Comparison and synthesis of image analysis algorithms: processing NDVI and RGB for UAV-based environmental monitoring
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https://doi.org/10.32523/3107-278X-2026-154-1-100-117Keywords:
NDVI, UAV, RGB imagery processing, vegetation monitoring, multispectral imaging, radiometric calibration, environmental monitoringAbstract
The assessment of vegetation health stands as a fundamental operation which precision agriculture and ecology and environmental risk management require. The Normalized Difference Vegetation Index (NDVI) uses red and near-infrared (NIR) reflectance data to function as the primary index which enables vegetation health assessment and stress detection. The implementation of these systems faces challenges when trying to deploy them in budget-friendly operations because they need multispectral sensors which include NIR channels. The research evaluates three algorithmic approaches through a complete analysis which includes (i) NDVI method and (ii) RGB-only channel transformation method and (iii) a synthesized hybrid algorithm that applies NDVI normalization to green-dominance for standard RGB cameras. The research includes mathematical derivations together with radiometric interpretation methods and calibration needs and error propagation calculations for all investigated approaches. The research develops a system which performs UAV image data preprocessing through geometric correction and radiometric normalization and noise filtering and illumination compensation. The system becomes more useful through discussions about UAV implementation scenarios and flight planning constraints and geospatial post-processing workflows. The text includes forest-fire-related use-cases as examples of fast assessment situations which vegetation stress maps help with post-event choices yet these examples do not represent the main focus. The research findings show that NDVI based on classical methods delivers better results for quantitative biophysical analysis but RGB-based methods work well for qualitative segmentation when using limited hardware resources. The developed algorithm delivers superior vegetation identification results than RGB transformation methods and functions as a useful solution for organizations which lack NIR-equipped systems. The document presents recommendations which help users choose methods based on their specific requirements regarding accuracy levels and budget constraints and time needs for deployment and system operational difficulties.
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Rouse, J. W., Haas, R. H., Schell, J. A., & Deering D. W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. In: Proc. 3rd Earth Resources Technology Satellite-1 Symposium, 309–317.
Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosystems Engineering, 114, 4, 358–371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712. https://doi.org/10.1007/s11119-012-9274-5
Ballesteros, R., Ortega, J. F., Hern´andez, D., & Moreno, M. (2015). Characterization of vitis viniferal. Canopy using unmanned aerial vehicle-based remote sensing and photogrammetry tech niques. American Journal of Enology and Viticulture, 66, 2, 120–129. https://doi.org/10.5344/ajev.2014.14070
Xie, X., Zhao, W., & Yin, G. (2023). TAVIs: Topographically adjusted vegetation index for a reliable proxy of gross primary productivity in mountain ecosystems. IEEE Transactions on Geoscience and Remote Sensing PP(99), 1-1. https://doi. org/10.1109/TGRS.2023.3336727
Xu, P., Lv, T., Dong, S., Cui, Z., Luo, X., Jia, B., Jeon, C. O., & Zhang, J. (2022). Association between intestinal microbiome and inflammatory bowel disease: insights from bibliometric analysis. Computational and Structural Biotechnology. J, 20, 1716–1725. https://doi. org/10.1016/j.csbj.2022.04.006
Yan, K., Gao, S., Chi, H., Qi, J., Song, W., Tong, Y., Mu, X., & Yan, G. (2020). Evaluation of the vegetation-index-based dimidiate pixel model for fractional vegetation cover estimation. IEEE Transaction on Geoscience and Remote Sensing, 60, 1-14. https://doi.org/10.1109/ TGRS.2020.3048493
Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google earth engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogrammetry and Remote Sensing, 164, 152–170. https://doi.org/ 10.1016/j.isprsjprs.2020.04.001
Al-Waeli, A. M. T. (2020). Assessment of soil sensitivity for physical degradation in Abi Garaq by geomatics techniques. International Journal of Agricaltural Statistical Sciences, 16(1), 1865-1873. https://connectjournals.com/03899.2020.16.1865
Xia, T., Kustas, W. P., & Andersonetal, M. C. (2016). Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one-and two-source modeling schemes. Hydrology and Earth System Sciences, 20, 4, 1523–1545. https://doi.org/10.5194/hess-20-1523-2016
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503–510. https://doi.org/10.1016/j.tree.2005.05.011
Hatfield, J. L., Prueger, J., Sauer, T. J., & Dold, C. (2019). Applications of Vegetative Indices from Remote Sensing to Agriculture: Past and Future. Inventions, 4, 71. https://doi.org/10.3390/inventions4040071
De Carvalho, R. M., & Szlafsztein, C. F. (2019). Urban vegetation loss and ecosystem services: the influence on climate regulation and noise and air pollution. Environmental Pollution, 245, 844–852. https://doi.org/10.1016/j.envpol.2018.10.114
Bartesaghi-Koc, C., Osmond, P., & Peters, A. (2018). Mapping and classifying green infrastructure typologies for climate-related studies based on remote sensing data. Urban Forestry & Urban Greening, 37, 154–167. https://doi.org/10.1016/j.ufug.2018.11.008
Shahtahmassebi, A., Li, C., Fan, Y., Wu, Y., Gan, M., Wang, K., Malik, A., & Blackburn, A. (2020). Remote sensing of urban green spaces: a review. Urban Forestry & Urban Greening, 57, 126946. https://doi.org/10.1016/j.ufug.2020.126946
Wang, K., Wang, T., & Liu, X. (2019). A review: individual tree species classification using integrated airborne LiDAR and optical imagery with a focus on the urban environment. Forests, 10, 1. https://doi.org/10.3390/f10010001
Kothencz, G., Kulessa, K., Anyyeva, A., & Lang, S. (2018). Urban vegetation extraction from VHR (tri-) stereo imagery - a comparative study in two central European cities. European Journal of Remote Sensing, 51, 285–300. https://doi.org/10.1080/22797254.2018.1431057
Hartling, S., Sagan, V., Sidike, P., Maimaitijiang, M., & Carron, J. (2019). Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning. Sensors, 19, 1284. https://doi.org/10.3390/s19061284
Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2016). The use of the normalized difference vegetation index (NDVI) to assess land degradation at multiple scales: a review of the current status, future trends, and practical considerations, in use of the Normalized Difference Vegetation Index (NDVI) to assess land degradation at multiple scales. SpringerBriefs in Environmental Science. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-24112-8
Zhang, L., Zhang, Z., Luo, Y., Cao, J., Xie, R., & Li, S. (2021). Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Agriculture and Forest Meteorology, 311, 108666. https://doi.org/10.1016/j.agrformet.2021.108666
Muhsin, I. J. (2016). Change detection of remotely sensed image using NDVI subtractive and classification methods. Iraqi Journal of Physics, 14(29), 125-137. https://doi.org/10.30723/ijp. v14i29.228
Bhandari, A. K., Kumar, A., & Singh, G. K. (2012). Feature extraction using normalized differencevegetation index (NDVI): A case study of Jabalpur city. Materials of Processing Technology, 6, 612-621. https:// doi.org/10.1016/j.protcy.2012.10.074
Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pig ment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 2-3, 337–354.
Jin, S., & Sader, S. A. (2005). Comparison of time-series NDVI and UAV-based imagery for monitoring forest health. Remote Sensing of Environment, 94(2), 189–197. https://doi.org/10.1016/j.rse.2004.10.012
Lobo, T. D., Queiroz, R., Nigri, P., Elena, C. L. R. L., Marcato, J. J., Martins, J., Ola, B. P., Gonçalves, W. N., & Liesenberg, V. (2020). Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery. Sensors, 20, 563. https://doi.org/10.3390/s20020563
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Copyright (c) 2026 R. Askaruly, A. Abilova, A. Syzdykov, Kh. Moldamurat (Author)
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