A Comparative Analysis of UAV-RTK and UAV-PPK Methods in Mapping Different Surface Types

Year 2021, Volume: 7 Issue: 1, 12 - 25, 30.06.2021

Remzi Eker , Ece Alkan , Abdurrahim Aydın

https://doi.org/10.33904/ejfe.938067

Cited By: 13

Abstract

This study aimed to compare unmanned aerial vehicle (UAV) based real-time kinematic (RTK) and post-processing kinematic (PPK) methods via five approaches: an RTK-CORS method (M1), a short-baseline PPK method obtaining corrections from a GNSS base station (M2), and three long-baseline PPK methods that obtained corrections from the three Turkish RTK-CORS network TUSAGA-Aktif reference stations (M3: IZMI, M4: CESM, and M5: KIKA). The comparison was based on the accuracy of the corrected camera positions, the average error of the camera locations computed in the photo-alignment and optimization process, georeferencing errors of the models via nine GCPs based on four scenarios, and Root Mean Square (RMS) errors in the Z-direction for different surface types (i.e. roads, shadows, shrubs, boulders, trees, and ground). For the surface types of “ground”, “roads”, and “shrubs”, RMS error rates were obtained 10 cm lower than that of other surface types in all methods except M4. The greatest differences were obtained over trees and shadowed areas. The conclusion of these comparisons was that the lowest RMS error rate was determined on a solid textured surface. The consideration of mean RMS error regardless of surface type in such model comparisons is misleading.

