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Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
dc.contributor.author | Arévalo Verjel, Alba Nely | |
dc.contributor.author | Lerma, José Luis | |
dc.contributor.author | Prieto, Juan F. | |
dc.contributor.author | Carbonell-Rivera, Juan Pedro | |
dc.contributor.author | Fernández, José | |
dc.date.accessioned | 2024-04-12T15:54:36Z | |
dc.date.available | 2024-04-12T15:54:36Z | |
dc.date.issued | 2022-06-16 | |
dc.identifier.uri | https://repositorio.ufps.edu.co/handle/ufps/6905 | |
dc.description.abstract | UAV-DAP (unmanned aerial vehicle-digital aerial photogrammetry) has become one of the most widely used geomatics techniques in the last decade due to its low cost and capacity to generate high-density point clouds, thus demonstrating its great potential for delivering highprecision products with a spatial resolution of centimetres. The questions is, how should it be applied to obtain the best results? This research explores different flat scenarios to analyse the accuracy of this type of survey based on photogrammetric SfM (structure from motion) technology, flight planning with ground control points (GCPs), and the combination of forward and cross strips, up to the point of processing. The RMSE (root mean square error) is analysed for each scenario to verify the quality of the results. An equation is adjusted to estimate the a priori accuracy of the photogrammetric survey with digital sensors, identifying the best option for µxyz (weight coefficients depending on the layout of both the GCP and the image network) for the four scenarios studied. The UAV flights were made in Lorca (Murcia, Spain). The study area has an extension of 80 ha, which was divided into four blocks. The GCPs and checkpoints (ChPs) were measured using dual-frequency GNSS (global navigation satellite system), with a tripod and centring system on the mark at the indicated point. The photographs were post-processed using the Agisoft Metashape Professional software (64 bits). The flights were made with two multirotor UAVs, a Phantom 3 Professional and an Inspire 2, with a Zenmuse X5S camera. We verify the influence by including additional forward and/or cross strips combined with four GCPs in the corners, plus one additional GCP in the centre, in order to obtain better photogrammetric adjustments based on the preliminary flight planning. | eng |
dc.format.extent | 17 Páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | Remote Sensing | spa |
dc.relation.ispartof | Remote Sens. 2022, 14, 2877. https://doi.org/ 10.3390/rs14122877 | |
dc.rights | under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | eng |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | spa |
dc.source | https://www.mdpi.com/2072-4292/14/12/2877 | spa |
dc.title | Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas | eng |
dc.type | Artículo de revista | spa |
dcterms.references | Mancini, F.; Dubbini, M.; Gattelli, M.; Stecchi, F.; Fabbri, S.; Gabbianelli, G. Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. Remote Sens. 2013, 5, 6880–6898. [Google Scholar] [CrossRef] [Green Version] | spa |
dcterms.references | Varbla, S.; Ellmann, A.; Puust, R. Centimetre-Range Deformations of Built Environment Revealed by Drone-Based Photogrammetry. Autom. Constr. 2021, 128, 103787. [CrossRef] | spa |
dcterms.references | Colomina, I.; Molina, P. Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79–97. [CrossRef] | spa |
dcterms.references | Moe, K.T.; Owari, T.; Furuya, N.; Hiroshima, T. Comparing Individual Tree Height Information Derived from Field Surveys, LiDAR and UAV-DAP for High-Value Timber Species in Northern Japan. Forests 2020, 11, 223. [CrossRef] | spa |
