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Influence of cloud cover and light intensity on the quality of photographic material obtained during nadir photogrammetric flights

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The current research aimed to compare RGB images taken during nadir photogrammetric flights made in different seasons of the year and different times of the day, which resulted in the collection of material taken in different light intensities and during different levels of cloud cover (in conditions without precipitation). The flight was carried out in an area with a varied land cover, which was reflected in the accuracy of the details visible in the photos (land, buildings, vegetation, vehicles, reservoirs, watercourses, etc.). The flights were carried out at an altitude of 120 m AGL, with the size of the ground pixel being no larger than 0.04 m and the overlap at the level of 85%. An unmanned aerial vehicle (DJI Matrice 210 v2 with a DJI Zenmuse X5S camera and an Olympus M.Zuiko 12 mm lens) was used. The obtained material was processed in the Pix4D Mapper program, which allowed us to compare photos taken at different light intensities (at different degrees of cloudiness); in this way, they were assessed in terms of clarity of detail. The same flight parameters (including setting the AutoFocus option) made it possible to indicate how the lighting intensity affects the quality and quantity of recognized details, with the distinction of the type of buildings, land, vegetation, vehicles, and water objects. It was found that the details visible in orthophotomosaics created from photos taken in low light intensity are characterized by a less visible raster texture, which causes difficulties in assessing the material of which the object is made. With low light intensity, however, the geometry of cubature objects is better exposed, making it easier to determine the type of architecture and the development boundaries. Orthophotomosaics created from photos obtained at high light intensity are characterized by much greater contrast, which is an important parameter in recognizing soil and vegetation. The issue of the size of an object that can be considered in terms of clarity has not been fully resolved. The dimensions of many point and linear objects are usually below the resolving power of orthophotomosaics. However, the variety of shapes and similar colors and shades sometimes limit the recognition and differentiation of objects from the soil and vegetation category in orthophotomosaics. The minimum degree of sky coverage with clouds, expressed as a percentage, was determined: low light intensity appears from 62% cloud cover, medium intensity from 37%, and high intensity from 19%. A SWOT analysis showed the low costs of UAV operations and the relatively short time of data acquisition, as well as the rapidly growing UAV market.
Czasopismo
Rocznik
Strony
39--49
Opis fizyczny
Bibliogr. 25 poz.
Twórcy
  • Silesian University of Technology, Faculty of Transport and Aviation Engineering; Krasińskiego 8, 40-019 Katowice, Poland
  • Silesian University of Technology, Faculty of Transport and Aviation Engineering; Krasińskiego 8, 40-019 Katowice, Poland
  • Silesian University of Technology, Civil Aviation Personnel Education Centre; Krasińskiego 13, 40-019 Katowice, Poland
Bibliografia
  • 1. Balestrieri, E. & Daponte, P. & De Vito, L. & Lamonaca, F. Sensors and measurements for unmanned systems: an overview. Sensors. 2021. Vol. 21(4). No. 1518.
  • 2. Borkowski, G. & Młynarczyk, A. Remote sensing using unmanned aerial vehicles for tourist-recreation lake valuation and development. Quaestiones Geographicae. 2019. Vol. 38(1). P. 5-14.
  • 3. Cramer, M. & Haala, N. DGPF project: evaluation of digital photogrammetric aerial-based imaging systems - overview and results from the pilot center. Photogrammetric Engineering and Remote Sensing. 2010. Vol. 76(9). P. 1019-1029.
  • 4. Ćwiąkała, P. & Kocierz, R. & Puniach, E. & Nędzka, M. & Mamczarz, K. & Niewiem, W. & Wiącek, P. Assessment of the possibility of using Unmanned Aerial Vehicles (UAVs) for the documentation of hiking trails in alpine areas. Sensors. 2018. Vol. 18(1). No. 81.
  • 5. Eisenbeiss, H. UAV photogrammetry. PhD thesis. Zurich: ETH. 2009.
  • 6. Haala, N. & Cramer, M. & Weimerb, F. & Trittlerb, M. Performance test on UAV-based photogrammetric data collection. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2011. Vol. XXXVIII-1/C22. P. 7-12.
  • 7. Honkavaara, E. & Saari, H. & Kaivosoja, J. & Polonen, I. & Hakala, T. & Litkey P. & Makynen J. & Pesonen L. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sensing. 2013. Vol. 5(10). P. 5006-5039.
  • 8. Jimenez-Jimenez, S.I. & Ojeda-Bustamante, W. & Marcial-Pablo, M.J. & Enciso, J. Digtal terrain models generated with low-cost UAV photogrammetry: methodology and accuracy. International Journal of Geo-Information. 2021. Vol. 10(5). No. 285.