Keywords

Accuracy comparison, GNSS, Photogrammetry, RTK/PPK, Surface Types, UAV

References

• Abdelkader, M., Shaqura, M., Claudel, C.G., Gueaieb, W., 2013. A UAV based system for real time flash flood monitoring in desert environments using Lagrangian microsensors. International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 25-34.
• Adams, M.S., Bühler, Y., Fromm, R., 2018. Multitemporal accuracy and precision assessment of unmanned aerial system photogrammetry for slope-scale snow depth maps in alpine terrain. Pure and Applied Geophysics, 175: 3303–3324.
• Agisoft Metashape User Manual, 2019. Agisoft Metashape User Manual: Professional Edition, Version 1.5 https://www.agisoft.com/pdf/metashape-pro_1_5_en.pdf.
• Agüera-Vega, F., Carvajal-Ramírez, F., Martínez-Carricondo, P., 2017. Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle. Meas J Int Meas Confed, 98: 221–227.
• Akgul, M., Yurtseven, H., Gulci, S., Akay, A.E., 2018. Evaluation of UAV- and GNSS-based DEMs for earthwork volume. Arabian Journal for Science and Engineering, 43(4): 1893–1909.
• Annis, A., Nardi, F., Petroselli, A., Apollonio, C., Arcangeletti, E., Tauro, F., Belli, C., Bianconi, R., Grimaldi, S., 2020. UAV-DEMs for Small-Scale Flood Hazard Mapping. Water, 12, 1717.
• Bühler, Y., Adams, M.S., Bösch, R., Stoffel, A., 2016. Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): Potential and limitations. Cryosphere, 10: 1075–1088.
• Campana, S., 2017. Drones in archaeology. State-of-the-art and future perspectives. Archaeol Prospect, 24: 275-296.
• Carvajal, F., Agüera, F., Pérez, M., 2011. Surveying a landslide in a road embankment using unmanned aerial vehicle photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII (Part 1/C22): 201–206.
• Colomina, I., Molina, P., 2014. Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 92: 79-97.
• De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., Della Vedova, G., 2016. Using a fixed-wing UAS to map snow depth distribution: An evaluation at peak accumulation. Cryosphere, 10: 511–522.
• Eisenbeiss, H., 2009. UAV photogrammetry. Ph.D. Thesis, Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland, 235.
• Eker, R., Aydın, A., 2021. Long-term retrospective investigation of a large, deep-seated, and slow-moving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey). Catena, 196: 104895.
• Eker, R., Aydın, A., Hübl, J., 2018. Unmanned aerial vehicle (UAV)-based monitoring of a landslide: Gallenzerkogel landslide (Ybbs-Lower Austria) case study. Environ. Monitor. Assess. 190: 14.
• Eker, R., Bühler, Y., Schlögl, S., Stoffel, A., Aydın, A., 2019. Monitoring snow cover ablation with very high spatial resolution remote sensing techniques. Remote Sensing, 11(6): 699.
• Evaerts, J., 2008. The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. Proceeding of the The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVII (Part B1. Beijing): 1187–1191.
• Fernández-Hernandez, J., González-Aguilera, D., Rodríguez-Gonzálvez, P., Mancera-Taboada, J., 2015. Image-based modelling from Unmanned Aerial Vehicle (UAV) photogrammetry: An effective, low-cost tool for archaeological applications. Archaeometry, 57: 128-145.
• Giordan, D., Manconi, A., Remondino, F., Nex, F., 2017. Use of unmanned aerial vehicles in monitoring application and management of natural hazards. Geomatics, Natural Hazards and Risk, 8: 1–4.
• Gomez, C., Purdie, H., 2016. UAV- based Photogrammetry and Geocomputing for Hazards and Disaster Risk Monitoring – A Review. Geoenvironmental Disasters, 3: 23.
• Gülci, S., 2019. The determination of some stand parameters using SfM-based spatial 3D point cloud in forestry studies: An analysis of data production in pure coniferous young forest stands. Environ Monit Assess, 191: 495.
• Harwin, S., Lucieer, A., Osborn, J., 2015. The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis. Remote Sensing, 7: 11933–11953.
• Hofmann-Wellenhof, B., Lichtenegger, H., Wasle, E., 2007. GNSS–Global Navigation Satellite Systems: GPS, GLONASS, Galileo and More. Springer Science & Business Media, New York, NY, USA, ISBN 3211730176.
• Honkavaara, E., Saari, H., Kaivosoja, J., Pölönen, I., Hakala, T., Litkey, P., Mäkynen, J., Pesonen, L., 2013. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sens, 5: 5006-5039.
• James, M.R., Robson, S., 2014. Mitigating systematic error in topographic models derived from UAV and ground-based image networks. Earth Surf Process Landf, 39: 1413–1420.
• James, M.R., Robson, S., d’Oleire-Oltmanns, S., Niethammer, U., 2017. Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment. Geomorphology, 280: 51–66.
• Jurjevic, L., Gašparovic, M., Milas, A.S., Balenovi, I., 2020. Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes. Remote Sensing, 12: 404.
• Langhammer, J., Lendzioch, T., Miřijovský, J., Hartvich, F., 2017. UAV-Based Optical Granulometry as Tool for Detecting Changes in Structure of Flood Depositions. Remote Sensing, 9(3): 240.
• Lindner, G., Schraml, K., Mansberger, R., Hübl, J., 2016. UAV monitoring and documentation of a large landslide. Appl Geomat, 8(1): 1–11.
• Lisein, J., Pierrot-Deseilligny, M., Bonnet, S., Lejeune, P., 2013. A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery. Forests, 4(4): 922–944.
• Lucieer, A., de Jong, S.M., Turner, D., 2014. Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Prog Phys Geogr, 38: 97–116.
