Spatial and temporal monitoring of rangelands by the use of satel-lite-derived indices within the rural territorial community

Remote Sensing for Environmental Monitoring

Authors

First and Last Name Academic degree E-mail Affiliation
Svitlana Kokhan Sc.D. svitlana.skokhan [at] gmail.com State Institution "Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine"
Kyiv, Ukraine
Taras Shevchenko National University of Kyiv
Kyiv, Ukraine
Oleg Drozdivskyi Ph.D. dop [at] casre.kiev.ua State Institution "Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine"
Kyiv, Ukraine
Viktor Chekhnii Ph.D. chekhniy [at] gmail.com Institute of Geography of the National Academy of Sciences of Ukraine
Kyiv, Ukraine
Liudmyla Bilous Ph.D. BilousLF [at] knu.ua Taras Shevchenko National University of Kyiv
Kyiv, Ukraine
Yuliia Temna No temnaylia [at] gmail.com State Institution "Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine"
Kyiv, Ukraine

I and my co-authors (if any) authorize the use of the Paper in accordance with the Creative Commons CC BY license

First published on this website: 28.08.2026 - 10:27
Abstract 

Rangelands are important natural and semi-natural ecosystems that provide a number of vital ecosystem functions. Assessing the state of rangelands based on geoinformation products and remote sensing data serves as a key mechanism for regulating their productivity and monitoring the seasonal dynamics of the grass cover.

The study involves geospatial monitoring of the state of natural meadows, pastures, and open areas with herbaceous vegetation, as well as their productivity, based on the use of the 10-day satellite products for Leaf Area Index (LAI, v1 RT6, 2014–2026), Gross Dry Matter Productivity (GDMP, v1 RT6, 2014–2026) with a spatial resolution of 300 m, and Normalized Difference Vegetation Index (NDVI, v3, 2014–present). 

A statistical analysis was conducted to assess the condition of rangelands and evaluate their phenology. The GDMP value varied throughout the study period from 431 kg DM/ha/day in May to 808 kg DM/ha/day in June, reaching a seasonal maximum. A decrease in the index was observed to 702 in July and 619 in August. For leaf area, the index increased from 8.2 m²/m² in May to 11.7 in June, gradually decreased to 10.3 in July, and dropped significantly to 6.2 in August. The NDVI remained relatively stable throughout the season - ranging from 0.62 in May to 0.70 in June, 0.71 in July, and 0.68 in August. Therefore the NDVI,  GDMP and LAI enabled the assessment of rangeland state throughout the growing season, specifically in terms of potential biomass yield,  leaf area, grass stand density.

References 

Bardgett, R. D., Bullock, J. M., Lavorel, S., Manning, P., Schaffner, U., Ostle, N., Chomel, M., Durigan, G., Fry, E. L., Johnson, D., Lavallee, J. M., Le Provost, G., Luo, S., Png, K., Sankaran, M., Hou, X., Zhou, H., Ma, L., Ren, W., Li, X., Ding, Y., Li, Y., & Shi, H. (2021). Combatting global grassland degradation. Nature Reviews Earth & Environment, 2, 720–735. https://doi.org/10.1038/s43017-021-00207-2

Bengtsson, J., Bullock, J. M., Egoh, B., Everson, C., Everson, T., O'Connor, T., O'Farrell, P. J., Smith, H. G., & Lindborg, R. (2019). Grasslands – more important for ecosystem services than you might think. Ecosphere, 10(2), e02582. https://doi.org/10.1002/ecs2.2582

Bilous, L., Samoilenko, V., Shyshchenko, P., & Havrylenko, O. (2022). Ecoregional biodiversity monitoring. Conference Proceedings of the XVI International Scientific Conference “Monitoring of Geological Processes and Ecological Condition of the Environment”, 1–5. European Association of Geoscientists & Engineers. https://doi.org/10.3997/2214-4609.2022580059

Eze, S., Palmer, S. M., & Chapman, P. J. (2018). Soil organic carbon stock in grasslands: Effects of inorganic fertilizers, liming and grazing in different climate settings. Journal of Environmental Management, 223, 74-84. https://doi.org/10.1016/j.jenvman.2018.06.013

Harmon, D. D., Rayburn, E. B., & Griggs, T. C. (2023). Grassland ecology and ecosystem management for sustainable livestock performance. Agronomy, 13(5), 1380. https://doi.org/10.3390/agronomy13051380

Kokhan, S., Burshtynska, K., Bykin, A., Bilous, L., Drozdivskyi, O., & Temna, Y. (2024). Digital farming technologies: Modern state and challenges. In A. Zagorodny, V. Bogdanov, & A. Zaporozhets (Eds.), Nexus of sustainability (pp. 255–280). Springer. https://doi.org/10.1007/978-3-031-66764-0_13

Kokhan, S., Dorozhynskyy, O., Burshtynska, K., Vostokov, A., & Drozdivskyi, O. (2020). Improved approach to the development of the crop monitoring system based on the use of multi-source spatial data. Journal of Ecological Engineering, 21(7), 108-114.https://doi.org/10.12911/22998993/125442

Pergola, M., De Falco, E., & Cerrato, M. (2024). Grassland ecosystem services: Their economic evaluation through a systematic review. Land, 13(8), 1143. https://doi.org/10.3390/land13081143

Rapiya, M., Ramoelo, A., & Truter, W. (2024). Seasonal monitoring of biochemical variables in natural rangelands using Sentinel-1 and Sentinel-2 data. International Journal of Remote Sensing, 45(14), 4737-4763. https://doi.org/10.1080/01431161.2024.2368929

Teague, R., & Kreuter, U. (2020). Managing grazing to restore soil health, ecosystem function, and ecosystem services. Frontiers in Sustainable Food Systems, 4, 534187. https://doi.org/10.3389/fsufs.2020.534187

Wang, B., Waters, C., Orgill, S., Cowie, A., Clark, A., Liu, D. L., Simpson, M., McGowen, I., & Sides, T. (2018). Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia. Ecological Indicators, 88, 425-438. https://doi.org/10.1016/j.ecolind.2018.01.049

Xu, S., Jagadamma, S., & Rowntree, J. (2018). Response of grazing land soil health to management strategies: A summary review. Sustainability, 10(12), 4769. https://doi.org/10.3390/su10124769