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.

The shrubland ecosystems in arid areas are highly sensitive to global climate change and human activities. Accurate extraction of shrubs using computer vision techniques plays an essential role in monitoring ecological balance and desertification. However, shrub extraction from high-resolution GF-2 satellite images remains challenging due to their dense distribution and small size, along with complex background. Therefore, this study introduces a Feature Enhancement and Transformer Network (FETNet) by integrating the Feature Enhancement Module (FEM) and Transformer module (EdgeViT). Correspondently, they can strengthen both global and local features and enable accurate segmentation of small shrubs in complex backgrounds. The ablation experiments demonstrated that incorporation of FEM and EdgeViT can improve the overall segmentation accuracy, with 1.19% improvement of the Mean Intersection Over Union (MIOU). Comparison experiments show that FETNet outperforms the two leading models of FCN8s and SegNet, with the MIOU improvements of 7.2% and 0.96%, respectively. The spatial details of the extracted results indicated that FETNet is able to accurately extract dense, small shrubs while effectively suppressing interference from roads and building shadows in spatial details. The proposed FETNet enables precise shrub extraction in arid areas and can support ecological assessment and land management.

В данной статье изучена динамика распределения территорий со сложным рельефом в Ташкентской области и представлена карта территорий со сложным рельефом.

This study examines the optimisation of Continuously Operating Reference Station (CORS) networks through a combined bibliometric and spatial analysis approach, with particular reference to Uzbekistan as an illustrative case. A total of 522 publications were reviewed, of which 53 were identified as most relevant for detailed thematic analysis, as they provided insights into global research trends, methodological approaches, and applications in geodetic network design. To demonstrate how these approaches can be applied in practice, a simplified GIS-based spatial analysis was conducted in ArcGIS Pro to evaluate the distribution of existing CORS stations in Uzbekistan. A 40 km geodetic buffer was used as a representative distance derived from commonly reported baseline ranges in the literature (30-70 km). It should be emphasised that such a buffer-based approach only reflects spatial coverage and does not account for positioning accuracy, network geometry, or GNSS error sources. The results indicate uneven spatial coverage, with relatively dense station distribution in central and eastern regions, while western areas remain less represented. This finding is presented as a preliminary spatial observation rather than a validated assessment of network performance. Overall, the study does not aim to define optimal station spacing, or to provide a full technical evaluation, but rather to synthesise existing knowledge and demonstrate how simplified spatial methods can support initial planning. Future work should focus on integrating these insights with empirical GNSS data and advanced optimisation techniques for more robust network design.

В данной статье представлено значение земельно-подготовительных работ и пути эффективного использования территорий со сложным рельефом в сельском хозяйстве и приведены примеры земельно-подготовительных работ, проводимых в настоящее время.