dcterms.references | Sanz-Ablanedo, E.; Chandler, J.H.; Rodríguez-Pérez, J.R.; Ordóñez, C. Accuracy of Unmanned Aerial Vehicle (UAV) and SfM Photogrammetry Survey as a Function of the Number and Location of Ground Control Points Used. Remote Sens. 2018, 10, 1606. [CrossRef] | spa |
dcterms.references | Doorn, A.J.; Van Koenderink, J.J. Affine Structure from Motion. JOSA A 1991, 8, 377–385. [CrossRef] | spa |
dcterms.references | Giordan, D.; Hayakawa, Y.; Nex, F.; Remondino, F.; Tarolli, P. Review Article: The Use of Remotely Piloted Aircraft Systems (RPASs) for Natural Hazards Monitoring and Management. Nat. Hazards Earth Syst. Sci. 2018, 18, 1079–1096. [CrossRef] | spa |
dcterms.references | Nettis, A.; Saponaro, M.; Nanna, M. RPAS-Based Framework for Simplified Seismic Risk Assessment of Italian RC-Bridges. Buildings 2020, 10, 150. [CrossRef] | spa |
dcterms.references | Contreras-De-villar, F.; García, F.J.; Muñoz-Perez, J.J.; Contreras-De-villar, A.; Ruiz-Ortiz, V.; Lopez, P.; Garcia-López, S.; Jigena, B. Beach Leveling Using a Remotely Piloted Aircraft System (Rpas): Problems and Solutions. J. Mar. Sci. Eng. 2021, 9, 19. [CrossRef] | spa |
dcterms.references | Monteiro, J.G.; Jiménez, J.L.; Gizzi, F.; Pˇrikryl, P.; Lefcheck, J.S.; Santos, R.S.; Canning-Clode, J. Novel Approach to Enhance Coastal Habitat and Biotope Mapping with Drone Aerial Imagery Analysis. Sci. Rep. 2021, 11, 574. [CrossRef] | spa |
dcterms.references | Siebert, S.; Teizer, J. Mobile 3D Mapping for Surveying Earthwork Projects Using an Unmanned Aerial Vehicle (UAV) System. Autom. Constr. 2014, 41, 1–14. [CrossRef] | spa |
dcterms.references | Galeana Pérez, V.M.; Chávez Alegría, O.; Medellín Aguilar, G. On the Measure of Land Subsidence throughout DEM and Orthomosaics Using GPS and UAV. Ing. Investig. Tecnol. 2021, 22, 1–12. [CrossRef] | spa |
dcterms.references | Miró Moncho, A. Optimización de La Geometría Alar de Un UAS/RPAS Para La Vigilancia Antiincendios; Polytechnic University of Valencia: Valencia, Spain, 2018. | spa |
dcterms.references | Ahmad, A.; Ordoñez, J.; Cartujo, P.; Martos, V. Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability. Agronomy 2020, 11, 7. [CrossRef] | spa |
dcterms.references | Araujo, R.F.; Chambers, J.Q.; Celes, C.H.S.; Muller-Landau, H.C.; dos Santos, A.P.F.; Emmert, F.; Ribeiro, G.H.P.M.; Gimenez, B.O.; Lima, A.J.N.; Campos, M.A.A.; et al. Integrating High Resolution Drone Imagery and Forest Inventory to Distinguish Canopy and Understory Trees and Quantify Their Contributions to Forest Structure and Dynamics. PLoS ONE 2020, 15, e0243079. [CrossRef] [PubMed] | spa |
dcterms.references | Baron, J.; Hill, D.J. Monitoring Grassland Invasion by Spotted Knapweed (Centaurea maculosa) with RPAS-Acquired Multispectral Imagery. Remote Sens. Environ. 2020, 249, 112008. [CrossRef] | spa |
dcterms.references | Gabara, G.; Sawicki, P. Multi-Variant Accuracy Evaluation of UAV Imaging Surveys: A Case Study on Investment Area. Sensors 2019, 19, 5229. [CrossRef] | spa |
dcterms.references | Polat, N.; Uysal, M. An Experimental Analysis of Digital Elevation Models Generated with Lidar Data and UAV Photogrammetry. J. Indian Soc. Remote Sens. 2018, 46, 1135–1142. [CrossRef] | spa |
dcterms.references | Acevo Herrera, R. Sistemas de Teledetección Activos y Pasivos Embarcados en Sistemas Aéreos No Tripulados para la Monitorización de la Tierra. Ph.D. Thesis, Universitat Politécnica Catalunya, Barcelona, Spain, 2011. | spa |
dcterms.references | Boletín Oficial del Estado (BOE). Real Decreto 1036/2017 de 15 de Diciembre. Bol. Estado 2017, 316, 129609–129641. | spa |
dcterms.references | Gómez-López, J.M.; Pérez-García, J.L.; Mozas-Calvache, A.T.; Delgado-García, J. Mission Flight Planning of RPAS for Photogrammetric Studies in Complex Scenes. ISPRS Int. J. Geo-Inf. 2020, 9, 392. [CrossRef] | spa |
