Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 5, pp. 145-157
Data from unmanned aerial vehicles and satellite systems in the analysis of spectral characteristics of monoculture plantings of forest-forming species in Siberia
E.I. Ponomarev
1, 2 , N.D. Yakimov
1, 2 , K.V. Krasnoshchekov
1 , A.V. Dergunov
1 1 Krasnoyarsk Science Center SB RAS, Krasnoyarsk, Russia
2 Siberian Federal University, Krasnoyarsk, Russia
Accepted: 22.07.2025
DOI: 10.21046/2070-7401-2025-22-5-145-157
The paper analyzes data from unmanned aerial vehicles (UAV) and satellite systems in order to identify the spectral characteristics of monoculture plantings of Siberian forest-forming species. The study is based on a unique experiment plot in which six forest-forming species of Siberian forest stands, including Siberian pine, larch, aspen, birch, pine, and spruce, were planted in monoculture. A series of instrumental registrations of the spectral characteristics of vegetation in various phenological phases with a periodicity of up to 14 days per season were carried out using the UAV/RedEdge-MX system and quasi-synchronous materials from the Landsat OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) data catalog. The first approximation of model curves describing changes in spectral characteristics of monocultures over the entire phenological cycle (120–275 days of the season) was considered. For each monoculture, seasonal NDVI (Normalized Difference Vegetation Index) trends were illustrated using second-degree polynomial curves. For deciduous stands, the coefficients of determination of the model curve were 0.77–0.95 (for UAV/RedEdge-MX data) and 0.61–0.93 (for the Landsat OLI). For coniferous stands, the coefficient of determination was at the level of 0.41–0.96. There was a satisfactory degree of NDVI value comparability between satellite and UAV data. The discrepancy between the values was recorded at the level of 15–35 %, while the relative deviation between the two sets of data during the phenological period of full summer (180–220 days of the season) was minimal (no more than 15–20 %).
Keywords: Siberia, monoculture plantings, spectral indices, NDVI, Landsat, unmanned aerial vehicle (UAV)
Full textReferences:
- Aleksanin A. I., Timofeev A. N., The influence of observation conditions on the accuracy of NDVI vegetation index calculation from Earth remote sensing data, Cosmic Research, 2023, V. 61, No. S1, pp. S188–S194, DOI: 10.1134/s0010952523700521.
- Aleshko R. A., Alekseeva A. A., Shoshina K. V. et al., Development of the methodology to update the information on a forest area using satellite imagery and small UAVs, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, V. 14, No. 5, pp. 87–99 (in Russian), DOI: 10.21046/2070-7401-2017-14-5-87-99.
- Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S.A, Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2016, 208 p. (in Russian).
- Bezkorovaynaya I. N., Shabalina O. M., Shugaley L. S., The main components of artificial forest biogeocenoses of a multi-year experiment, Siberian J. Forest Science, 2024, No. 3, pp. 83–95 (in Russian), DOI: 10.15372/SJFS20240308.
- Bogdanov A. P., Aleshko R. A., Ilintsev A. S., Relationship between tree crown diameter and various taxation indicators in the north-taiga forest area, Voprosy lesnoi nauki, 2019, V. 2, No. 4, 10 p. (in Russian), DOI: 10.31509/2658-607x-2019-2-4-1-10.
- Ershov D. V., Gavrilyuk E. A., Belova E. I., Nikitina A. D., Determination of the species structure of a forest area using orthophoto plans of unmanned aerial photography, Aktual’nye problemy sovremennogo lesovodstva. Vtorye Mezhdunarodnye chteniya pamyati G. F. Morozova: sbornik statei (Proc. Current Problems of Modern Forestry. 2nd Intern. Readings in Memory of G. F. Morozov), Simferopol’: IT “Arial”, 2020, pp. 141–152 (in Russian).
- Ivanova N. V., Shashkov M. P., Shanin V. N., Obtaining tree stand attributes from unmanned aerial vehicle (UAV) data: the case of mixed forests, Vestnik Tomskogo gosudarstvennogo universiteta. Biologiya, 2021, No. 54, pp. 158–175 (in Russian), DOI: 10.17223/19988591/54/8.
- Kerimov I. A., Elzhaev A. S., Doduev A. A., Remote monitoring of forest areas (using the example of the reference site “Roshni-Chu”), Geologiya i geofizika Yuga Rossii, 2024, V. 14, No. 4, pp. 180–191 (in Russian), DOI: 10.46698/VNC.2024.85.96.015.
- Nazimova D. I., Ponomarev E. I., Konovalova M. E., Role of an altitudinal zonal basis and remote sensing data in the sustainable management of mountain forests, Contemporary Problems of Ecology, 2020, V. 13, No. 7, pp. 742–753, DOI: 10.1134/S1995425520070070.
- Di Gennaro S. F., Toscano P., Gatti M. et al., Spectral comparison of UAV-based hyper and multispectral cameras for precision viticulture, Remote Sensing, 2022, V. 14, No. 3, Article 449, DOI: 10.3390/rs14030449.
- Lu B., Dao P. D., Liu J. et al., Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 2020, V. 12, No. 16, Article 2659, DOI: 10.3390/rs12162659.
- Potapov P., Hansen M. C., Pickens A. et al., The global 2000–2020 land cover and land use change dataset derived from the Landsat archive: First results, Frontiers in Remote Sensing, 2022, V. 3, Article 856903, DOI: 10.3389/frsen.2022.856903.
- Pushparaj J., Hegde A. V., Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery, Arabian J. Geosciences, 2017, V. 10, Article 119, DOI: 10.1007/s12517-017-2878-3.
- Torresan C., Berton A., Carotenuto F. et al., Forestry applications of UAVs in Europe: a review, Intern. J. Remote Sensing, 2017, V. 38, Iss. 8–10, pp. 2427–2447, DOI: 10.1080/01431161.2016.1252477.
- Vivone G., Alparone L., Chanussot J. et al., A critical comparison among pansharpening algorithms, IEEE Trans. Geoscience and Remote Sensing, 2015, V. 53, pp. 2565–2586, DOI: 10.1109/TGRS.2014.2361734.
- Wulder M. A., Masek J. G., Cohen W. B. et al., Opening the archive: How free data has enabled the science and monitoring promise of Landsat, Remote Sensing of Environment, 2012, V. 122, pp. 2–10, DOI: 10.1016/j.rse.2012.01.010.
- Zhang J., Hud J., Liane J. et al., Seeing the forest from drones: testing the potential of lightweight drones as a tool for long-term forest monitoring, Biological Conservation, 2016, V. 198, pp. 60–69, DOI: 10.1016/j.biocon.2016.03.027.
- Zhang X., Friedl M. A., Schaaf C. B. et al., Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data, Global Change Biology, 2004, V. 10, Iss. 7, pp. 1133–1145, DOI: 10.1111/j.1529-8817.2003.00784.x.
- Zhao Y. I., Xu J., Zhong K. et al., Impervious surface extraction by linear spectral mixture analysis with post-processing model, IEEE Access, 2020, V. 8, pp. 128476–128489, DOI: 10.1109/access.2020.3008695.