Alexey Terekhov

This study analyzes air pollution by fine particulate matter (PM2.5) in Almaty, the largest megacity of Kazakhstan. The city is located in a basin formed by the spurs of the Zailiyskiy Alatau range. Due to its geographical features, it is highly exposed to pollution by anthropogenic fine particulate matter. The main sources of air pollution include transport, industrial enterprises, and heating systems. The objectives of this study are to identify the dominant source of pollution, to assess spatial heterogeneity between central and foothill districts, and to evaluate the effectiveness of household air purifiers in improving indoor air quality. Urban vehicular traffic exhibits a periodic temporal pattern, with daily minima at night and weekly minima on weekends. This dynamic behavior enables a quantitative assessment of its contribution to the overall PM2.5 concentration in the urban atmosphere. The results suggest that heat generation systems are likely to be the dominant contributor to PM2.5 pollution during the autumn–winter period. In addition, observations indicate that high pollution levels in foothill areas occur approximately 30% less frequently than in central districts. The use of household air purification systems may reduce indoor particulate matter concentrations severalfold under the observed conditions.

Satellite direct-to-cell connectivity can extend 5G Internet of Things (IoT) services into areas without terrestrial coverage by reusing terrestrial mobile-service spectrum. This study examines aggregate interference at a non-terrestrial network (NTN) satellite receiver in selected bands within 694–2690 MHz, where terrestrial duplex arrangements differ between regions. A satellite receiving an uplink may also receive terrestrial downlink emissions from another country using TDD or an overlapping FDD downlink. Six geographical case studies combine satellite motion, population-based terrestrial deployments, and aggregate-interference calculations over a 180 kHz receiver bandwidth. Results are presented as interference-to-noise ratio (I/N) complementary cumulative distribution functions and geographical maps, with nadir and random off-nadir pointing cases. In the evaluated TDD cases, BS contributions dominate the upper part of the aggregate-interference distributions, and elevated interference extends beyond the emitting countries. These findings identify cross-border interference pathways under the assumed deployments, loading, spectral coupling, and antenna patterns. They do not establish that duplex arrangement outweighs deployment density independently of these assumptions. The adopted −6 dB I/N threshold is a screening reference; service availability, link performance, and the effectiveness of mitigation techniques are not evaluated.

Freely meandering rivers flow through gently sloping plains composed of loess and fluvial sediments. Low-gradient alluvial plains are formed without the influence of landscape features such as rock outcrops or other features that distort the flow path. The channel configurations of such rivers, and in particular the size of meanders and oxbow lakes, depend on the average long-term water discharge. Large rivers form large meanders, while small rivers form correspondingly small ones. Morphological analysis of river channel configurations can offer a metric for estimating the average long-term water discharge of a river based solely on the sinuosity of its channel. The study examined six freely meandering rivers in Kazakhstan, with discharges ranging from 4.5 to 760 m³/s and channel slopes from 0.005 to 0.06%. The morphological analysis of river channels was based on the Relative Elevation Model, specifically its version based on the Copernicus Global Digital Elevation Model, with a spatial resolution of 30 m. River channel configurations were approximated using a set of inscribed circles, the diameters of which formed the basis for the river’s average long-term water discharge metric. The largest diameter circles, which could support the river channel with a sector of at least 135°, were expertly inscribed into river bends. The diameters of the inscribed circles within these sets varied from four times for small rivers to ten times for large rivers. These sets of circles, sorted by size, can characterize the average long-term water discharge of the analyzed rivers. For example, a sample of average median values of inscribed circle diameters has a high correlation with the average long-term water discharge, with a linear approximation reliability of R² = 0.997. The scope of the developed method for assessing the average long-term water discharge of freely meandering rivers includes retrospective analysis of changes in average long-term average long-term water discharge. This can provide significant historical depth of analysis, spanning centuries and millennia, since the analysis is based on describing the results of very slow processes of natural deformation of river channels. Thus, the method proposed in this study for assessing the average long-term water discharge of freely meandering rivers based on morphological analysis of their channel configurations expands the arsenal of tools for reconstructing certain paleoclimate elements related to the hydrology of territories.

Freely meandering rivers flow through gently sloping plains composed of loess and fluvial sediments. Alluvial fans are formed without the influence of landscape features such as rock outcrops or other features that distort the flow path. The channel configurations of such rivers, and in particular the size of meanders and oxbow lakes, de-pend on the average long-term water discharge. Large rivers form large meanders, while small rivers form correspondingly small ones. Morphological analysis of river channel configurations can offer a metric for estimating the average long-term water content of a river based solely on the sinuosity of its channel. The study examined six freely meandering rivers in Kazakhstan, with discharges ranging from 4.5 to 760 m3/sec and channel slopes from 0.005 to 0.06%. The morphological analysis of river channels was based on the Relative Elevation Model, specifically its version based on the Copernicus Global Digital Elevation Model, with a spatial resolution of 30 meters. River channel configurations were approximated using a set of inscribed circles, the diameters of which formed the basis for the river's water content metric. The largest diameter circles, which could support the river channel with a sector of at least 135°, were expertly inscribed into river bends. The diameters of the inscribed circles within these sets varied from four times for small rivers to ten times for large rivers. These sets of circles, sorted by size, can characterize the average long-term water content of the analyzed rivers. For example, a sample of average median values of inscribed circle diameters has a high correlation with the average long-term water discharge, with a linear approximation reliability of R2 = 0.997. The scope of the developed method for assessing the water content of freely meandering rivers includes retrospective analysis of changes in average long-term water discharge. This can provide significant historical depth of analysis, spanning centuries and millennia, since the analysis is based on describing the results of very slow processes of natural deformation of river channels. Thus, the method proposed in this study for assessing the water content of freely meandering rivers based on morphological analysis of their channel configurations expands the arsenal of tools for reconstructing certain paleoclimate elements related to the hydrology of territories.

The thematic processing of pseudocolor composite images, especially those created from remote sensing data, is of considerable interest. The set of spectral classes comprising such images is typically described by a nominal scale, meaning the absence of any predetermined relationships between the classes. However, in many cases, images of this type may contain elements of a regular spatial order, one variant of which is a gradient structure. Gradient structures are characterized by a certain regular spatial ordering of spectral classes. Recognizing gradient patterns in the structure of pseudocolor composite images opens up new possibilities for deeper thematic images processing. This article describes an algorithm for analyzing the spatial structure of a pseudocolor composite image to identify gradient patterns. In this process, the initial nominal scale of spectral classes is transformed into a rank scale of the gradient legend. The algorithm is based on the analysis of Moore neighborhoods for each image pixel. This creates an array of the prevalence of all types of local binary patterns (the pixel’s nearest neighbors). All possible variants of the spectral class rank scale composition are then considered. The rank scale variant that describes the largest proportion of image pixels within its gradient order is used as a final result. The user can independently define the criteria for the significance of the gradient order in the analyzed image, focusing either on the overall statistics of the proportion of pixels consistent with the spatial structure of the selected gradient or on the statistics of a selected key image region. The proposed algorithm is illustrated using analysis of test examples.

Precision farming methods require consideration of subtle differences in plant growth processes in different areas of cultivated arable land. Differences in the relief of the field, its water supply, the thickness of the humus layer, etc. cause the need to rank the arable land for the application of agrotechnical operations of different intensities, which ultimately leads to higher yields and lower costs of agricultural crops. The ranking of arable land within a field is usually accomplished by segmentation of remotely sensed data. The creation of a segmentation system requires periodic remote and ground monitoring of fields, collection and processing of the received information with its geographical reference. Both satellite remote sensing systems and unmanned aerial platforms (UAPs) can be used for this purpose. The volume of information received, especially when using UAVs, is very significant, and the requirements for the speed of processing are high. In this regard, efficient methods of systematization and processing of the received images, which rely on sufficiently fast segmentation algorithms, are relevant. This paper considers methods of image segmentation from various remote sensing systems, which allow to increase the economic performance of agronomic measures in the precision farming loop. An example of a threshold segmentation program is given, which can be used separately or as part of an information system to support precision farming processes. The paper presents the results of its application for segmentation of satellite images of a field by NDVI index value. The conducted analysis and recommendations on the segmentation data will contribute to the prevention of environmental violations, yield losses due to sudden changes in weather conditions and differences in the relief of cultivated arable land.