dcterms.references | Lerma, J.L.G. Fotogrametria Moderna: Analitica y Digital; Universitat Politècnica de València: Valencia, Spain, 2002; 560p, ISBN 978-84-9705-210-8. | spa |
dcterms.references | Akturk, E.; Altunel, A.O. Accuracy Assesment of a Low-Cost UAV Derived Digital Elevation Model (DEM) in a Highly Broken and Vegetated Terrain. Meas. J. Int. Meas. Confed. 2019, 136, 382–386. [CrossRef] | spa |
dcterms.references | Agüera-Vega, F.; Carvajal-Ramírez, F.; Martínez-Carricondo, P. Assessment of Photogrammetric Mapping Accuracy Based on Variation Ground Control Points Number Using Unmanned Aerial Vehicle. Meas. J. Int. Meas. Confed. 2017, 98, 221–227. [CrossRef] | spa |
dcterms.references | Uysal, M.; Toprak, A.S.; Polat, N. DEM Generation with UAV Photogrammetry and Accuracy Analysis in Sahitler Hill. Meas. J. Int. Meas. Confed. 2015, 73, 539–543. [CrossRef] | spa |
dcterms.references | Jiménez-Jiménez, S.I.; Ojeda-Bustamante, W.; Ontiveros-Capurata, R.E.; Flores-Velázquez, J.; Marcial-Pablo, M.d.J.; Robles-Rubio, B.D. Quantification of the Error of Digital Terrain Models Derived from Images Acquired with UAV Cuantificación del Error de Modelos Digitales de Terreno Derivados de Imágenes Adquiridas Con UAV. Ing. Agríc. Biosist. 2017, 9, 85–100. [CrossRef] | spa |
dcterms.references | Cisneros, S.; García, É.; Montoya, K.; Sinde, I. Study of the Configurations of Ground Control Points for Photogrammetry with Drone. Rev. Geoespac. 2019, 16, 43–57. [CrossRef] | spa |
dcterms.references | Casella, V.; Chiabrando, F.; Franzini, M.; Manzino, A.M. Accuracy Assessment of a UAV Block by Different Software Packages, Processing Schemes and Validation Strategies. ISPRS Int. J. Geo-Inf. 2020, 9, 164. [CrossRef] | spa |
dcterms.references | Gómez-Candón, D.; De Castro, A.I.; López-Granados, F. Assessing the Accuracy of Mosaics from Unmanned Aerial Vehicle (UAV) Imagery for Precision Agriculture Purposes in Wheat. Precis. Agric. 2014, 15, 44–56. [CrossRef] | spa |
dcterms.references | Reshetyuk, Y.; Mårtensson, S.G. Generation of Highly Accurate Digital Elevation Models with Unmanned Aerial Vehicles. Photogramm. Rec. 2016, 31, 143–165. [CrossRef] | spa |
dcterms.references | Zimmerman, T.; Jansen, K.; Miller, J. Analysis of UAS Flight Altitude and Ground Control Point Parameters on DEM Accuracy along a Complex, Developed Coastline. Remote Sens. 2020, 12, 2305. [CrossRef] | spa |
dcterms.references | Martínez-Carricondo, P.; Agüera-Vega, F.; Carvajal-Ramírez, F.; Mesas-Carrascosa, F.J.; García-Ferrer, A.; Pérez-Porras, F.J. Assessment of UAV-Photogrammetric Mapping Accuracy Based on Variation of Ground Control Points. Int. J. Appl. Earth Obs. Geoinf. 2018, 72, 1–10. [CrossRef] | spa |
dcterms.references | Arévalo-Verjel, A.N.; Lerma, J.L.; Fernández, J. Análisis Comparativo de Software Para Obtener MDT Con Fotogrametría RPAS. In Proceedings of the Tercer Congreso en Ingeniería Geomática, Valencia, Spain, 7–8 July 2021; pp. 209–215. | spa |
dcterms.references | Tomaštík, J.; Mokroš, M.; Surový, P.; Grznárová, A.; Merganiˇc, J. UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas? Remote Sens. 2019, 11, 721. [CrossRef] | spa |
dcterms.references | Fernandez, J.; Prieto, J.F.; Escayo, J.; Camacho, A.G.; Luzón, F.; Tiampo, K.F.; Palano, M.; Abajo, T.; Pérez, E.; Velasco, J.; et al. Modeling the Two- and Three-Dimensional Displacement Field in Lorca, Spain, Subsidence and the Global Implications. Sci. Rep. 2018, 8, 14782. [CrossRef] [PubMed] | spa |
dcterms.references | González, P.J.; Fernández, J. Drought-Driven Transient Aquifer Compaction Imaged Using Multitemporal Satellite Radar Interferometry. Geology 2011, 39, 551–554. [CrossRef] | spa |
dcterms.references | Bonì, R.; Herrera, G.; Meisina, C.; Notti, D.; Béjar-Pizarro, M.; Zucca, F.; González, P.J.; Palano, M.; Tomás, R.; Fernández, J.; et al. Twenty-Year Advanced DInSAR Analysis of Severe Land Subsidence: The Alto Guadalentín Basin (Spain) Case Study. Eng. Geol. 2015, 198, 40–52. [CrossRef] | spa |
