{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:41:07Z","timestamp":1776102067785,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"LIFE 2014\u20132020 EU Programme","award":["LIFE15 ENV\/IT\/000423"],"award-info":[{"award-number":["LIFE15 ENV\/IT\/000423"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precision Agriculture (PA) is an approach to maximizing crop productivity in a sustainable manner. PA requires up-to-date, accurate and georeferenced information on crops, which can be collected from different sensors from ground, aerial or satellite platforms. The use of optical and thermal sensors from Unmanned Aerial Vehicle (UAV) platform is an emerging solution for mapping and monitoring in PA, yet many technological challenges are still open. This technical note discusses the choice of UAV type and its scientific payload for surveying a sample area of 5 hectares, as well as the procedures for replicating the study on a larger scale. This case study is an ideal opportunity to test the best practices to combine the requirements of PA surveys with the limitations imposed by local UAV regulations. In the field area, to follow crop development at various stages, nine flights over a period of four months were planned and executed. The usage of ground control points for optimal georeferencing and accurate alignment of maps created by multi-temporal processing is analyzed. Output maps are produced in both visible and thermal bands, after appropriate strip alignment, mosaicking, sensor calibration, and processing with Structure from Motion techniques. The discussion of strategies, checklists, workflow, and processing is backed by data from more than 5000 optical and radiometric thermal images taken during five hours of flight time in nine flights throughout the crop season. The geomatics challenges of a georeferenced survey for PA using UAVs are the key focus of this technical note. Accurate maps derived from these multi-temporal and multi-sensor surveys feed Geographic Information Systems (GIS) and Decision Support Systems (DSS) to benefit PA in a multidisciplinary approach.<\/jats:p>","DOI":"10.3390\/rs14194954","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4954","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Technical Challenges for Multi-Temporal and Multi-Sensor Image Processing Surveyed by UAV for Mapping and Monitoring in Precision Agriculture"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-1088","authenticated-orcid":false,"given":"Alessandro","family":"Lambertini","sequence":"first","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4822-1577","authenticated-orcid":false,"given":"Emanuele","family":"Mandanici","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7745-640X","authenticated-orcid":false,"given":"Maria Alessandra","family":"Tini","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9815-1004","authenticated-orcid":false,"given":"Luca","family":"Vittuari","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., and Sun, X. (2021). A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sens., 13.","DOI":"10.3390\/rs13061204"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.procs.2018.07.063","article-title":"Review on Application of Drone Systems in Precision Agriculture","volume":"133","author":"Mogili","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A review on UAV-based applications for precision agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Merz, M., Pedro, D., Skliros, V., Bergenhem, C., Himanka, M., Houge, T., Matos-Carvalho, J.P., Lundkvist, H., C\u00fcr\u00fckl\u00fc, B., and Hamr\u00e9n, R. (2022). Autonomous UAS-Based Agriculture Applications: General Overview and Relevant European Case Studies. Drones, 6.","DOI":"10.3390\/drones6050128"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Aguilar, F.J., Rivas, J.R., Nemmaoui, A., Pe\u00f1alver, A., and Aguilar, M.A. (2019). UAV-based digital terrain model generation under leaf-off conditions to support teak plantations inventories in tropical dry forests. A case of the coastal region of Ecuador. Sensors, 19.","DOI":"10.3390\/s19081934"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tmu\u0161i\u0107, G., Manfreda, S., Aasen, H., James, M.R., Gon\u00e7alves, G., Ben-Dor, E., Brook, A., Polinova, M., Arranz, J.J., and M\u00e9sz\u00e1ros, J. (2020). Current practices in UAS-based environmental monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12061001"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications\u2014A review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/MNET.001.1900521","article-title":"Toward Robust and Intelligent Drone Swarm: Challenges and Future Directions","volume":"34","author":"Chen","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e20103","DOI":"10.1002\/cppb.20103","article-title":"Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?","volume":"5","author":"Gago","year":"2020","journal-title":"Curr. Protoc. Plant Biol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Aslan, M.F., Durdu, A., Sabanci, K., Ropelewska, E., and G\u00fcltekin, S.S. (2022). A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses. Appl. Sci., 12.","DOI":"10.3390\/app12031047"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.isprsjprs.2021.12.006","article-title":"UAV in the advent of the twenties: Where we stand and what is next","volume":"184","author":"Nex","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, J., Xiang, J., Jin, Y., Liu, R., Yan, J., and Wang, L. (2021). Boost precision agriculture with unmanned aerial vehicle remote sensing and edge intelligence: A survey. Remote Sens., 13.","DOI":"10.3390\/rs13214387"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Messina, G., and Modica, G. (2020). Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook. Remote Sens., 12.","DOI":"10.3390\/rs12091491"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rinc\u00f3n, M.G., Mendez, D., and Colorado, J.D. (2022). Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images. Remote Sens., 14.","DOI":"10.3390\/rs14020356"},{"key":"ref_19","unstructured":"Inoue, Y., and Yokoyama, M. (August, January 28). Drone-Based Optical, Thermal, and 3d Sensing for Diagnostic Information in Smart Farming\u2014Systems and Algorithms. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Donati, C., Mammarella, M., Comba, L., Biglia, A., Gay, P., and Dabbene, F. (2022). 3D Distance Filter for the Autonomous Navigation of UAVs in Agricultural Scenarios. Remote Sens., 14.","DOI":"10.3390\/rs14061374"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pagliai, A., Ammoniaci, M., Sarri, D., Lisci, R., Perria, R., Vieri, M., D\u2019arcangelo, E., Storchi, P., and Kartsiotis, S.P. (2022). Comparison of Aerial and Ground 3D Point Clouds for Canopy Size Assessment in Precision Viticulture. Remote Sens., 14.","DOI":"10.3390\/rs14051145"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Comba, L., Khaliq, A., Chiaberge, M., and Gay, P. (2020). UAV and machine learning based refinement of a satellite-driven vegetation index for precision agriculture. Sensors, 20.","DOI":"10.3390\/s20092530"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.compag.2017.05.001","article-title":"An overview of current and potential applications of thermal remote sensing in precision agriculture","volume":"139","author":"Khanal","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.agwat.2016.08.026","article-title":"High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard","volume":"183","author":"Santesteban","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2021.09.022","article-title":"An optimized approach for generating dense thermal point clouds from UAV-imagery","volume":"182","author":"Jurado","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1080\/01431161.2019.1641241","article-title":"A photogrammetric approach to fusing natural colour and thermal infrared UAS imagery in 3D point cloud generation","volume":"41","author":"Javadnejad","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Iizuka, K., Watanabe, K., Kato, T., Putri, N.A., Silsigia, S., Kameoka, T., and Kozan, O. (2018). Visualizing the spatiotemporal trends of thermal characteristics in a peatland plantation forest in Indonesia: Pilot test using unmanned aerial systems (UASs). Remote Sens., 10.","DOI":"10.3390\/rs10091345"},{"key":"ref_28","unstructured":"AGROWETLANDS (2022, August 31). AGROWETLANDS II Project Funded by LIFE 2014\u20132020 European Union Programme. Available online: http:\/\/www.lifeagrowetlands2.eu\/."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Calone, R., Sanoubar, R., Lambertini, C., Speranza, M., Vittori Antisari, L., Vianello, G., and Barbanti, L. (2020). Salt tolerance and na allocation in sorghum bicolor under variable soil and water salinity. Plants, 9.","DOI":"10.3390\/plants9050561"},{"key":"ref_30","first-page":"2229","article-title":"Real Time Monitoring of Water Quality in an Agricultural Area with Salinity Problems","volume":"18","author":"Cipolla","year":"2019","journal-title":"Environ. Eng. Manag. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vittori Antisari, L., Speranza, M., Ferronato, C., De Feudis, M., Vianello, G., and Falsone, G. (2020). Assessment of water quality and soil salinity in the agricultural coastal plain Ravenna, North Italy. Minerals, 10.","DOI":"10.3390\/min10040369"},{"key":"ref_32","first-page":"2273","article-title":"Smart water and soil-salinity management in agro-wetlands","volume":"18","author":"Masina","year":"2019","journal-title":"Environ. Eng. Manag. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.iswcr.2020.11.007","article-title":"GIS-based soil maps as tools to evaluate land capability and suitability in a coastal reclaimed area (Ravenna, northern Italy)","volume":"9","author":"Falsone","year":"2021","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Masina, M., Lambertini, A., Dapr\u00e0, I., Mandanici, E., and Lamberti, A. (2020). Remote Sensing Analysis of Surface Temperature from Heterogeneous Data in a Maize Field and Related Water Stress. Remote Sens., 12.","DOI":"10.3390\/rs12152506"},{"key":"ref_35","first-page":"315","article-title":"Evolution of the techniques for subsidence monitoring at regional scale: The case of Emilia-Romagna region (Italy)","volume":"372","author":"Bitelli","year":"2015","journal-title":"Proc. Int. Assoc. Hydrol. Sci."},{"key":"ref_36","first-page":"39","article-title":"Updating the subsidence map of Emilia-Romagna region (Italy) by integration of SAR interferometry and GNSS time series: The 2011\u20132016 period","volume":"382","author":"Bitelli","year":"2020","journal-title":"Proc. Int. Assoc. Hydrol. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1007\/s10040-008-0319-9","article-title":"Salt water intrusion in the coastal aquifer of the southern Po Plain, Italy","volume":"16","author":"Antonellini","year":"2008","journal-title":"Hydrogeol. J."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-L\u00f3pez, J.M., P\u00e9rez-Garc\u00eda, J.L., Mozas-Calvache, A.T., and Delgado-Garc\u00eda, J. (2020). Mission flight planning of rpas for photogrammetric studies in complex scenes. