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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 5, pp. 29-37

Assessment of the effectiveness of unmanned aerial system data post-processing algorithms to improve the accuracy of RGB vegetation indices

M.V. Tikhonova 1 , Ya.S. Zhigaleva 1 , N.A. Aleksandrov 1 , A.V. Buzylev 1 , A.M. Yaroslavtsev 1 
1 Russian State Agrarian University — Moscow Timiryazev Agricultural Academy, Moscow, Russia
Accepted: 09.07.2025
DOI: 10.21046/2070-7401-2025-22-5-29-37
The introduction of unmanned aerial vehicles (UAVs) in agroecological monitoring is limited by the errors that occur during photogrammetric data processing. This paper evaluates the effectiveness of a specialized post-processing algorithm aimed at eliminating the artifacts of orthomosaic stitching obtained from a standard RGB camera (Red, Green, Blue). The study is based on the example of a post-agricultural agroecosystem in Penza Region. A comparative analysis of five vegetation indices was conducted: NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistant Index), ExG (Excess Green Vegetation Index), TGI (Triangular Greenness Index), and VEG (Vegetativen Index), calculated based on UAV data before and after correction. The NDVI (Normalized Difference Vegetation Index) obtained from multispectral remote sensing data from the Sentinel-2 (L2A) satellite was used as a reference indicator. Regression analysis with calculation of the determination coefficient (R2) was used to assess the accuracy. It was found that the application of the correction algorithm provides a systematic and statistically significant improvement in the comparability of RGB indices with satellite data. For a point sample across the entire study area, the R2 increased by an average of 35 %. The best results were shown by the NGRDI (R2 increased from 0.372 to 0.506) and the VARI (R2 increased from 0.361 to 0.494).
Keywords: remote sensing, UAV, vegetation indices, NDVI, image processing, TGI, VARI, ExG, NGRDI, VEG, agroecology
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