Optimal UAV Flight altitude for Multispectral monitoring of Wheat growth in Bayantsogt, Mongolia
DOI:
https://doi.org/10.5564/pmas.v65i02.4390Keywords:
Multispectral UAV imagery, wheat growth stages, vegetation indices, NDVI, spatial resolution, crop monitoringAbstract
This study evaluated the growth stages of wheat crops in the agricultural fields of Bayantsogt Soum, Tuv aimag-province, Mongolia, using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. Aerial imagery was captured at four spatial resolutions - 1.59 cm, 3.63 cm, 5.44 cm, and 11.35 cm - corresponding to flight altitudes of 30 m, 80 m, 120 m, and 250 m, respectively. Six vegetation indices (NDVI, GNDVI, LCI, NDRE, NDWI, and OSAVI) were calculated to evaluate their relationships with wheat biometric parameters. The determinant coefficient (R²) values for these indices were: LCI (0.81), OSAVI (0.79), NDRE (0.76), NDVI (0.76), NDWI (0.75), and GNDVI (0.73). Regarding spatial resolution, the corresponding R² values were 0.62 (1.59 ± 0.46 cm), 0.87 (3.63 ± 0.02 cm), 0.88 (5.44 ± 0.06 cm), and 0.68 (11.35 ± 0.04 cm) respectively. The findings indicate that the optimal flight altitude for estimating wheat growth characteristics was 120 m, providing a high correlation at a resolution of 5.44 ± 0.06 cm. By contrast, imagery captured from 250 m demonstrated relatively lower correlation. Overall, this study highlights the potential of UAV-based multispectral imaging for efficient crop monitoring and suggests an optimal operational altitude for precision agriculture applications.
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1. Barnes, E. M., Clarke, T. R., Richards, S. E., Colaizzi, P. D., Haberland, J., Kostrzewski, M., … Moran, M. S. (2000, July 16–19). Coincident detection of crop water stress, nitrogen status, and canopy density using ground-based multispectral data. In Proceedings of the 5th International Conference on Precision Agriculture (pp. 1–15). Bloomington, MN. https://www.indexdatabase.de/db/r-single.php?id=642&utm_source.
2. Banaszek, A., Zarnowski, A., Cellmer, A., & Banaszek, S. (2017). Application of new technology data acquisition using aerial (UAV) digital images for the needs of urban revitalization. In Environmental Engineering: Proceedings of the 10th International Conference on Environmental Engineering (ICEE) (Vol. 10, pp. 1–7). Vilnius Gediminas Technical University. https://doi.org/10.3846/enviro.2017.159.
3. Cauchard, J. R., Zhai, K. Y., Spadafora, M., & Landay, J. A. (2016). Emotion encoding in human-UAV interaction. In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). https://doi.org/10.1109/hri.2016.7451761.
4. Clevers, J. G. P. W., Kooistra, L., & van der Brande, M. M. M. (2017). Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing, 9(4), p. 405. https://doi.org/10.3390/rs9040405.
5. Datt, B. (1999). Remote sensing of water content in eucalyptus leaves: The Leaf Chlorophyll Index (LCI). Journal of Plant Physiology, 154(1), pp. 30–36. https://doi.org/10.1016/S0176-1617(99)80125-6.
6. Giones, F., & Brem, A. (2017). From toys to tools: The co-evolution of technological and entrepreneurial developments in the UAV industry. Business Horizons, 60(6), pp. 875–884. https://doi.org/10.1016/j.bushor.2017.08.001.
7. Gao, F., Zhang, L., & Wang, X. (2021). Water content estimation using UAV remote sensing. Agricultural Water Management, p. 247, 106694. https://doi.org/10.1016/j.agwat.2020.106694.
8. Honkavaara, E., & Khoramshahi, E. (2018). Radiometric correction of close-range spectral image blocks captured using an unmanned aerial vehicle with a radiometric block adjustment. Remote Sensing, 10(2), p. 256. https://doi.org/10.3390/rs10020256.
9. Hansen, P. M., & Schjoerring, J. K. (2003). Leaf chlorophyll measurement using spectral indices. Photosynthetica, 41(3), pp. 303–305. https://doi.org/10.1023/A:1023960623095.
10. Hossen, M. I., Al-Tamimi, A., Salah, M., & Al-Habaibeh, A. (2021). Soil nitrogen estimation using UAV-based multispectral imaging and LIBS. arXiv. https://arxiv.org/abs/2107.02355.
11. Iqbal, F., Lucieer, A., & Barry, K. (2018). Simplified radiometric calibration for UAS mounted multispectral sensor. European Journal of Remote Sensing, 51(1), pp. 301–313. https://doi.org/10.1080/22797254.2018.1432293.
12. Keshet, D., Brook, A., Malkinson, D., Izhaki, I., & Charter, M. (2022). The use of drones to determine rodent location and damage in agricultural crops. Drones, 6(12), Article 396. https://doi.org/10.3390/drones6120396.
