Assessing Spatiotemporal Dynamics of AI-based Land Use and Land Cover through Sentinel-2 Time-Series Imagery: A Case Study of the Shimbay District, Republic of Karakalpakstan, Uzbekistan

Ilyaskhoja Jumaniyazov; Mukhiddin Juliev; Mamanbek Reimov; Rustam Oymatov; Aziz Inamov; Begench Yunusov; Dilbar Abdramanova

doi:10.23917/forgeo.8724

Authors

Ilyaskhoja Jumaniyazov Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent https://orcid.org/0000-0002-5175-6911
Mukhiddin Juliev Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent; Tashkent International University of Education, Imam Bukhariy street, Tuzel-2 block, Yashnabad district, Tashkent; Tashkent State Technical University, Universitet 2, Tashkent, 100095 https://orcid.org/0000-0002-8582-0352
Mamanbek Reimov Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent https://orcid.org/0009-0009-3578-8719
Rustam Oymatov Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent https://orcid.org/0009-0008-1258-1961
Aziz Inamov Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, 100000, Tashkent https://orcid.org/0000-0001-8753-7748
Begench Yunusov Ministry of Water Resources of the Republic of Uzbekistan, Karasuv-4, building 11, 100187, Tashkent https://orcid.org/0009-0009-0499-2258
Dilbar Abdramanova Karakalpak State University named after Berdakh, 1 Str. Ch.Abdirov, 230112, Nukus, Republic of Karakalpakstan https://orcid.org/0009-0003-6275-6428

DOI:

https://doi.org/10.23917/forgeo.8724

Keywords:

Land Cover, Land Use, GIS, Remote sensing, Sentinel-2, Accuracy assessment, Shimbay

Abstract

Land use and land cover (LULC) change is a vital indicator of environmental transformation and sustainable land management, particularly in arid and semi-arid regions such as Karakalpakstan. Although several studies have explored LULC dynamics across Central Asia, most have focused on large-scale or regional assessments, leaving a gap in district-level analyses that capture localized land transformation processes. This study addresses this gap by examining the temporal dynamics of LULC in the Shimbay district, one of the most populous districts in the northern part of the Republic of Karakalpakstan, Uzbekistan. The research aims to identify changes in LULC using Sentinel-2 satellite imagery over 6 years, from 2017 to 2022. The study area was selected based on 10 years of government “Land Fund” data, indicating significant changes in bare land. The satellite images and field measurements were analyzed using ArcGIS 10.4.1. LULC was categorized into five classes: water bodies; vegetation and agricultural land (including flooded mangroves, emergent vegetation, paddy fields, irrigated agricultural lands, cereals, grasses, and non-tree crops); built-up areas; saline land; and bare land. Following classification, 100 random sample points were generated in ArcGIS and verified using Google Earth Pro to ensure classification accuracy. The results showed that the overall accuracy of the LULC classification was 81% (Kappa coefficient= 0.74) in 2017 and 71% (Kappa coefficient= 0.66) in 2022, both considered within the “substantial” agreement range. The most significant change occurred in vegetation and agricultural lands, with 13,183.38 ha (9.4% of the study area) converted into bare land. These findings provide a detailed understanding of landscape transformation in the Shimbay district and offer policymakers and planners valuable insights to enhance sustainable land management and prevent further land degradation.

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References

Acar, R.U., Zengi̇N, E. (2023). Performance assessment of Landsat 8 and Sentinel-2 satellite images for the production of time series land use/land cover (LULC) maps. J. Sci. Rep.-Rep. -A, 1–15. doi: 10.59313/jsr-a.1213548 [Google Scholar] [CrossRef]

Aslanov, I., Jumaniyazov, I., Embergenov, N., Allanazarov, K., Khodjaeva, G., Joldasov, A., Alimova, S. (2023). Re-mote Sensing for Land Use Monitoring in the Suburban Areas of Tashkent, Uzbekistan, XV International Scientific Conference “INTERAGROMASH 2022, Lecture Notes in Networks and Systems. Springer Interna-tional Publishing, Cham, 1899–1907. doi: 10.1007/978-3-031-21219-2_211 [Google Scholar] [CrossRef]

Aziz, G., Minallah, N., Saeed, A., Frnda, J., Khan, W., (2024). Remote sensing basedsensing-based forest cover classifi-cation using machine learning. Sci. Rep. 14, 69. doi: 10.1038/s41598-023-50863-1 [Google Scholar] [CrossRef]

