USE OF INTEGRAL VEGETATION INDICES IN THE ANALYSIS OF VEGETATION COVER CONDITION AND ITS CHANGE TRENDS BASED ON SATELLITE DATA
Keywords:
remote sensing, vegetation indices, Integral Vegetation Condition Index (IVCI), drought analysis and monitoring, hydrothermal coefficient (HTC), grain yieldAbstract
Drought phenomena are among the most significant factors determining the productivity of agroecosystems and the resilience of agricultural production under climate change. For Kazakhstan, the majority of whose territory falls within the zone of risky agriculture, drought monitoring, vegetation cover assessment, and the identification of change trends are of particular importance. The increasing frequency and intensity of dry periods lead to substantial losses in cereal crop yields, directly affecting the country's food security. Traditional methods for assessing agroclimatic conditions are based on ground-based observations of air temperature and precipitation. Despite their informativeness, these data are limited by the spatial discreteness of the meteorological station network and do not fully reflect the spatial diversity of drought conditions. In this regard, remote sensing (RS) methods have gained particular importance, providing systematic and spatially continuous monitoring of vegetation conditions. Among the tools of remote sensing, vegetation indices have become widely used for assessing vegetation dynamics, detecting stress conditions, and identifying interannual variations. The aim of this study is to assess the dynamics of vegetation and drought conditions in the northern regions of Kazakhstan (Akmola, Kostanay, and North Kazakhstan regions) for the period 2000–2023 using vegetation indices, including the developed IVCI index, as well as to verify it against ground-based agroclimatic indicators and cereal crop yield statistics.
References
- Al Nadabi M.S.; D’Antonio P.; Fiorentino C.; Scopa A.; Shams E.M.; Fadl M.E. (2024). Utilizing the Google Earth Engine for Agricultural Drought Conditions and Hazard Assessment Using Drought Indices in the Najd Region, Sultanate of Oman.Remote Sens. 16, 2960. https://doi.org/10.3390/rs16162960
- Baisholanov S.S., Pavlova V.N., Zhakieva A.R., Chernov D.A., Gabbasova M.S. (2018) Agroclimatic resources of Northern Kazakhstan. Hydrometeorological research and forecasts.1(367),168-184. https://cyberleninka.ru/article/n/agroklimaticheskoerayonirovanie-selskohozyaystvennyh-kultur-v severnom-kazahstane
- Gitelson A., Kogan F., Zakarin E., Spivak L., Lebed L. (1995) Estimation of seasonal dynamics of arid zone pasture and crop productivity using NOAA/AVHRR data. Final Report. Submitted to the Office of the Science Advisor USAID. https://pdf.usaid.gov/pdf_docs/PNABY317.pdf
- Kogan F.N. (1990) Remote sensing of weather impacts on vegetation non-homogenous areas. Int.J.of Remoute Sensing. 11, 1405- 1419 https://doi.org//10.1080/01431169008955102
- Kogan F.N. (1994) Application of vegetation index and brightness temperature for drought detection. Adv. Space Res. - Vol. 15. No. 11. P. 91-100. https://doi.org/10.1016/0273-1177(95)00079-T
- Kogan F.N. (1997) Global drought watch from space. Bulletin of the American Meteorological Society. N 78. P. 621-636. DOI: https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2
- Kogan F., Gitelson A., Zakarin E., Spivak L., Lebed L. (2003) AVHRR-Based Spectral Vegetation Index for Quantitative Assessment of Vegetation State and Productivity: Calibration and Validation. Photogrammetric Engineering & Remote Sensing., 69, 8. 899-906. https://doi.org/10.14358/PERS.69.8.899
- Kronberg, (1988) P. Remote sensing of the Earth. Mir. https://www.geokniga.org/books/20459.
- Mann H. B. (1945). Nonparametric Tests Against Trend. Econometrica: Journal of the Econometric Society, 13(3), 245–259. https://doi.org/10.2307/1907187
- Orlov A.I. (2004). Applied Statistics: Textbook. Moscow: Exam Publishing House, 656 p. http://www.aup.ru/books/m163/
- Rouse J. W., Haas R.H., Schell J.A., and Deering D.W. (1973). Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA SP-351 I, 309-317. https://doi.org/10.13140/RG.2.1.3691.8244
- Spivak L.. Vitkovskaya I.. Batyrbayeva M. (2008). Analysis of inter seasonal variations of productivity of vegetative cover of Kazakhstan using temporal remote sensing rows. News of Nat. Acad. of Sci. of Kazakhstan. Phys.-Math.-Ser., 4. https://doi.org/10.31489/2021No2/90-95.
- Spivak L., Vitkovskaya I., Batyrbayeva M., Terekhov A. (2009). Detection of desertification zones using multi-year rows of remote sensing data// Series “NATO science for peace and security: environmental security”. https://doi.org/10.1007/978-90-481-9618-0_26
- Spivak L.F., Batyrbayeva M.Zh., Vitkovskaya I.S., Muratova N.R., Berdygulov N.I., Mombekova D.K., Zhaksybekova K.A., Kapatay Zh.T. (2016). Possibility of using satellite data in assessing aridity in Kazakhstan. Hydrometeorology and ecology. 3, 26-36. https://journal.kazhydromet.kz/index.php/kazgidro/article/view/451/464
- Vitkovskaya, I., Batyrbayeva, M., Berdigulov, N., & Mombekova, D. (2024). Prospects for drought detection and monitoring using long-term vegetation indices series from satellite data in Kazakhstan. Land, 13, 2225. https://doi.org/10.3390/land13122225
- Handbook of Drought Indicators and Indices. Integrated Drought Management Programme (IDMP). WMO, (2016). No. 1173, 60 p. https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf
- VIII National Communication and V Biennial Report Republic of Kazakhstan UN Framework Convention about climate change. (2022). http://infkkts.int/site/default/files/resource/8NS_Kazakhstan_2022v1.0.pdf.
- Meteorological Database website https://meteo.kazhydromet.kz/database_meteo/
- Agencies for strategic planning and reforms of the Republic of Kazakhstan https://stat.gov.kz/ru/region/akmola/dynamic-tables/1485/