dcterms.references | Ezquerro, P.; Tomás, R.; Béjar-Pizarro, M.; Fernández-Merodo, J.A.; Guardiola-Albert, C.; Staller, A.; Sánchez-Sobrino, J.A.; Herrera, G. Improving Multi-Technique Monitoring Using Sentinel-1 and Cosmo-SkyMed Data and Upgrading Groundwater Model Capabilities. Sci. Total Environ. 2020, 703, 134757. [CrossRef] | spa |
dcterms.references | Drone Mapping Software. Available online: https://www.dronedeploy.com/ (accessed on 3 June 2021). | spa |
dcterms.references | Dach, R.; Schaer, S.; Arnold, D.; Kalarus, M.S.; Prange, L.; Stebler, P.; Villiger, A.; Jäggi, A. CODE Final Product Series for the IGS; Astronomical Institute, University of Bern: Bern, Switzerland, 2016. | spa |
dcterms.references | Teunissen, P.J.G.; Montenbruck, O. Springer Handbook of Global Navigation Satellite Systems; Springer International Publishing: Cham, Switzerland, 2017. | spa |
dcterms.references | Boehm, J.; Werl, B.; Schuh, H. Troposphere Mapping Functions for GPS and Very Long Baseline Interferometry from European Centre for Medium-Range Weather Forecasts Operational Analysis Data. J. Geophys. Res. Solid Earth 2006, 111, 2406. [CrossRef] | spa |
dcterms.references | Velasco, J.; Herrero, T.; Molina, I.; López, J.; Pérez-Martín, E.; Prieto, J. Methodology for Designing, Observing and Computing of Underground Geodetic Networks of Large Tunnels for High-Speed Railways. Inf. Constr. 2015, 67, e076. [CrossRef] | spa |
dcterms.references | Velasco-Gómez, J.; Prieto, J.F.; Molina, I.; Herrero, T.; Fábrega, J.; Pérez-Martín, E. Use of the Gyrotheodolite in Underground Networks of Long High-Speed Railway Tunnels. Surv. Rev. 2016, 48, 329–337. [CrossRef] | spa |
dcterms.references | ArcGIS for Desktop. Available online: https://desktop.arcgis.com/es/arcmap/10.3/manage-data/kml/what-is-kml-.htm (accessed on 3 June 2021). | spa |
dcterms.references | Agisoft PhotoScan User Manual—Professional Edition, Version 1.2. 2016. Available online: https://www.agisoft.com/pdf/ photoscan-pro_1_2_en.pdf (accessed on 2 June 2021). | spa |
dcterms.references | Kraus, K. Volume 2, Advanced Methods and Applications. In Photogrammetry; Jansa, J., Kager, H., Eds.; Dümmler: Bonn, Germany, 1997; p. 459. ISBN 3427786943. | spa |
dcterms.references | FGDC-STD-007.3-1998; Geospatial Positioning Accuracy Standards, Part 3: National Standard for Spatial Data Accuracy. Subcommittee for Base Cartographic Data, Federal Geographic Data Committee: Reston, VA, USA, 1998. | spa |
dcterms.references | Kraus, K. Volume 1, Fundamentals and Standard Processes. In Photogrammetry; Dümmler: Bonn, Germany, 1993; p. 389, ISBN 3427786846. | spa |
dcterms.references | James, M.R.; Robson, S. Mitigating Systematic Error in Topographic Models Derived from UAV and Ground-Based Image Networks. Earth Surf. Process. Landf. 2014, 39, 1413–1420. [CrossRef] | spa |
dc.identifier.doi | https://doi.org/10.3390/rs14122877 | |
dc.relation.citationedition | Vol.14 No.12 (2022) | spa |
dc.relation.citationendpage | 17 | spa |
dc.relation.citationissue | 12 (2022) | spa |
dc.relation.citationstartpage | 1 | spa |
dc.relation.citationvolume | 14 | spa |
dc.relation.cites | : Arévalo-Verjel, A.N.; Lerma, J.L.; Prieto,Carbonell-Rivera, J.P.; Fernández, J. Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas. Remote Sens. 2022, 14, 2877. https://doi.org/ 10.3390/rs14122877 | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.creativecommons | Atribución 4.0 Internacional (CC BY 4.0) | spa |
dc.subject.proposal | UAV | eng |
dc.subject.proposal | UAV-DAP | eng |
dc.subject.proposal | aerial close-range photogrammetry | eng |
dc.subject.proposal | GCP | eng |
dc.subject.proposal | flight planning | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |