Abstract
Introduction. The effective use of agricultural land is a key factor in ensuring sustainable agricultural development and food security. In the context of increasing anthropogenic pressure and climate variability, there is a growing need for modern tools to assess land-use efficiency. Geoinformation technologies and remote sensing provide reliable methods for monitoring vegetation cover and analyzing spatial patterns of land productivity. Materials and methods. This study is based on the use of Geographic Information Systems (GIS) and satellite imagery (Landsat) for the years 2015, 2020, and 2025. The Normalized Difference Vegetation Index (NDVI) was calculated to assess vegetation condition and land productivity. Spatial analysis was carried out using ArcGIS Pro, including classification and comparative analysis of NDVI maps. Results and discussion. The analysis revealed significant spatial differentiation of vegetation cover across the Almaty region. High NDVI values are observed in mountainous and foothill areas, while low values dominate in arid and semi-arid zones. The comparison of NDVI data showed an increase in vegetation density in 2020 compared to 2015, followed by a more heterogeneous pattern in 2025, indicating both improvement and degradation processes. Conclusions. The results confirm that NDVI is an effective indicator for assessing agricultural land use. The integration of GIS and remote sensing technologies allows for objective monitoring of vegetation dynamics and provides a scientific basis for improving land-use efficiency and sustainable land management.
01 Introduction
The full text of the article is available for download in PDF format on the right panel.
02 References
- FAO. (2021). The state of the world’s land and water resources for food and agriculture – Systems at breaking point (SOLAW 2021). Rome: FAO. https://doi.org/10.4060/cb7654en
- D’Acunto, F., Marinello, F., & Pezzuolo, A. (2024). Rural land degradation assessment through remote sensing: Current technologies, models, and applications. Remote Sensing, 16(16), 3059. https://doi.org/10.3390/rs16163059
- Dubovyk, O. (2017). The role of remote sensing in land degradation assessments: Opportunities and challenges. European Journal of Remote Sensing, 50(1), 601–613. https://doi.org/10.1080/22797254.2017.1378926
- Reynolds, J. F. (2007). Global desertification: Building a science for dryland development. Science, 316(5826), 847–851. https://doi.org/10.1126/science.1131634
- Sabljić, L., Lukić, T., Bajić, D., Marković, S. B., & Delić, D. (2024). Application of remote sensing in monitoring land degradation: A case study of Stanari municipality. Open Geosciences, 16(1). https://doi.org/10.1515/geo-2022-0671
- Foley, J. A., DeFries, R., Asner, G. P., et al. (2005). Global consequences of land use. Science, 309(5734), 570–574. https://doi.org/10.1126/science.1111772
- Shokparova, D. K., Sirazhitdinova, M., Bissenbayeva, S. B., & Patel, N. (2025). Remote sensing and GIS-based land assessment in Zhanaarka region of Ulytau oblast, Kazakhstan. Frontiers in Environmental Science, 13, 1516460. https://doi.org/10.3389/fenvs.2025.1516460
- 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
- Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., & Woodcock, C. E. (2012). Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122, 2–10. https://doi.org/10.1016/j.rse.2012.01.010
- Fensholt, R., & Proud, S. R. (2012). Evaluation of Earth observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series. Remote Sensing of Environment, 119, 131–147. https://doi.org/10.1016/j.rse.2011.12.015
- Rafikov, T., Yerbolkyzy, M., & Zhildikbayeva, A. (2024). Application of remote sensing data and NDVI analysis in the East Kazakhstan region. Izdenister Natigeler, 1(101), 183–191. https://doi.org/10.37884/1-2024/18 (in Russian)
- Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
- Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171. https://doi.org/10.1016/j.rse.2014.01.011
- Zhumataeva, Z., Serikbaeva, G., Turganaliev, S., Mukaliev, Z., & Rafikov, T. (2024). Increasing ecological and economic efficiency of land use. Izdenister Natigeler, 2(102), 360–369. https://doi.org/10.37884/2-2024/35
- Ashimkhan, N., Rafikov, T., Zhildikbayeva, A., Mukaliyev, Zh., Doktyrbek, A., & Pentayev, T. (2025). Application of geographic databases for the development of geoportals in Kazakhstan. Izdenister Natigeler, 4(108), 470–480. https://doi.org/10.37884/4-2025/48