Have a personal or library account? Click to login

Assessing the Impact of UAV Flight Altitudes on the Accuracy of Multispectral Indices

Open Access
|Dec 2024

References

  1. Avtar R., Suab S.A., Syukur M.S., Korom A., Umarhadi D.A., Yunus A.P. (2020): Assessing the influence of UAV altitude on extracted biophysical parameters of young oil palm. Remote Sensing, 12(18): 3030. https://doi.org/10.3390/rs12183030
  2. Bewick V., Cheek L., Ball J. (2004): Statistics review 9: One-way analysis of variance. Critical Care, 8: 130. https://doi.org/10.1186/cc2836
  3. Campi T., Cruciani S., Maradei F., Feliziani M. (2019): Innovative design of drone landing gear used as a receiving coil in wireless charging application. Energies, 12(18): 3483. https://doi.org/10.3390/en12183483
  4. Herrmann I., Bdolach E., Montekyo Y., Rachmilevitch S., Townsend P.A., Karnieli A. (2020): Assessment of maize yield and phenology by drone-mounted superspectral camera. Precision Agriculture, 21: 51-76. https://doi.org/10.1007/s11119-019-09659-5
  5. Ivošević B., Kostić M., Ljubičić N., Grbović Ž., Panić M. (2023a): Chapter 2 - A drone view for agriculture. In: Unmanned Aerial Systems in Agriculture. Bochtis D., Tagarakis A.C., Kateris D. (Eds.), Academic Press, pp. 25–47. https://doi.org/10.1016/B978-0-323-91940-1.00002-5
  6. Ivošević B., Kostić M., Ljubičić N., Grbović Ž., Panić M. (2023b): Chapter 3 - Application of unmanned aerial systems to address real-world issues in precision agriculture. In: Unmanned Aerial Systems in Agriculture. Bochtis D., Tagarakis A.C., Kateris D. (Eds.), Academic Press, pp. 51-69. https://doi.org/10.1016/B978-0-323-91940-1.00003-7
  7. Jełowicki L., Sosnowicz K., Ostrowski W., Osińska-Skotak K., Bakuła K. (2020): Evaluation of rapeseed winter crop damage using UAV-based multispectral imagery. Remote Sensing, 12(16): 2618. https://doi.org/10.3390/rs12162618
  8. Jiang R., Wang P., Xu Y., Zhou Z., Luo X., Lan Y., Zhao G., Sanchez-Azofeifa A., Laakso K. (2020): Assessing the operation parameters of a low-altitude UAV for the collection of NDVI values over a paddy rice field. Remote Sensing, 12(11): 1850. https://doi.org/10.3390/rs12111850
  9. Jovanović M., Pavić D., Mesaroš M., Stankov U., Pantelić M., Armenski T., Dolinaj D., Popov S., Ćosić Đ., Popović L., Frank A., Crnojević V. (2013): Water shortage and drought monitoring in Bačka region (Vojvodina, North Serbia) – setting-up measurement stations network. Geographica Pannonica, 17: 114-124.
  10. Jurado J.M., Ortega L., Cubillas J.J., Feito F.R. (2020): Multispectral mapping on 3D models and multi-temporal monitoring for individual characterization of olive trees. Remote Sensing, 12(7): 1106. https://doi.org/10.3390/rs12071106
  11. Kim H.-Y. (2015): Statistical notes for clinical researchers: post-hoc multiple comparisons. Restorative Dentistry & Endodontics, 40(2): 172. https://doi.org/10.5395/rde.2015.40.2.172
  12. Liu S., Zhang B., Yang W., Chen T., Zhang H., Lin Y., Tan J., Li X., Gao Y., Yao S., Lan Y., Zhang L. (2023): Quantification of physiological parameters of rice varieties based on multi-spectral remote sensing and machine learning models. Remote Sensing, 15(2): 453. https://doi.org/10.3390/rs15020453
  13. Lukas V., Huňady I., Kintl A., Mezera J., Hammerschmiedt T., Sobotková J., Brtnický M., Elbl J. (2022): Using UAV to identify the optimal vegetation index for yield prediction of oil seed rape (Brassica napus L.) at the flowering stage. Remote Sensing, 14: 4953. https://doi.org/10.3390/rs14194953
  14. Mesas-Carrascosa F.J., Rumbao C.I., Torres-Sánchez J., García-Ferrer A., Peña J.M., López Granados F. (2017): Accurate orthomosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes. International Journal of Remote Sensing, 38(8-10): 2161-2176. http://doi.org/10.1080/01431161.2016.1249311
  15. Mesas-Carrascosa F.-J., Torres-Sánchez J., Clavero-Rumbao I., García-Ferrer A., Peña J.-M., Borra-Serrano I., López-Granados F. (2015): Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by uav to support site-specific crop management. Remote Sensing, 7: 12793-12814. https://doi.org/10.3390/rs71012793
