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
DOI:
https://doi.org/10.23917/forgeo.8724Keywords:
Land Cover, Land Use, GIS, Remote sensing, Sentinel-2, Accuracy assessment, ShimbayAbstract
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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Copyright (c) 2026 Ilyaskhoja Jumaniyazov, Mukhiddin Juliev, Mamanbek Reimov, Rustam Oymatov, Aziz Inamov, Begench Yunusov, Dilbar Abdramanova
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