• Mateos, R.M., Azañón, J.M., Roldán, F.J., Notti, D., Pérez-Peña, V., Galve, J.P., Pérez-García, J.L., Colomo, C.M., Gómez-López, J.M., Montserrat, O., Devantèry, N., Lamas-Fernández, F., Fernández-Chacón, F., 2017. The combined use of PSInSAR and UAV photogrammetry techniques for the analysis of the kinematics of a coastal landslide affecting an urban area (SE Spain). Landslides, 14(2): 743–754.
• Matese, A., Toscano, P., Di Gennaro, S.F., Genesio, L., Vaccari, F.P., Primicerio, J., Gioli, B., 2015. Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sensing, 7 (3): 2971-2990.
• Mekik, Ç., Yıldırım, Ö., Bakıcı, S., 2011. The Turkish real time kinematic GPS network (TUSAGA-Aktif) infrastructure. Scientific Research and Essays, 6(19): 3986-3999.
• Nebiker, S., Annen, A., Scherrer, M., Oesch, D., 2008. A lightweight multispectral sensor for micro-UAV—opportunities for very high resolution airborne remote sensing. Proceeding of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVII (Part B1. Beijing), 1193–1199.
• Niethammer, U., Rothmund, S., Joswig, M., 2009. UAV-based remote sensing of the slow-moving landslide super-Sauze. In: Malet JP, Remaître A, Boogard T (eds) Proceedings of the International Conference on Landslide Processes: From geomorphologic mapping to dynamic modeling. CERG Editions, Strasbourg, France, 69–74.
• Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I., 2020. A compilation of UAV applications for precision agriculture. Computer Networks, 172: 107148.
• Rehak, M., Mabillard, R., Skaloud, J., 2013. A micro-UAV with the capability of direct georeferencing. ISPRS – Int Arch Photogramm Remote Sen Spatial Inform Sci XL-1/W2: 317-323.
• Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., Sarazzi, D., 2011. UAV photogrammetry for mapping and 3D modeling - Current status and future perspectives. In: Int Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(1/C22). ISPRS Conference UAV-g, Zurich, Switzerland.
• Rydlund, P.H. Jr., Densmore, B.K., 2012. Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, Book 11, Chapt. D1, 102.
• Sanz-Ablanedo, E., Chandler, J., Rodríguez-Pérez, J., Ordóñez, C., Sanz-Ablanedo, E., Chandler, J.H., Rodríguez-Pérez, J.R., Ordóñez, C., 2018. Accuracy of unmanned aerial vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sensing, 10: 1606.
• Saroglou, C., Asteriou, P., Zekkos, D., Tsiambaos, G., Clark, M., Manousakis, J., 2018. UAV-based mapping, back analysis and trajectory modeling of a coseismic rockfall in Lefkada island, Greece. Nat Hazards Earth Syst Sci, 18: 321-333.
• Shervais, K., 2015. Structure from Motion, Introductory Guide. Retrieved 27 July 2016 from https://www.unavco.org/education/resources/educational-resources/lesson/field-geodesy/module-materials/sfm-intro-guide.pdf.
• Sugiura, R., Noguchi, N., Ishii, K., 2007. Correction of low-altitude thermal images applied to estimating soil water status. Biosystems Engineering, 96(3): 301–313.
• Taddia Y, Stecchi F, Pellegrinelli A (2020) Coastal mapping using DJI phantom 4 RTK in post-processing kinematic mode. Drones 4: 9.
• Thiel, C., Schmullius, C., 2017. Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective. International Journal of Remote Sensing, 38(8-10): 2411-2426.
• Tokekar, P., Hook, J.V., Mulla, D., Isler, V., 2016. Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Transactions on Robotics, 32(6): 1498 – 1511.
• Tomaštík, J., Mokroš, M., Surový, P., Grznárová, A., Merganic, J., 2019. UAV RTK/PPK method-An optimal solution for mapping inaccessible forested areas? Remote Sensing, 11: 721.
• Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S.F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zaldei, A., Wallace, L., 2017. Forestry applications of UAVs in Europe: A review. International Journal of Remote Sensing, 38(8-10): 2427-2447. Turner, D., Lucieer, A., de Jong, S.M., 2015. Time series analysis of landslide dynamics using an unmanned aerial vehicle (UAV). Remote Sensing, 7(2): 1736–1757.
• Vander Jagt, B., Lucieer, A., Wallace, L., Turner, D., Durand, M., 2015. Snow Depth Retrieval with UAS Using Photogrammetric Techniques. Geosciences, 5: 264.
• Wallace, L., Lucieer, A., Malenovsky, Z., Turner, D., Vopenka, P., 2016. Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7: 62.
• Wallace, L., Lucieer. A., Watson, C., Turner, D., 2012. Development of a UAV-LiDAR system with application to forest inventory. Remote Sensing, 4(12): 1519–1543.
• Watts, A.C., Ambrosia, V.G., Hinkley, E.A., 2012. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sensing, 4(12): 1671–1692.
• Westoby, M.J., Brasington, J., Glasser, N.F., Hambrey, M.J., Reynolds, J.M., 2012. Structure-from-Motion photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179: 300–314.
• White Paper, 2018. Reaching 1 cm (0.4 in) drone survey accuracy. Available at https://wingtra.com/mapping-drone-wingtraone/drone-survey-accuracy/wingtra-white-paper-reaching-1cm-drone-survey-accuracy/.
• Yıldırım, Ö., Mekik, Ç., Bakıcı, S., 2011. TUSAGA-Active(CORS-TR)System, Contributions to The General Directorate of Land Registry and Cadastre. Geodesy, Geoinformation and Land Management Journal, Special Issue, 2011(2): 134-139.
• Yuan, C., Zhang, Y., Liu, Z., 2015. A Survey on Technologies for Automatic Forest Fire Monitoring, Detection, and Fighting Using Unmanned Aerial Vehicles and Remote Sensing Techniques. Canadian Journal of Forest Research, 45(7): 783–792.
• Zhang, H., Aldana-Jague, E., Clapuyt, F., Wilken, F., Vanacker, V., Van Oost, K., 2019. Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure-from-motion (SfM) photogrammetry and surface change detection. Earth Surf Dyn, 7: 807–827.