Website of D. Serikbayev EKTU

THE EFFECTIVENESS OF GIS TOOLS IN THE AGRICULTURAL LAND MONITORING AND MANAGEMENT SYSTEM

Authors

Name Affiliation
Zhazira Zhumatayeva

Al-Farabi Kazakh National University

Zhandos Mukaliyev

Al-Farabi Kazakh National University

Dariga Onalbayeva

Kazakh National Agrarian Research University

Kamila Kassymkhanova

Kazakh National Research Technical University named after K.I. Satpayev

Saken Turganaliev

Al-Farabi Kazakh National University

Issue:

Volume 1, No. 1, 2026

Pages:

30-41

Views:

17

Keywords:

geographic information systems, agricultural lands, land monitoring, spatial analysis, remote sensing, land management, resource management, remote sensing of the Earth

Abstract

The article evaluates the effectiveness of geoinformation tools in monitoring and managing agricultural lands using the example of the Abai region and the East Kazakhstan region. The topographic base, digital soil maps and data from the Sentinel-2 satellite are combined into a single geobase: true-color composites and the index of normalized vegetation difference (NDVI), as well as a thematic map of agricultural cover. The methodology includes the masking of atmospheric barriers, the creation of seasonal composites, regional NDVI statistics on land use classes and soil types, as well as criteria for the effectiveness of the process (accuracy, efficiency, labor intensity, interoperability). State maps and management repositories of agricultural landscapes have been obtained, defining the zones of agrostress and the differences between "arable land" and "pasture degradation". The scientific discovery consists in the practice-oriented integration of satellite indicators and the soil zone for updating digital soil maps. The practical importance lies in the reproducibility of updates and the availability of data for use in management.

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

  1. Frimpong, B. F., Koranteng, A., Atta-Darkwa, T., Junior, O. F., & Zawiła-Niedźwiecki, T. (2023). Land cover changes utilising Landsat satellite imageries for the Kumasi metropolis and its adjoining municipalities in Ghana (1986–2022). Sensors, 23, 2644. https://doi.org/10.3390/s23052644
  2. Hively, W. D., McCarty, G. W., Reeves, J. B., Lang, M. W., Oesterling, R., & Delwiche, S. R. (2011). Use of airborne hyperspectral imagery to map soil properties in tilled agricultural fields. Applied and Environmental Soil Science, 2011, 1–13. https://doi.org/10.1155/2011/358193
  3. Ivlieva, N. G., Manuhov, V. F., & Alferina, A. V. (2020). Cartographic and geoinformation support of soil and agrochemical research (on the example of a separate agricultural enterprise). InterCarto. InterGIS. Geoinformation support for sustainable development of territories: Proceedings of the International Conference, 26(2), 41–53. https://doi.org/10.35595/2414-9179-2020-2-26-41-53
  4. Joiner, J., Yoshida, Y., Anderson, M. C., Holmes, T., Hain, C., Reichle, R. H., Koster, R. D., Middleton, E. M., & Zeng, F. (2018). Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales. Remote Sensing of Environment, 219, 339–352. https://doi.org/10.1016/j.rse.2018.10.020
  5. Karmakar, P., Teng, S. W., Murshed, M., Pang, P., Li, Y., & Hao, L. (2024). Crop monitoring by multimodal remote sensing: A review. Remote Sensing Applications: Society and Environment, 33, 101093. https://doi.org/10.1016/j.rsase.2023.101093
  6. Khanal, S., Kushal, K. C., Fulton, J. P., Shearer, S., & Ozkan, E. (2020). Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sensing, 12, 3783. https://doi.org/10.3390/rs12223783
  7. Kingra, P. K., Majumder, D., & Singh, S. P. (2016). Application of remote sensing and GIS in agriculture and natural resource management under changing climatic conditions. Agricultural Research Journal, 53, 295–302. https://doi.org/10.5958/2395-146X.2016.00058.2
  8. Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors, 7(11), 2636–2651. https://doi.org/10.3390/s7112636
  9. Nie, J., & Yang, B. (2021). A detailed study on GPS and GIS enabled agricultural equipment field position monitoring system for smart farming. Scalable Computing: Practice and Experience, 22. https://doi.org/10.12694/scpe.v22i2.1882
  10. Padhiary, M., Kumar, R., & Sethi, L. N. (2024). Navigating the future of agriculture: A comprehensive review of automatic all-terrain vehicles in precision farming. Journal of The Institution of Engineers (India) Series A, 105, 767–782. https://doi.org/10.1007/s40030-024-00816-2
  11. Saha, A., Patil, M., Goyal, V. C., & Rathore, D. S. (2019). Assessment and impact of soil moisture index in agricultural drought estimation using remote sensing and GIS techniques. Proceedings, 7, 2. https://doi.org/10.3390/ecws-3-05802
  12. Sarmah, K., Deka, C. R., Sharma, U., & Sarma, R. (2018). Role of GIS-based technologies in sustainable agriculture resource planning & management using spatial decision support approach. International Journal of Innovative Research in Engineering & Management, 5, 30–34.
  13. Shekhar, S., Gusain, R., Vidhyarthi, A., & Prakash, R. (2022). Role of remote sensing and GIS strategies to increase crop yield. In 2022 International Conference on Advances in Computing, Communication and Materials (ICACCM) (pp. 1–6). IEEE. https://doi.org/10.1109/ICACCM56405.2022.10009217
  14. Sivakumar, V. G., Vijaya Baskar, V., Vadivel, M., Vimal, S. P., & Murugan, S. (2023). IoT and GIS integration for real-time monitoring of soil health and nutrient status. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1265–1270). IEEE. https://doi.org/10.1109/ICSSAS57918.2023.10331694
  15. Sun, T., Cheng, W., Abdelkareem, M., & Al-Arifi, N. (2022). Mapping prospective areas of water resources and monitoring land use/land cover changes in an arid region using remote sensing and GIS techniques. Water, 14, 2435. https://doi.org/10.3390/w14152435
  16. Yang, X., & Kuang, H. (2022). Evaluation of ecological quality in southeast Chongqing based on modified remote sensing ecological index. Scientific Reports. https://doi.org/10.1038/s41598-022-19851-9
  17. Rafikov, T., Zhumatayeva, Z., Mukaliyev, Z., & Zhildikbayeva, A. (2024). Evaluating Land Degradation in East Kazakhstan Using NDVI and Landsat Data. International Journal of Design & Nature and Ecodynamics, 19(5), 1677–1686. https://doi.org/10.18280/ijdne.190521
  18. Vtar, R., Komolafe, A. A., Kouser, A., et al. (2020). Assessing sustainable development prospects through remote sensing: A review. Remote Sensing Applications: Society and Environment, 20, 100402. https://doi.org/10.1016/j.rsase.2020.100402

Volume 1, No. 1, 2026

Zhumatayeva, Zh., Mukaliyev, Zh., Onalbayeva, D., Kassymkhanova, К., & Turganaliev, S. (2026). THE EFFECTIVENESS OF GIS TOOLS IN THE AGRICULTURAL LAND MONITORING AND MANAGEMENT SYSTEM. EKTU Journal of Engineering Sciences, 1(1), 30-41. https://doi.org/10.51885/3134-8009_JES_2026_1_3