  • 9. Kung, O. & Strecha, C. & Beyeler, A. & Zufferey, J-C. & Floreano, D. & Fua, F. & Gervaix, F. The accuracy of automatic photogrammetric techniques on ultra-light UAV imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2011. Vol. XXXVIII-1/C22. P. 125-130.
  • 10. Kurczyński, Z. & Bakuła, K. & Karabin, M. & Kowalczyk, M. & Markiewicz, J.S. & Ostrowski, W. & Podlasiak P. & Zawieska, D. The possibility of using images obtained from the UAS in cadastral works. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016. Vol. XLI-B1. P. 909-915.
  • 11. Li, M. & Shamshiri, R.R. & Schirrmann, M. & Weltzien, C. & Shafian, S. & Laursen, M.S. UAV oblique imagery with an adaptive micro-terrain model for estimation of leaf area index and height of maize canopy from 3D point clouds. Remote Sensing. 2022. Vol. 14(3). No. 585.
  • 12. Marcisz, M. & Gawor, Ł. & Kobylańska, M. Valorization of geotourist and geoheritage objects in the region of Mikołów (USCB, Southern Poland). Gospodarka Surowcami Mineralnymi - Minerał Resources Management. 2022. Vol. 38. P. 189-210.
  • 13. Marcisz, M. & Probierz, K. & Gawor, Ł. Possibilities of reclamation and using of large-surface coal mining dumping grounds in Poland. Gospodarka Surowcami Mineralnymi - Mineral Resources Management. 2020. Vol. 1. P. 105-122.
  • 14. Mazzoleni, M. & Paron, P. & Reali, A. & Juizo, D. & Manane, J. & Brandimarte, L. Testing UAV-derived topography for hydraulic modelling in a tropical environment. Natural Hazards. 2020. Vol. 103. P. 139-163.
  • 15. Pecho, P. & Śkvarekova, I. & Ataltović, V. & Bugaj, M. UAV usage in the process of creating 3D maps by RGB spectrum. Transportation Research Procedia. 2019. Vol. 43. P. 328-333.
  • 16. Popovic, M. & Vidal-Calleja, T. & Hitz, G. & Chung, J.J. & Sa, I. & Siegwart, R. & Nieto, J. An informative path planning framework for UAV-based terrain monitoring. Autonomous Robots. 2020. Vol. 44. P. 889-911.
  • 17. Probierz, K. & Gawor, Ł. & Marcisz, M. Valorization of coal mining waste dumps from the mines of Jastrzębska Spółka Węglowa SA for the needs of the recovery of coal and further reclamation and management. Gospodarka Surowcami Mineralnymi - Minerał Resources Management. 2018. Vol. 4. P. 97-114.
  • 18. Remondino, F. & Barazzetti, L. & Nex, F. & Scaoioni, M. & Sarazzi, D. UAV photogrammetry for mapping and 3d modeling-current status and future perspetives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2011. Vol. XXXVIII-1/C22. P. 25-29.
  • 19. Rosnell, T. & Honkavaara, E. & Nurminen, K. On geometric processing of multi-temporal image data collected by light UAV systems. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2011. Vol. XXXVIII-1/C22. P. 63-68.
  • 20. Sauerbier, M. & Siegrist, E. & Eisenbeiss, H. & Demir, N. The practical application of UAV-based photogrammetry under economic aspects. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2011. Vol. XXXVIII-1/C22. P. 45-50.
  • 21. Sawicki, P. Unmanned aerial vehicles in photogrammetry and remote sensing - state of the art and trends. Archiwum Fotogrametrii, Kartografii i Teledetekcji. 2012. Vol. 23. P. 365-376.
  • 22. Szczechowski, B. The use of unmanned aerial vehicles (mini helicopters) in photogrammetry from low level. Archiwum Fotogrametrii, Kartografii i Teledetekcji. 2008. Vol. 18. P. 569-579.
  • 23. Tahar, K.N. Aerial terrain mapping using unmanned aerial vehicle approach. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2012. Vol. 29. P. 493498.
  • 24. Żabota, B. & Kobal, M. Accuracy assessment of UAV-photogrammetric-derived products using PPK and GCPs in challenging terrains: in search of optimized Rockall mapping. Remote Sensing. 2021. Vol. 13. No. 3812.
  • 25. Zhang, C. & Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture. 2012. Vol. 13. P. 693-712.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-75e70b47-aaea-48c8-aa4e-b08aa62c4dc1
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