Development of a GIS Digital Framework for the Central Kazakhstan Region Based on Cartographic Materials and Remote Sensing Data
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DOI:
https://doi.org/10.32523/2616-6771-2025-152-3-122-141Keywords:
GIS basis, digital map, cartographic materials, remote sensing data, Central Kazakhstan RegionAbstract
The article is dedicated to the creation of a digital geoinformation framework for Central Kazakhstan based on the integration of various sources of spatial data. The aim of the study is to develop a structured GIS foundation capable of providing effective support for analysis, monitoring, and spatial modeling in a region with high natural resource potential. The research outlines the stages of collecting and systematizing cartographic materials and remote sensing data, georeferencing and vectorization of features, verification, and spatial data analysis. Methods employed include the collection and structuring of cartographic materials, vectorization of topographic maps, digital elevation model analysis, topology assessment, and more. The outcome of the study is an updated digital GIS framework of the region, which can be utilized for spatial planning, environmental monitoring, infrastructure management, as well as for educational and scientific purposes.
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Ananda, F., Kuria, D.N., Ngigi, M.M. (2016). Towards a new methodology for web GIS development. International Journal of Software Engineering & Applications (IJSEA) 7(4), 47-66. https://doi.org/http://dx.doi.org/10.5121/ijsea.2016.7405
Bissenbayeva, S., Shokparova, D., Abuduwaili, J., Samat, A.,Ma,L., Ge, Y. (2025). Spatiotemporal Dynamics of the Aridity Index in Central Kazakhstan. Sustainability 17(15), 7089. https://doi.org/https://doi.org/10.3390/su17157089
Bocco, G., Mendoza, M., Velázquez, A. (2001). Remote sensing and GIS-based regional geomorphological mapping - a tool for land use planning in developing countries. Geomorphology 39(3-4), 211-219. https://doi.org/https://doi.org/10.1016/S0169-555X(01)00027-7
Burrough, P.A., McDonnell, R.A., Lloyd, C.D. (2015). Principles of geographical information systems. Oxford university press.
CSIRO. (2025). Rangeland and Pasture Productivity (RaPP) Map. Retrieved June 4 from https://www.csiro.au/en/research/animals/livestock/RAPP-Map-GEOGLA
Esri. (2020). ArcGIS Pro documentation. Environmental Systems Research Institute. Retrieved June 4 from https://pro.arcgis.com
Foody, G.M. (2002). Status of land cover classification accuracy assessment. Remote sensing of environment 80(1), 185-201. https://doi.org/https://doi.org/10.1016/S0034-4257(01)00295-4
Goodchild, M.F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal 69, 211-221. https://doi.org/https://doi.org/10.1007/s10708-007-9111-y
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of environment 202, 18-27. https://doi.org/https://doi.org/10.1016/j.rse.2017.06.031
Grohmann, C.H. (2015). Effects of spatial resolution on slope and aspect derivation for regional-scale analysis. Computers & Geosciences 77, 111-117. https://doi.org/http://dx.doi.org/10.31223/osf.io/6xc29
Guindon, B., Zhang, Y. (2017). Application of the Dice Coefficient to Accuracy Assessment of Object-Based Image Classification. Canadian Journal of Remote Sensing 43(1), 48-61. https://doi.org/10.1080/07038992.2017.1259557
Haklay, M., Weber, P. (2008). Openstreetmap: User-generated street maps. IEEE Pervasive computing 7(4), 12-18. https://doi.org/http://dx.doi.org/10.1109/MPRV.2008.80
Höhle, J. (2017). Generating topographic map data from classification results. Remote Sensing 9(3), 224. https://doi.org/https://doi.org/10.3390/rs9030224
Issanova, G., Saduakhas, A., Abuduwaili, J., Tynybayeva, K., Tanirbergenov, S. (2020). Desertification and land degradation in Kazakhstan. Scientific Journal of Pedagogy and Economics 5, 95-102. https://journals.nauka-nanrk.kz/bulletin-science/article/view/829
Jarvis, A., Lane, A., Hijmans, R.J. (2008). The effect of climate change on crop wild relatives. Agriculture, Ecosystems & Environment 126(1-2), 13-23. https://doi.org/https://doi.org/10.1016/j.agee.2008.01.013
Kariyeva, J., Van Leeuwen, W.J. (2011). Environmental drivers of NDVI-based vegetation phenology in Central Asia. Remote Sensing 3(2), 203-246. https://doi.org/https://doi.org/10.3390/rs3020203
Kazhydromet. (2020). Annual Bulletin of Monitoring the State and Climate Change in Kazakhstan. https://www.kazhydromet.kz/uploads/files/403/file/619e16aeb6ec1ezhegodnyy-byulleten-monitoringa-sostoyaniya-i-izmeneniya-klimata-kazahstana-za-2020.pdf?utm_source=chatgpt.com
Kurowska, K., Marks-Bielska, R., Bielski, S., Aleknavičius, A., Kowalczyk, C. (2020). Geographic information systems and the sustainable development of rural areas. Land 10(1), 6. https://doi.org/https://doi.org/10.3390/land10010006
Lillesand, T., Kiefer, R.W., Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.
Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W. (2015). Geographic information science and systems. John Wiley & Sons.
Makanga, P.T., Schuurman, N., Sacoor, C., Boene, H., Von Dadelszen, P., Firoz, T. (2016). Guidelines for creating framework data for GIS analysis in low‐and middle‐income countries. The Canadian Geographer/Le Géographe canadien 60(3), 320-332. https://doi.org/http://dx.doi.org/10.1111/cag.12295
Minghini, M., Lupia, F., Napolitano, M., Palmas, A., Delucchi, L. (2017). Collaborative mapping response to disasters through OpenStreetMap: the case of the 2016 Italian earthquake. Geam. geoingegneria ambientale e mineraria 151(2), 21-26.
Miu, M., Zhang, X., Dewan, M., Wang, J. (2017). Aggregation and visualization of spatial data with application to classification of land use and land cover. arXiv preprint arXiv:1704.05860. https://doi.org/https://doi.org/10.48550/arXiv.1704.05860
Munkhdulam, O., Clement, A., Amarsaikhan, D., Yokoyama, S., Erdenesukh, S., Sainbayar, D. (2022). Detection of Anthropogenic and Environmental Degradation in Mongolia Using Multi-Sources Remotely Sensed Time Series Data and Machine Learning Techniques. In Environmental Degradation in Asia: Land Degradation, Environmental Contamination, and Human Activities (17-47 p.). Springer. https://doi.org/https://doi.org/10.1007/978-3-031-12112-8_2
Pachikin, K., Erokhina, O., Funakawa, S. (2014). Soils of Kazakhstan, Their Distribution and Mapping. In L. Mueller, A. Saparov, & G. Lischeid (Eds.), Novel Measurement and Assessment Tools for Monitoring and Management of Land and Water Resources in Agricultural Landscapes of Central Asia (519-533 p.). Springer International Publishing. https://doi.org/10.1007/978-3-319-01017-5_32
Reuter, H.I., Nelson, A., Jarvis, A. (2007). An evaluation of void‐filling interpolation methods for SRTM data. International Journal of Geographical Information Science 21(9), 983-1008. https://doi.org/https://doi.org/10.1080/13658810601169899
Roy, D.P., Wulder, M.A., Loveland, T.R., Woodcock, C.E., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of environment 145, 154-172. https://doi.org/https://doi.org/10.1016/j.rse.2014.02.001
Tatem, A.J., Noor, A.M., Von Hagen, C., Di Gregorio, A., Hay, S.I. (2007). High resolution population maps for low income nations: combining land cover and census in East Africa. PloS One 2(12), e1298. https://doi.org/https://doi.org/10.1371/journal.pone.0001298
WorldBank. (2021). Kazakhstan: Sustainable Livestock Development Program for Results. https://www.worldbank.org/en/country/kazakhstan/brief/sustainable-livestock-development-program-for-results
Wulder, M.A., Loveland, T.R., Roy, D.P., Crawford, C.J., Masek, J.G., Woodcock, C.E., Allen, R.G., Anderson, M.C., Belward, A.S., Cohen, W.B. (2019). Current status of Landsat program, science, and applications. Remote sensing of environment 225, 127-147. https://doi.org/https://doi.org/10.1016/j.rse.2019.02.015
Zhao, Y., Abdelkareem, M., Abdalla, F. (2024). Remote sensing and GIS techniques in Monitoring and mapping Land System Change in semi-arid environments. Environmental Earth Sciences 83(13), 420. https://doi.org/https://doi.org/10.1007/s12665-024-11706-y
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Copyright (c) 2025 Sh. Kairova, N. Zhengissova, Zh. Toktarov, K. Zulpykharov, D. Tokkozhayev, A. Assanbayeva, O. Taukebayev (Author)
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