  16. Njane S.N., Tsuda S., van Marrewijk B.M., Polder G., Katayama K., Tsuji H. (2023): Effect of varying UAV height on the precise estimation of potato crop growth. Frontiers in Plant Science, 14: 1233349. https://doi.org/10.3389/fpls.2023.1233349
  17. Olson D., Chatterjee A., Franzen D.W., Day S.S. (2019): Relationship of drone-based vegetation indices with corn and sugarbeet yields. Agronomy Journal, 111(5): 2545-2557. https://doi.org/10.2134/agronj2019.04.0260
  18. Otto A., Agatz N., Campbell J., Golden B., Pesch E. (2018): Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: a survey. Networks, 72(4): 411-458. https://doi.org/10.1002/net.21818
  19. Pipatsitee P., Tisarum R., Taota K. Samphumphuang A., Cha-um S. (2023): Effectiveness of vegetation indices and UAV-multispectral imageries in assessing the response of hybrid maize (Zea mays L.) to water deficit stress under field environment. Environmental Monitoring and Assessment, 195: 128. https://doi.org/10.1007/s10661-022-10766-6
  20. Primicerio J., Di Gennaro S.F., Fiorillo E., Genesio L., Lugato E., Matese A., Vaccari F.P. (2012): A Flexible Unmanned Aerial Vehicle for Precision Agriculture. Precision Agriculture, 13(5): 517-523. https://doi.org/10.1007/s11119-012-9257-6
  21. Qi H., Wu Z., Zhang L., Li J., Zhou J., Jun Z., Zhu B. (2021): Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction. Computers and Electronics in Agriculture, 187: 106292. https://doi.org/10.1016/j.compag.2021.106292
  22. Rane N.L. & Choudhary S.P. (2023): Remote sensing (RS), UAV/drones, and machine learning (ML) as powerful techniques for precision agriculture: effective applications in agriculture. International Research Journal of Modernization in Engineering Technology and Science, 5(4): 4375-4392. https://www.doi.org/10.56726/IRJMETS36817
  23. Rasmussen J., Ntakos G., Nielsen J., Svensgaard J., Poulsen R., Christensen S. (2016): Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy, 74: 75-92. https://doi.org/10.1016/j.eja.2015.11.026
  24. Stow D., Nichol C., Wade T., Jakob A., Gillian S., Helfter C. (2019): Illumination geometry and flying height influence surface reflectance and NDVI derived from multispectral UAS imagery. Drones, 3(3): 55. https://doi.org/10.3390/drones3030055
  25. Su J., Yi D., Su B., Mi Z., Liu C., Hu X., Xu X., Guo L., Chen W.-H. (2020): Aerial visual perception in smart farming: field study of wheat yellow rust monitoring. IEEE Transactions on Industrial Informatics, 17(3): 2242-2249. 10.1109/TII.2020.2979237
  26. Szeląg B., Sobura S., Stoińska R. (2023): Application of multispectral images from unmanned aerial vehicles to analyze operations of a wastewater treatment plant. Energies, 16(6): 2871. https://doi.org/10.3390/en16062871
  27. Wilber A.L., Czarnecki J.M.P., McCurdy J.D. (2021): An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small-plot turfgrass research. Crop Science, 62: 503-511. https://doi.org/10.1002/csc2.20669
  28. WRB (2014): World Reference Base for soil resources 2014: International soil classification system for naming soils and creating legends for soil maps. Food and Agriculture Organization of the United Nations, Rome. Available at: https://openknowledge.fao.org/server/api/core/bitstreams/bcdecec7-f45f-4dc5-beb1-97022d29fab4/content (Accessed: 1 September 2023).
DOI: https://doi.org/10.2478/contagri-2024-0019 | Journal eISSN: 2466-4774 | Journal ISSN: 0350-1205
Language: English
Page range: 157 - 164
Submitted on: Oct 10, 2023
Accepted on: Sep 6, 2024
Published on: Dec 12, 2024
Published by: University of Novi Sad
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2024 Zoran Stamenković, Krstan Kešelj, Marko Kostić, Vladimir Aćin, Dragana Tekić, Mladen Ivanišević, Tihomir Novaković, published by University of Novi Sad
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.