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9060392"},{"key":"ref_39","unstructured":"AeroScientific (2022, August 31). DJIFlightPlanner. Available online: https:\/\/www.djiflightplanner.com\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1186\/s13007-018-0376-6","article-title":"PhenoFly Planning Tool: Flight planning for high-resolution optical remote sensing with unmanned areal systems","volume":"14","author":"Roth","year":"2018","journal-title":"Plant Methods"},{"key":"ref_41","unstructured":"Team, A.D. (2022, August 31). ArduPilot Mission Planner. Available online: https:\/\/ardupilot.org\/planner\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7154","DOI":"10.1080\/01431161.2018.1515508","article-title":"The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs","volume":"39","author":"Rangel","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1080\/22797254.2022.2054028","article-title":"Overlap influence in images obtained by an unmanned aerial vehicle on a digital terrain model of altimetric precision","volume":"55","author":"Conti","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Deng, J., Zhong, Z., Huang, H., Lan, Y., Han, Y., and Zhang, Y. (2020). Lightweight semantic segmentation network for real-time weed mapping using unmanned aerial vehicles. Appl. Sci., 10.","DOI":"10.3390\/app10207132"},{"key":"ref_45","unstructured":"Satellite Data Services (2022, August 31). Polar Orbit Tracks. Available online: https:\/\/www.ssec.wisc.edu\/datacenter\/."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kuenzer, C., and Dech, S. (2013). Theoretical Background of Thermal Infrared Remote Sensing. Thermal Infrared Remote Sensing: Sensors, Methods, Applications, Springer.","DOI":"10.1007\/978-94-007-6639-6"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1002\/esp.4125","article-title":"3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: Precision maps for ground control and directly georeferenced surveys","volume":"42","author":"James","year":"2017","journal-title":"Earth Surf. Process. Landforms"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"397","DOI":"10.5194\/isprsarchives-XL-1-W4-397-2015","article-title":"Direct georeferencing on small unmanned aerial platforms for improved reliability and accuracy of mapping without the need for ground control points","volume":"40","author":"Mian","year":"2015","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/TGRS.2013.2265295","article-title":"Direct georeferencing of ultrahigh-resolution UAV imagery","volume":"52","author":"Turner","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"247","DOI":"10.5194\/isprs-archives-XLII-2-W13-247-2019","article-title":"Uav direct georeferencing approach in an emergency mapping context. the 2016 central Italy earthquake case study","volume":"42","author":"Chiabrando","year":"2019","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"012029","DOI":"10.1088\/1755-1315\/500\/1\/012029","article-title":"Precise topographic mapping using direct georeferencing in UAV","volume":"500","author":"Syetiawan","year":"2020","journal-title":"IOP Conf. Ser.: Earth Environ. Sci"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"401","DOI":"10.5194\/isprsarchives-XL-1-W5-401-2015","article-title":"3D surface generation from aerial thermal imagery","volume":"40","author":"Khodaei","year":"2015","journal-title":"Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Maes, W.H., Huete, A.R., and Steppe, K. (2017). Optimizing the processing of UAV-based thermal imagery. Remote Sens., 9.","DOI":"10.3390\/rs9050476"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.isprsjprs.2018.10.002","article-title":"Structure from Motion for aerial thermal imagery at city scale: Pre-processing, camera calibration, accuracy assessment","volume":"146","author":"Conte","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ribeiro-Gomes, K., Hern\u00e1ndez-L\u00f3pez, D., Ortega, J.F., Ballesteros, R., Poblete, T., and Moreno, M.A. (2017). Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors, 17.","DOI":"10.3390\/s17102173"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Alarc\u00f3n, V., Garc\u00eda, M., Alarc\u00f3n, F., Viguria, A., Mart\u00ednez, A., Janisch, D., Acevedo, J.J., Maza, I., and Ollero, A. (2020). Procedures for the Integration of Drones into the Airspace Based on U-Space Services. Aerospace, 7.","DOI":"10.3390\/aerospace7090128"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ramirez-Atencia, C., and Camacho, D. (2018). Extending QGroundControl for Automated Mission Planning of UAVs. Sensors, 18.","DOI":"10.3390\/s18072339"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Trevisiol, F., Lambertini, A., Franci, F., and Mandanici, E. (2022). An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs. Remote Sens., 14.","DOI":"10.3390\/rs14040849"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.coastaleng.2016.03.011","article-title":"UAVs for coastal surveying","volume":"114","author":"Turner","year":"2016","journal-title":"Coast. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hartley, R.J.a.L., Henderson, I.L., and Jackson, C.L. (2022). BVLOS Unmanned Aircraft Operations in Forest Environments. Drones, 6.","DOI":"10.3390\/drones6070167"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Fang, S.X., O\u2019young, S., and Rolland, L. (2018). Development of small UAS beyond-visual-line-of-sight (BVLOS) flight operations: System requirements and procedures. Drones, 2.","DOI":"10.3390\/drones2020013"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Tsiamis, N., Efthymiou, L., and Tsagarakis, K.P. (2019). A comparative analysis of the legislation evolution for drone use in oecd countries. Drones, 3.","DOI":"10.3390\/drones3040075"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Alamouri, A., Lampert, A., and Gerke, M. (2021). An exploratory investigation of UAS regulations in europe and the impact on effective use and economic potential. Drones, 5.","DOI":"10.3390\/drones5030063"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4954\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:46:26Z","timestamp":1760143586000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4954"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,4]]},"references-count":63,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194954"],"URL":"https:\/\/doi.org\/10.3390\/rs14194954","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,4]]}}}