Bekanov, K., Safarov, E., Prenov, S., Uvraimov, S., & Yusupov, B. (2020). Creating land use/land cover map using methods gis and remote sensing (on the example the chimbay district of the karakalpakstan republic). Interna-tional Journal of Pharmaceutical Research, 12(3), 1704-1708. doi: 10.31838/ijpr/2020.12.03.231 [Google Scholar] [CrossRef]

Bekanov, K., Safarov, E., Prenov, Sh., Uvraimov, S., Yusupov, B. (2020). Creating land use / land cover map using methods gis and remote sensing (on the example the chimbay district of the karakalpakstan republic). Int. J. Pharm. Res. 9(3), 12. doi: 10.31838/ijpr/2020.12.03.231 [Google Scholar] [CrossRef]

Blanka, V., Par Johan, A., (2015). New sensors benchmark report on Sentinel-2A. Commission of the European Union. Joint Research Centre. Institute for the Protection and the Security of the Citizen..Citizen. Publications Office, LU.

City Population. (2025). Uzbekistan: Administrative Division. Retrieved from https://www.citypopulation.de/en/uzbekistan/admin/

Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ, 37, 35–46. doi: 10.1016/0034-4257(91)90048-B [Google Scholar] [CrossRef]

Deb, D.S.K., Nathr, R.K. (2012). Land use/cover classification- An introduction review and comparison. Retrieved from https://globaljournals.org/GJRE_Volume12/2-Land-usecover-classification-An-introduction-review.pdf

Delwart, S. (2015). ESA Standard Document.

Dhakal, S., Kandel, S., Puri, L., Shrestha, S. (2022). Assessment on Land Use Land Cover Mapping:Mapping : Sentinel-2 Versus Landsat-9. For. J. Inst. For. Nepal, 19, 56–63. doi: 10.3126/forestry.v19i01.55704 [Google Scholar] [CrossRef]

Esri. (2025). Sentinel-2 10m Land Use/Land Cover Time Series [ArcGIS Online dataset]. Retrieved from https://www.arcgis.com/home/item.html?id=cfcb7609de5f478eb7666240902d4d3d

Fatima, N., Javed, A. (2021). Assessment of Land Use Land Cover Change Detection Using Geospatial Techniques in Southeast Rajasthan. Journal of Geoscience and Environment Protection, 09(12), 299–319. doi : 10.4236/gep.2021.912018 [Google Scholar] [CrossRef]

Foody, G.M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the ac-curacy of thematic maps obtained by image classification. Remote Sens. Environ, 239, 111630. doi: 10.1016/j.rse.2019.111630 [Google Scholar] [CrossRef]

Gebeyehu, A.E., Chunju, Z., Yihong, Z. (2019). Assessment and Mapping of Land Use Change by Remote Sensing and GIS:GIS : A Case Study of Abaya Chamo Sub-basin, Ethiopia. Nat. Environ. Pollut. Technol., 18. [Google Scholar]

He, H., Yan, J., Liang, D., Sun, Z., Li, J., Wang, L., (2024). Time-series land cover change detection using deep learning-based temporal semantic segmentation. Remote Sens. Environ, 305, 114101. doi: 10.1016/j.rse.2024.114101 [Google Scholar] [CrossRef]

Htitiou, A., Boudhar, A., Chehbouni, A., Benabdelouahab, T. (2021). National-Scale Cropland Mapping Based on Phe-nological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine. Remote Sensing, 13(21), 4378. doi: 10.3390/rs13214378 [Google Scholar] [CrossRef]

Islami, F.A., Tarigan, S.D., Wahjunie, E.D., Dasanto, B.D. (2022). Accuracy Assessment of Land Use Change Analysis Using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conf. Ser. Earth Environ. Sci., 950, 012091. doi: 10.1088/1755-1315/950/1/012091 [Google Scholar] [CrossRef]

Juliev, M., Pulatov, A., Fuchs, S., Hübl, J., (2019). Analysis of Land Use Land Cover Change Detection of Bostanlik Dis-trict, Uzbekistan. Pol. J. Environ. Stud., 28, 3235–3242. doi: 10.15244/pjoes/94216 [Google Scholar] [CrossRef]

Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use/land cover with Sentinel 2 and deep learning. In 2021 IEEE international geoscience and remote sensing symposium IGARSS. IEEE, 4704-4707.

