Shamshodbek Akmalov

Understanding the motion parameters of floating ice is very important for characterizing the ice water dynamics of rivers during freezing periods. Due to the low spatiotemporal resolution of satellite images, limited observation range of unmanned aerial vehicles, and deformation of shore-based camera images, it is difficult to simultaneously quantify the translational and rotational motion characteristics of floating ice and long-distance transportation. This study used the unmanned aerial vehicle GPS joint observation method to observe and obtain various motion parameters such as local translation, rotation, and long-distance transportation in the curved section of the upper reaches of the Yellow River and the straight section of the middle reaches of the Yellow River during the winter of 2024–2025 under conditions of ice density of 50–90%. The velocity field obtained by the drone shows an average ice velocity of 1.27 m/s at the bend and 1.18 m/s in the straight section, with lateral velocity gradients of −0.245 to 0.050 s⁻¹ and −0.141 to 0.222 s⁻¹, respectively. The angular velocity of a single floating ice block is 0.008–0.016 rad/s at bends and 0.010–0.036 rad/s in straight sections. The angular velocity is positively correlated with the local shear strength, and the rotation direction is consistent with the sign of the velocity gradient. GPS tracking provides long-distance transportation trajectories, and the average difference between the speeds obtained by GPS and drones is 0.10 m/s, confirming the reliability of speed estimation based on drones. These results indicate that integrated unmanned aerial vehicle GPS observation can quantitatively characterize local floating ice movement and long-distance floating ice transport behavior, providing on-site parameters for river ice water dynamics research and hazard assessment, and has the potential to be applied to rivers in other cold regions.

Due to the complexity inherent in river ice dynamics, the utilization of remote sensing imagery represents the most crucial and effective method currently available for monitoring changes in river ice. In the Inner Mongolia segment of the Yellow River during winter, two distinct types of ice surfaces are observed: juxtaposed ice and consolidated ice. Additionally, certain areas of open water remain unfrozen. Rapid identification and classification of extensive ice formations and open water zones along this lengthy river section constitute critical information for informed decision-making in ice prevention and management strategies within the Yellow River basin. This paper takes the formation and characteristic analysis of different types of ice in the Yellow River channels in Inner Mongolia as the starting point. It employs a support vector machine (SVM) as the classifier and introduces an optimized model for classifying river ice types using high-resolution Sentinel-2 optical imagery. The model utilizes multi-band spectral features, along with multi-spectral fusion indices such as the normalized difference snow index (NDSI) and the normalized difference frozen surface index (NDFSI), as feature vectors. This approach attains an overall accuracy of 94.91% in classifying different types of ice and can significantly contribute to river ice monitoring by offering robust theoretical support. In the winter of 2023–2024, the proportion of juxtaposed ice on the Yellow River section in Inner Mongolia changed from 45% to 55%, the proportion of consolidated ice changed from 30% to 40%, and the proportion of open water changed from 9% to 19%. This research investigates the characteristics of river ice formations and develops a classification methodology for river ice patterns utilizing high-resolution Sentinel-2 imagery in conjunction with a supervised classification algorithm. The findings of this study are intended to offer technical support for the expedited interpretation of ice conditions in the Yellow River, thereby serving as a scientific basis for precise monitoring and effective disaster prevention and management related to river ice phenomena.

Mountain glaciers are considered natural indicators of warming and a device for climatic change. In addition, it is also a solid reservoir of freshwater resources. Along with climate change, clarifying the dynamic changes of glacier in the Aksu River Basin (ARB) are important for hydrological processes. The study examined the variations in glacier area, elevation, and their reaction to climate change in the ARB between 1990 and 2022. The glacier melt on the runoff is explored from 2003 to 2020. This investigation utilized Landsat and Sentinal-2 images, ICESat, CryoSat, meteorological and hydrological data. The findings suggest that: (1) The glacier area in the ARB retreated by 309.40 km² (9.37%, 0.29%·a⁻¹) from 1990 to 2022. From 2003 to 2021, the ARB glacier surface elevation retreat rate of 0.38 ± 0.12 m·a⁻¹ (0.32 ± 0.10 m w.e.a⁻¹). Comparison with 2003–2009, the retreat rate is faster from 2010 to 2021. (2) From 1990 to 2022, the Toxkan and the Kumalak River Basin’s glacier area decreases between 61.28 km² (0.28%·a⁻¹) and 248.13 km² (0.30%·a⁻¹). Additionally, the rate of glacier surface elevation declined by −0.34 ± 0.11 m·a⁻¹, −0.42 ± 0.14 m·a⁻¹ from 2003 to 2021. (3) The mass balance sensitivities to cold season precipitation and ablation-phase accumulated temperatures are +0.27 ± 0.08 m w.e.a⁻¹(10%)⁻¹ and −0.33 ± 0.10 m w.e.a⁻¹ °C⁻¹, respectively. The mass loss is (962.55 ± 0.57) × 10⁶ m³ w.e.a⁻¹, (1087.50 ± 0.68) × 10⁶ m³ w.e.a⁻¹ during 2003–2009, 2010–2021 respectively. Warmer ablation-phase accumulated temperatures dominate glacier retreat in the ARB. (4) Glacier meltwater accounted for 34.57% and 41.56% of the Aksu River’s runoff during the ablation-phase of 2003–2009 and 2010–2020, respectively. The research has important implications for maintaining the stability of water resource systems based on glacier meltwater.

