry, still within the rugged terrain, the DRTK2 + GCP method also stands out, with an RMSE of 0.057 m, reflecting good vertical accuracy thanks to the use of GCPs. However, this accuracy remains slightly lower than those reported by Monjardin-Armenta et al. [3] (0.0201 m) and Zeybek et al. [21] (0.039 m), with discrepancies ranging from 0.018 to 0.037 m, which can be attributed to flight configuration, GNSS signal quality, and topographic complexity. The DRTK2 method without GCP shows 0.066 m, a decent performance but lower than the literature (Monjardin-Armenta et al. [3]: 0.0209 m; Zeybek et al. [21]: 0.039 m), which highlights the dependence of accuracy on environmental conditions and the shooting geometry. Finally, the PPK without GCP provides an RMSE of 0.055 m, reflecting an overall satisfactory performance, with deviations of 0.002 to 0.057 m compared to the studies cited previously (Zeybek et al. [21]: 0.059 m and Kovanič et al. [22]: 0.112 m) confirming its robustness for vertical restitution even in the absence of control points.

In 3D, the DRTK2 + GCP method maintains the best accuracy, with an RMSE of 0.063 m, close to that of Monjardin-Armenta et al. [3] (0.0546 m). The 8.4 mm difference confirms the reliability of our measurements and the importance of GCP for three-dimensional accuracy. The DRTK2 method without GCP achieves 0.095 m, a performance lower than the values reported in the literature (Monjardin-Armenta et al. [3]: 0.0561 m; Zeybek et al. [21]: 0.0545 m). PPK, on the other hand, offers an RMSE of 0.085 m, reflecting generally satisfactory accuracy, with deviations of 1.8 to 5.1 cm compared to reference studies, confirming its reliability for three-dimensional reconstruction even without GCP.

Overall, these results show that GCP integration remains essential for achieving optimal accuracy, while PPK provides a robust and practical alternative when GCPs are unavailable. These findings can be explained by more complex topographic and environmental conditions, which influence GNSS signal stability, image geometry, and variations in terrain elevation.

The objective of this study, to evaluate the planimetric and altimetric accuracy of modern photogrammetry using the DRTK2, DRTK2 + GCP, and PPK methods, was largely achieved. The results obtained demonstrate that in relatively non-rugged areas, all three methods showed acceptable accuracy, with only the PPK method remaining the most precise. Conversely, the DRTK2 + GCP method performed better in rugged terrain.

It is important to note that this study revealed on the one hand the importance of the use of GCPs and their proper distribution and on the other hand that in implementation PPK is the easiest to apply, followed by simple DRTK2, while the DRTK2 + GCP method was the most complex (with the choice of the number of GCPs and the optimization of their distribution).

The originality of our work lies in the fact that it combines two types of terrain with different morphologies, with rigorously standardized flights (same altitude, same configurations in DRTK and PPK). GCPs leveled directly on an altimetric network and surveyed themselves in RTK, as well as an optimization of the point distribution according to Villanueva and Blanco, 2019 [23]. This comprehensive methodology allowed us to experimentally validate the contexts in which each method excels.

However, our results call for caution, because although our study confirms the superiority of DRTK2 + GCP in terms of accuracy in rugged terrain, it also highlights the performance of PPK in such areas. Both DRTK2 and PPK remain limited in rugged terrain, and we did not explore factors such as point cloud density, systematic oblique image acquisition, or the direct effect of varying altitudes. In the future, it would be interesting to investigate the impact of the number of GCPs used with PPK, as well as performance in the presence of dense vegetation, which affects the reflectance and accuracy of photogrammetric point clouds.

Our study thus makes a significant contribution by highlighting the importance of adapting positioning methods to the context, in order to optimize the quality of cartographic products from modern photogrammetry.

In the rugged terrain, the DRTK2 + GCPs method is the most reliable, offering the best accuracy for both 2D (orthophotos) and 3D (DEM and point clouds) products. However, in the non-rugged area, the results show that the PPK method is the most effective for all cartographic products, whether 2D or 3D. These findings therefore help to guide the choice of method depending on the type of terrain and the cartographic objectives.

Based on the results obtained, several recommendations can be made. The choice of positioning method must be adapted to the topographic context and the project constraints. In flat areas, the PPK method appears to be the most appropriate, as it offers satisfactory accuracy, reduced cost, and simpler implementation. Conversely, in hilly areas, the DRTK2 + GCP method is preferable to ensure better three-dimensional accuracy, despite its higher cost and complexity. It is also important to emphasize the importance of an optimal distribution of GCPs, which largely determines the quality of the photogrammetric results. A homogeneous and well-considered placement of control points not only improves survey accuracy but also reduces uncertainties related to the terrain.

Further research would be valuable, incorporating other techniques such as Precise Point Positioning (PPP) or CORS networks, to reduce reliance on GCPs while maintaining high accuracy. Similarly, extending this type of study to diverse environments (dense urban areas, forest environments, and large areas) would allow for a better assessment of the quality and transferability of the tested methods.