Kumari, A., Karthikeyan, S. (2023). Sentinel-2 Data for Land Use/Land Cover Mapping:Mapping : A Meta-analysis and Review. SN Comput. Sci., 4, 815. doi: 10.1007/s42979-023-02214-0 [Google Scholar] [CrossRef]

Kwan, C., Ayhan, B., Budavari, B., Lu, Y., Perez, D., Li, J., Bernabe, S., Plaza, A. (2020). Deep Learning for Land Cover Classification Using Only a Few Bands. Remote Sensing, 12, 2000. doi: 10.3390/rs12122000 [Google Scholar] [CrossRef]

Li, K., Cao, X., Meng, D., 2024. A New Learning Paradigm for Foundation Model-Based Remote-Sensing Change De-tection. IEEE Trans. Geosci. Remote Sensing, 62, 1–12. doi: 10.1109/TGRS.2024.3365825 [Google Scholar] [CrossRef]

Liu, L., Xiao, X., Qin, Y., Wang, J., Xu, X., Hu, Y., Qiao, Z. (2020). Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine. Remote Sens. Environ., 239, 111624. doi: 10.1016/j.rse.2019.111624 [Google Scholar] [CrossRef]

Luo, C., Zhang, X., Wang, Y., Men, Z., Liu, H. (2022). Regional soil organic matter mapping models based on the opti-mal time window, feature selection algorithm and Google Earth Engine. Soil Tillage Res., 219, 105325. doi: 10.1016/j.still.2022.105325 [Google Scholar] [CrossRef]

Makar R. S., Shahin Sahar A., El-Nazer Mostafa., Wheida Ali. (2022). Development of a PCA-based land use/land co-ver classification utilizing Sentinel-2time series. Middle East Journal of Agriculture Research, 11(2), 630-637. doi: 10.36632/mejar/2022.11.2.42 [Google Scholar] [CrossRef]

Maxwell, A.E., Warner, T.A., Fang, F., (2018). Implementation of machine-learning classification in remote sensing: an applied review. Int. J. Remote Sens., 39, 2784–2817. doi: 10.1080/01431161.2018.1433343 [Google Scholar] [CrossRef]

McHugh, M.L. (2012). Interrater reliability: the kappa statistic. Biochem. Medica, 276–282. doi: 10.11613/BM.2012.031 [Google Scholar] [CrossRef]

Nasiri, V., Deljouei, A., Moradi, F., Sadeghi, S.M.M., Borz, S.A. (2022). Land Use and Land Cover Mapping Using Sen-tinel-2, Landsat-8 Satellite Images, and Google Earth Engine:Engine : A Comparison of Two Composition Methods. Remote Sens., 14, 1977. doi: 10.3390/rs14091977 [Google Scholar] [CrossRef]

Nedd, R., Light, K., Owens, M., James, N., Johnson, E., Anandhi, A., (2021). A Synthesis of Land Use/Land Cover Stu-dies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Land-scape. Land, 10, 994. doi: 10.3390/land10090994 [Google Scholar] [CrossRef]

Pande, C.B., Moharir, K.N., Khadri, S.F.R., (2021). Assessment of land-use and land-cover changes in Pangari wa-tershed area (MS), India, based on the remote sensing and GIS techniques. Appl. Water Sci., 11, 96. doi: 10.1007/s13201-021-01425-1 [Google Scholar] [CrossRef]

Pande, Dr.C. (2022). Land Use/Land Cover and Change Detection mapping in Rahuri watershed area (MS), India using the Google Earth Engine and Machine Learning Approach. Geocarto International, 37(26). doi: 10.1080/10106049.2022.2086622 [Google Scholar] [CrossRef]

Paudel, B., Zhang, Y., Li, S., Liu, L., Wu, X., Khanal, N.R. (2016). Review of studies on land use and land cover change in Nepal. J. Mt. Sci., 13, 643–660. doi: 10.1007/s11629-015-3604-9 [Google Scholar] [CrossRef]

Pelletier, C., Webb, G., Petitjean, F., (2019). Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series. Remote Sensing, 11, 523. doi: 10.3390/rs11050523 [Google Scholar] [CrossRef]

Peng, M., Liu, Y., Khan, A., Ahmed, B., Sarker, S.K., Ghadi, Y.Y., Bhatti, U.A., Al-Razgan, M., Ali, Y.A., (2024). Crop monitoring using remote sensing land use and land change data: Comparative analysis of deep learning methods using pre-trained CNN models. Big Data Res. 36, 100448. https://doi.org/10.1016/j.bdr.2024.100448[Google Scholar] [CrossRef]

Pogoda i Klimat. (2025). Weather and climate data. Retrieved from http://www.pogodaiklimat.ru/

Rakhmonov, S., Umurzakov, U., Rakhmonov, K., Bozarov, I., Karamatov, O., (2021). Land Use and Land Cover Change in Khorezm, Uzbekistan. E3S Web Conf., 227, 01002. doi: 10.1051/e3sconf/202122701002 [Google Scholar] [CrossRef]

Salman, Md.A., Nomaan, Md.S.S., Sayed, S., Saha, A., Rafiq, M.R., (2020). Land use and land cover change detection by using remote sensing and GIS technology in Barishal district, Bangladesh. Earth Sci. Malays, 5, 33–40. doi: 10.26480/esmy.01.2021.33.40 [Google Scholar] [CrossRef]

Sefrin, O., Riese, F.M., Keller, S., (2020). Deep Learning for Land Cover Change Detection. Remote Sens, 13, 78. doi: 10.3390/rs13010078 [Google Scholar] [CrossRef]

Skidmore, A.K., Stein, A., Van Der Meer, F., Gorte, B. (1999). Accuracy assessment of spatial information, in Spatial Statistics for Remote Sensing, Remote Sensing and Digital Image Processing. Springer Netherlands, Dordrecht, 197–209. doi: 10.1007/0-306-47647-9_12 [Google Scholar] [CrossRef]

Tejasree, G., Agilandeeswari, L., (2024). Land use/land cover (LULC) classification using deep-LSTM for hyperspectral images. Egypt. J. Remote Sens. Space Sci. 27, 52–68. https://doi.org/10.1016/j.ejrs.2024.01.004[Google Scholar] [CrossRef]

Teshager, Z., & Abeje, K. (2021). GIS and Remote Sensing based Land Us e/Land Cover Change Detection: The Case of Kility Watershed. J Remote Sens GIS, 10(3). doi: 10.35248/2469-4134.21.10.282 [Google Scholar] [CrossRef]

UN. (2019). Sustainable Development Goals Report. Retrieved from https://unstats.un.org/sdgs/report/2019/

Vajsova, B., & Aastrand, P. (2015). New sensors benchmark report on Sentinel-2A. Retrieved from https://ec.europa.eu/jrc

Vaughn, I. (2019). Landsat 8 (L8) Data Users. Handbook. Retrieved From https://www.usgs.gov/media/files/landsat-8-data-users-handbook

Wulansari, H. (2017). Uji Akurasi Klasifikasi Penggunaan Lahan Dengan Menggunakan Metode Defuzzifikasi Maxi-mum Likelihood Berbasis Citra Alos Avnir-2. BHUMI J. Agrar. Dan Pertanah., 3, 98. doi: 10.31292/jb.v3i1.233 [Google Scholar] [CrossRef]

Xu, P., Tsendbazar, N.-E., Herold, M., De Bruin, S., Koopmans, M., Birch, T., Carter, S., Fritz, S., Lesiv, M., Mazur, E., Pickens, A., Potapov, P., Stolle, F., Tyukavina, A., Van De Kerchove, R., Zanaga, D., 2024. Comparative vali-dation of recent 10 m-resolution global land cover maps. Remote Sens. Environ, 311, 114316. doi: 10.1016/j.rse.2024.114316 [Google Scholar] [CrossRef]

Zaabar, N., Niculescu, S., Mihoubi, M.K. (2023). Assessment of Land Cover Changes in the Allala Watershed Based on Object Based Image Analysis Using Landsat and Sentinel-2 Images, in:in : Niculescu, S. (Ed.), European Spa-tial Data for Coastal and Marine Remote Sensing. Springer International Publishing, Cham, 81–96. doi: 10.1007/978-3-031-16213-8_5 [Google Scholar] [CrossRef]

Zafar, Z., Zubair, M., Zha, Y., Fahd, S., Ahmad Nadeem, A., (2024). Performance assessment of machine learning algo-rithms for mapping of land use/land cover using remote sensing data. Egypt. J. Remote Sens. Space Sci. 27, 216–226. doi: 10.1016/j.ejrs.2024.03.003 [Google Scholar] [CrossRef]