More than 1,000 satellites are launched into space, and they differ in their functions, rotation orbits, resolution, and other properties. Scientists divide the satellites into low-resolution, medium-resolution, high-resolution, and very high-resolution satellites by their properties. Now, the biggest challenge facing scientists is to use some of these different resolution images in their field. To get the expected result, it is very important to analyze the image that needs an which gives more accurate results. Therefore, the main attention of this article is aimed to find the answer to these problems. In this article 3 satellite images which have different resolution are analyzed. The possibility of middle-resolution images of MODIS, high-resolution images of Landsat, and very high-resolution images of WorldView-2 (WV-2) satellites using GIS are analyzed. A research area was the Syrdarya region, and downloaded different images of satellites of this area and compared with using e Cognition. According to the results, a more accurate satellite image for irrigation sets information is WorldView-2 images. In comparison analysis, it shows more accurate properties than other satellite images. As irrigation sets are small objects for the analysis, very high spatial resolution satellite images are important. Water discharge and surface change happen very fast; thus, it requires daily monitoring of the condition. And in this case, the temporal resolution of the MODIS and Landsat is 16 day, and it is a too long period.

After the 2000s, the launch of very high-resolution satellites provided great water and irrigation network management personnel opportunities. Now, the water management staff have the opportunity to study and monitor water supply systems and exploitation conditions of irrigation systems remotely via satellite imagery. By using those satellite images, specialists can search for water bodies, detect defected place of irrigation systems, and monitor their technical condition. Another advantage of satellite imagery is that they capture large areas of the Earth, keeping water systems under control in large areas. Therefore, the use of very high-resolution images has greatly developed in the water branch since the 2000s. The creation of different water extraction methods, models, indexes, and using different layers in the analysis for different regions using different satellites with very high resolution is developed. These indexes and layers are so numerous that they are now over 100. The user has difficulty getting any of them in the analyzes. Therefore, in this article, we have studied more than 50 water extraction methods, which gave positive and accurate results in an arid region. From those 50 methods, separated 10 the most effective methods and tested with WorldView-2 image analysis in the arid region and the water-rich region of Syrdarya region. According to the results of the analysis recommend the highest accuracy method for arid areas. Results show that water extraction using NIR2 layer of the WorldView-2 satellite images is the most accurate method than other methods. The accuracy of the results was 94 %. The analysis found the irrigation systems filled with sand and vegetation.

In recent times there have been many changes on Earth, which have appeared after anthropogenic impact. Finding solu-tions to problems in the environment requires studying the problems quickly, make proper conclusions and creating safe and useful measures. Humanity has always had an effect on the environment. There can be many changes on the Earth be-cause of direct and indirect effects of humans on nature. Determining these changes at the right time and organizing meas-urements of them requires the creation of quick analysing methods. This development has improved specialists’ interest for remote sensing (RS) imagery. Moreover, in accordance with analysis of literature sources, agriculture, irrigation and ecolo-gy have the most demand for RS imagery. This article is about using geographic information system (GIS) and RS technol-ogies in cadastre and urban construction branches. This article covers a newly created automated method for the calculation of artificial surface area based on satellite images. Accuracy of the analysis is verified according to the field experiments. Accuracy of analysis is 95%. According to the analysis from 1972 to 2019 artificial area enlargement is 13.44%. This method is very simple and easy to use. Using this data, the analysis method can decrease economical costs for field measures. Using this method and these tools in branches also allows for greater efficiency in time and resources.

In the past few decades, the shrinkage of the Aral Sea is one of the biggest ecological catastrophes caused by human activity. To quantify the joint impact of both human activities and climate change on groundwater, the spatiotemporal groundwater dynamic characteristics in the Amu Darya Delta of the Aral Sea from 1999 to 2017 were analyzed, using the groundwater level, climate conditions, remote sensing data, and irrigation information. Statistics analysis was adopted to analyze the trend of groundwater variation, including intensity, periodicity, spatial structure, while the Pearson correlation analysis and principal component analysis (PCA) were used to quantify the impact of climate change and human activities on the variabilities of the groundwater level. Results reveal that the local groundwater dynamic has varied considerably. From 1999 to 2002, the groundwater level dropped from −189 cm to −350 cm. Until 2017, the groundwater level rose back to −211 cm with fluctuation. Seasonally, the fluctuation period of groundwater level and irrigation water was similar, both were about 18 months. Spatially, the groundwater level kept stable within the irrigation area and bare land but fluctuated drastically around the irrigation area. The Pearson correlation analysis reveals that the dynamic of the groundwater level is closely related to irrigation activity within the irrigation area (Nukus: −0.583), while for the place adjacent to the Aral Sea, the groundwater level is closely related to the Large Aral Sea water level (Muynak: 0.355). The results of PCA showed that the cumulative contribution rate of the first three components exceeds 85%. The study reveals that human activities have a great impact on groundwater, effective management, and the development of water resources in arid areas is an essential prerequisite for ecological protection.

The purpose of the study is the creation of a new generation of cosmogeological maps-remote fundamentals of the study area based on the integrated use of satellite imagery using GIS technologies. Earth remote sensing (ERS) materials in the form of aerial photographs have been widely used for a long time in regional geological surveys, structural and geological studies, mineral exploration, hydrogeology and engineering geology, and many other areas of fundamental and applied geology. On their basis, thematic methods of geological interpretation of remote sensing materials were developed. As a result of the scientific and technological revolution, space research already significantly affect the acceleration of the pace of development of geological science and technology and bring a great economic effect. At present, the materials of digital satellite images obtained from Landsat 7.8 TM, Aster, etc. satellites and the method of their interpretation have led to the creation of a new type of cosmogeological maps - remote bases. The purpose of the research is the creation of a new generation of cosmogeological maps-remote fundamentals of the study area based on the integrated use of satellite imagery using GIS technologies. The article provides information about the remote basics of the cosmogeological map based on GIS technologies.

Proceedings of the International conference

Human activities are mainly responsible for the Aral Sea crisis, and excessive farmland expansion and unreasonable irrigation regimes are the main manifestations. The conflicting needs of agricultural water consumption and ecological water demand of the Aral Sea are increasingly prominent. However, the quantitative relationship among the water balance elements in the oasis located in the lower reaches of the Amu Darya River Basin and their impact on the retreat of the Aral Sea remain unclear. Therefore, this study focused on the water consumption of the Nukus irrigation area in the delta of the Amu Darya River and analyzed the water balance variations and their impacts on the Aral Sea. The surface energy balance algorithm for land (SEBAL) was employed to retrieve daily and seasonal evapotranspiration (ET) levels from 1992 to 2018, and a water balance equation was established based on the results of a remote sensing evapotranspiration inversion. The results indicated that the actual evapotranspiration (ET_a) simulated by the SEBAL model matched the crop evapotranspiration (ET_c) calculated by the Penman–Monteith method well, and the correlation coefficients between the two ET_a sources were greater than 0.8. The total ET_a levels in the growing seasons decreased from 1992 to 2005 and increased from 2005 to 2015, which is consistent with the changes in the cultivated land area and inflows from the Amu Darya River. In 2000, 2005 and 2010, the groundwater recharge volumes into the Aral Sea during the growing season were 6.74×10⁹ m³, 1.56×10⁹ m³ and 8.40×10⁹ m³; respectively; in the dry year of 2012, regional ET exceeded the river inflow, and 2.36×10⁹ m³ of groundwater was extracted to supplement the shortage of irrigation water. There is a significant two-year lag correlation between the groundwater level and the area of the southern Aral Sea. This study can provide useful information for water resources management in the Aral Sea region.