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All issues Volume 590 (2024) E3S Web of Conf., 590 (2024) 04007 References
Open Access
Issue
E3S Web of Conf.
Volume 590, 2024
6th Annual International Scientific Conference on Geoinformatics - GI 2024: “Sustainable Geospatial Solutions for a Changing World”
Article Number 04007
Number of page(s) 11
Section GIS in Land Use and Management, and Cadaster
DOI https://doi.org/10.1051/e3sconf/202459004007
Published online 13 November 2024
  1. Gaur, S.; Singh, R. A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability 2023, 15, 903. https://doi.org/10.3390/su15020903 [CrossRef] [Google Scholar]
  2. Turner, B., Meyer, W. B., & Skole, D. L. (1994). Global land-use/land-cover change: towards an integrated study. In Ambio (1 ed., Vol. 23, pp. 91-95) [Google Scholar]
  3. Allan, A.; Soltani, A.; Abdi, M.H.; Zarei, M. Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review. Land 2022, 11, 1222. https://doi.org/10.3390/land11081222 [CrossRef] [Google Scholar]
  4. Changes in Land use and land cover: A global perspective: edited by William B. Meyer and BL Turner, II Cambridge University Press, 1994. 537 page, ISBN 0 521 47085 4. https://www.academia.edu/72684185/Changes_in_Land_use_and_land_cover_A_glob al_perspective_edited_by_William_B_Meyer_and_BL_Turner_II_Cambridge_Univers ity_Press_1994_35_00_49_95_hbk_xi_537_pages_ISBN_0_521_47085_4 [Google Scholar]
  5. Wang Y., Sun Y., Cao X., Wang Yi, Zhang W., Xinglu Cheng X. A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, Vol. 206, Pages 311-334, https://doi.org/10.1016/j.isprsjprs.2023.11.014. [CrossRef] [Google Scholar]
  6. Alikhanov B., Pulatov B., Samiev L. The Detection of Past and Future Land Use and Land Cover Change in Ugam Chatkal National Park, Uzbekistan, Using CA-Markov and Random Forest Machine Learning Algorithms. Forum Geografi. 2024, 38(2), 121-137. DOI: 10.23917/forgeo.v38i2.4221 [CrossRef] [Google Scholar]
  7. Talukdar, S.; Singha, P.; Mahato, S.; Shahfahad; Pal, S.; Liou, Y.-A.; Rahman, A. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sens. 2020, 12, 1135. https://doi.org/10.3390/rs12071135 [CrossRef] [Google Scholar]
  8. Koehler, J.; Kuenzer, C. Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review. Remote Sens. 2020, 12, 3513. https://doi.org/10.3390/rs12213513 [CrossRef] [Google Scholar]
  9. Ouma, Yashon O., Okuku, Clinton O., Njau, Evalyne N., Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya, Complexity, 2020, 9570789, 23 pages, 2020. https://doi.org/10.1155/2020/9570789 [Google Scholar]
  10. Alipbeki O., Alipbekova Ch, Sterenharz A., Toleubekova T., Aliyev M., Mineyev N., Amangaliyev K. A. Spatiotemporal Assessment of Land Use and Land Cover Changes in Peri-Urban Areas: A Case Study of Arshaly District, Kazakhstan. Sustainability, 2020, Vol. 12, Issue 4, 10.3390/su12041556. Available online: https://www.mdpi.com/2071-1050/12/4/1556 [CrossRef] [Google Scholar]
  11. Alipbeki O., Alipbekova Ch., Sterenharz A., Toleubekova Zh., Makenova S., Aliyev M., Mineyev N. Analysis of Land-Use Change in Shortandy District in Terms of Sustainable Development. Land, 2020, Vol. 9, Issue 5, 147. doi:10.3390/land9050147 [CrossRef] [Google Scholar]
  12. Alipbeki O., Alipbekova Ch., Mussaif G., Mineyev N., Aliyev M., Akhmetov B. and Turegeldiyeva R. The driving forces of changes in land use of a Peri-urban area: The case of Tselinograd district, Kazakhstan 05013. Published online: 12 May 2023 E3S Web Conf. Volume 386, 2023. Annual International Scientific Conferences: GIS in Central Asia – GISCA 2022 and Geoinformatics – GI 2022 “Designing the Geospatial Ecosystem” DOI: https://doi.org/10.1051/e3sconf/202338605013 [Google Scholar]
  13. Alipbeki O., Mussaif G., Alipbekova C., Kapassova A., Grossul P., Aliyev M., Mineyev N. Untangling the Integral Impact of Land Use Change, Economic, Ecological and Social Factors on the Development of Burabay District (Kazakhstan) during the Period 1999–2021. Sustainability 2023, 15, 7548. https://doi.org/10.3390/su15097548 [CrossRef] [Google Scholar]
  14. Alipbeki O., Alipbekova C., Mussaif G., Grossul P., Zhenshan D., Muzyka O., Turekeldiyeva R., Yelubayev D., Rakhimov D., Kupidura P. Aliken E. Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan. Agronomy 2024, 14, 268. https://doi.org/10.3390/agronomy14020268 [CrossRef] [Google Scholar]
  15. Seitkazy, M., Beisekenov, N., Taukebayev, O., Zulpykharov, K., Tokbergenova, A., Duisenbayev, S., Sarybaev, E., & Turymtayev, Z. (2023). Forecasting Land Use Dynamics in Talas District, Kazakhstan, Using Landsat Data and the Google Earth Engine (GEE) Platform. Sustainability, 16(14), 6144. https://doi.org/10.3390/su16146144 [Google Scholar]
  16. Earth Engine Data Catalog. Available online: https://developers.google.com/earth- engine/datasets (accessed on 02 September 2024). [Google Scholar]
  17. IBM SPSS Regression. Available online: https://www.ibm.com/products/spss- statistics/regression (accessed on 02 September 2024). [Google Scholar]
  18. Abay District, Karaganda Region. Available online: https://en.wikipedia.org/wiki/Abay_District,_Karaganda_Region (accessed on 02 September 2024). [Google Scholar]
  19. Bureau of National statistics. Agency for Strategic planning and reforms of the Republic of Kazakhstan. Available online: https://stat.gov.kz/ (accessed on 02 September 2024). [Google Scholar]
  20. Compton J. Tucker, Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, Volume 8, Issue 2, 1979, Pages 127-150, ISSN 0034-4257, https://doi.org/10.1016/0034-4257(79)90013-0. [CrossRef] [Google Scholar]
  21. H. Q. Liu and A. Huete, “A feedback based modification of the NDVI to minimize canopy background and atmospheric noise,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 2, pp. 457-465, March 1995, doi: 10.1109/TGRS.1995.8746027. [CrossRef] [Google Scholar]
  22. McFeeters, S.K. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432. [CrossRef] [Google Scholar]
  23. Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Brisco, B.; Homayouni, S.; Gill, E.; DeLancey, E.; Bourgeau-Chavez, L. Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform. Can. J. Remote Sens. 2020, 46, 15–33. [CrossRef] [Google Scholar]
  24. Accuracy Metrics. Available online: https://gsp.humboldt.edu/olm_2019/courses/GSP_216_Online/lesson6-2/metrics.html (accessed on 02 September 2024). [Google Scholar]
  25. Warrens, J.M. Properties of the quantity disagreement and the allocation disagreement. Int. J. Remote Sens. 2015, 36, 1439–1446. [CrossRef] [Google Scholar]
  26. PCA/Factor node. Available online: https://www.ibm.com/docs/en/cloud-paks/cp-data/5.0.x?topic=modeling-pcafactor-node (accessed on 02 September 2024). [Google Scholar]

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