13. Korobeinikov, N. I., Peshkova, N. V., Valekzhanin, V. S., Boradulina, V. A., & Musalitin, G. M. (2014). State Scientific Institution Altai Scientific Research Institute of Agriculture.
14. Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., Shavers, E., Fishman, J., Peterson, J., Kadam, S., Burken, J., & Fritschi, F. B. (2017). Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, p. 43–58. https://doi.org/10.1016/j.isprsjprs.2017.10.011.
15. Mamaghani, B., Saunders, M. G., & Salvaggio, C. (2019). Inherent Reflectance Variability of Vegetation. Agriculture 2019, 9(11), p. 246; https://doi.org/10.3390/.
16. Masiri Kaamin, M. I. A., Faisal, M. H. A., Zaini, N. Z., Sahat, S., Mat Nor, A. H., Mokhtar, M., ... & Kamal, M. A. M. (n.d.). Production and evaluation of orthophoto map using UAV photogrammetry. Vol. 33 No. 1: December (2023). https://doi.org/10.37934/araset.33.1.187196.
17. Mazzia, V., Khaliq, A., Salvetti, F., Chiaberge, M., & Blotto, D. (2020). Improvement in crop monitoring through a deep learning framework for time series prediction. Sensors, 20(9), 2530. https://doi.org/10.3390/s20092530.
18. Mishra, A., & Singh, R. (2019). Chlorophyll monitoring with UAV imagery. Precision Agriculture, 20, pp. 65–80. https://doi.org/10.1007/s11119-018-09656-3.
19. Li, Y., Zhang, X., & Wang, J. (2020). Vegetation index applications in precision agriculture: A review. Agricultural Systems, 180, 102780. https://doi.org/10.1016/j.agsy.2020.102780.
20. Panthakkan, A., Murugan, A., & Ghosh, R. (2025). Evaluation of UAV-based RGB and multispectral vegetation indices for precision agriculture: A case study on palm trees. arXiv. https://arxiv.org/abs/2505.07840.
21. Peñuelas, J., Baret, F., & Filella, I. (1993). The soil-adjusted vegetation index (SAVI). International Journal of Remote Sensing, 14(8), pp. 1735–1745. https://doi.org/10.1080/01431169308954055.
22. Pu, R., Gong, P., & Yu, Q. (2008). Comparative analysis of EO-1 ALI and Hyperion, and Landsat ETM+ data for mapping forest crown closure and leaf area index. Sensors, 8(6), pp. 3744–3766. https://doi.org/10.3390/s8063744.
23. Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), pp. 95–107. https://doi.org/ 10.1016/0034-4257(95)00186-7.
24. Serrano, L., Filella, I., & Peñuelas, J. (2000). Remote sensing of biomass and yield of winter wheat under different nitrogen supplies: Green normalized difference vegetation index (GNDVI). Crop Science, 40(3), pp. 723–731. https://doi.org/10.2135/cropsci2000.403723x.
25. Sullivan, J. (2006). Evolution or revolution? The rise of UAVs. IEEE Technology and Society Magazine, 25(3), pp. 43–49. https://doi.org/10.1109/mtas.2006.1700021.
26. Taddia, Y., Russo, P., Lovo, S., & Pellegrinelli, A. (2019). Multispectral UAV monitoring of submerged seaweed in shallow water. Applied Geomatics, 12(1), pp. 19–34. https://doi.org/10.1007/s12518-019-00270-x.
27. Wang, C. (2021). At-sensor radiometric correction of a multispectral camera (RedEdge) for sUAS vegetation mapping. Sensors, 21(22), p. 8224. https://doi.org/10.3390/s21248224.
28. Wang, F.-M., Huang, J.-F., Tang, Y.-L., & Wang, X.-Z. (2007). New vegetation index and its application in estimating leaf area index of rice. https://doi.org/10.1016/S1672-6308(07)60027-4.
29. Zhang, L., & Wang, S. (2007). Application of the WOFOST model in estimating wheat yield at the production level in northern China using MODIS data. Journal of Zhejiang University-SCIENCE A, 8(5), pp. 834–841. https://doi.org/10.1016/S1672-6308(07)60027-4.
30. Zhang, X., & Wang, L. (2021). Monitoring wheat growth using UAV-based multispectral imagery. Remote Sensing, 13(12), p. 2345. https://doi.org/10.3390/rs13122345.
31. Zhu, W., Feng, Z., Dai, S., Zhang, P., & Wei, X. (2022). Using UAV multispectral remote sensing with appropriate spatial resolution and machine learning to monitor wheat scab. https://doi.org/10.3390/agriculture12111785.
32. Zhu, W., Rezaei, E. E., & Nouri, H. (2023). UAV flight height impacts on wheat biomass estimation via machine and deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2023.3302571.
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