Ephemera and ephemeroid plants form a short-term but relatively developed herbaceous cover during spring in South Kazakhstan. This important component of vegetation of arid territories develops in early spring due to moisture accumulated during the cold period. Satellite monitoring of vegetation states allows assessing the reaction of ephemera and ephemeroids to long-term weather variations. In this research, the direction of monotonous NDVI (Normalized Difference Vegetation Index) trends is estimated for the territory of South Kazakhstan. The MOD13Q1A1(NDVI) 500-m resolution data for 2000–2022 and the nonparametric Mann–Kendall (M-K) test are used. The South Kazakhstan growing season from March to October is described by 15 separate 16-day time windows of the MOD13Q1 product. The M-K test is used to determine whether a time series has a monotonic upward or downward trend. The M-K test values, in the format of the difference between the number of concordant and disconcordant pairs of samples, are used. By threshold values of +3.5 and –3.5, the time series are ranked to three types of monotonous trends: ascending (>+3.5), descending (<–3.5), and uncertain. The received values are grouped into seasonal (spring, summer, autumn) sets. Each set includes five 16-day assessments: spring from March 5 to May 23, summer from May 24 to August 11, and autumn from August 12 to October 30. The end result for each 500 × 500-m elementary section is an average assessment of the trend direction of the 16-day MOD13Q1A1(NDVI) values during the 80‑day period of the analyzed time of year (spring, summer, autumn). The obtained values of the average trend direction range from –100 to +100%, which corresponds to either five downward or five upward trends. Previously, a test site has been allocated in South Kazakhstan, with an area of about 50 thousand km², which, according to NDVI (160 scenes from Sentinel‑2 of March–October 2018–2022), was dominated by vegetation of the spring period. It is found that the spring vegetation of the period from March 5 to May 23, attributed by the terms of vegetation to the development of ephemera and ephemeroid plants, has an average assessment of monotonous trends for the analyzed contour equal to –21.95%; for summer vegetation, –48.63%; and for autumn vegetation, –53.13%. Thus, in South Kazakhstan, during 2000–2022, a dominance of negative trends in the vegetation state in the zone of prevailing ephemera and ephemeroid plants is recorded. At the same time, the trends in spring vegetation appear somewhat better than the trends in summer and autumn vegetation with more pronounced downward slide.

Background. Globally, about half of the world's crops are cultivated under irrigation, which affects the climate of the fields and can also increase the comfort of the environment for humans. The development of satellite technologies has opened up opportunities for rapid and low-cost monitoring of the Irrigation Cooling Effect (ICE). This area of research is in its infancy, which predetermines the relevance of identifying the current trend of its development.Purpose. To analyze scientific literature in the field of ICE satellite index use for irrigated lands monitoring, to identify main directions of development and research centers.Materials and methods. Information from the Scopus and RSCI databases was used as the main source of information. The case study demonstrates the relationship between ICE and NDVI for the XUAR irrigated area in the PRC.Results. Analysis of primary literature sources has shown that at present ICE is used mainly to assess the cooling effect of irrigated crop and its water consumption, the cooling effect of urban vegetation, and to assess the impact of land cover change on local and regional climate. The main research centers are scientific organizations of China and the USA, which is confirmed by the number of scientific publications and amount of their citations. A promising direction is the use of ICE for operational satellite monitoring of crops (including rain-fed crops).Conclusion. Thus, ICE can be regarded as a useful complement to the NDVI index commonly used in satellite crop monitoring, which causes the increasing use of this index in the world, especially for irrigated land monitoring.

Lowland rivers flowing through the loess rocks carry large quantities of suspended materials and experience active processes of channel deformation (meandering). A large number of shoals, as a result of sediment deposition, exist in the riverbeds. Shoals often change, especially during the period of high water. Such rivers do not have stable hydrological profiles, and so satellite estimates of their water content are of great practical interest. During the period of low water, the morphology of the riverbed is relatively stable and the shoals are in a semisubmerged state. Under these conditions, the proportion of flooding of shoals is very sensitive to the river water level, which creates favorable conditions for the development of satellite methods for assessing water levels. The study examined the Ili River in Central Asia with an annual outflow of about 12 km³, which is main tributary of the large Lake Balkhash. The informative value of Sentinel-2 data (resolution 10 m) in the task of assessing water level in the river in conditions of relative low water was shown. 179 cloudless Sentinel-2 satellite images were selected for the period from April to November 2018–2022. An additional criterion for choosing the image was the water level at the Ili-Dobyn hydropost of Kazhydromet less than 280 cm. This water level was typical for about 80% of the time of the low-water period of 2018–2022. The Ili riverbed on the border of China and Kazakhstan with a length of about 40 km was used to create the shoal (sediments) mask with a total area above 4 km². The MNDWI1 (Modified Normalized Difference Water Index) water index and its threshold value of +0.25 were used to identify the “water” class on the shoal mask. The proportion of flooded shoals was compared with ground data from the Ili-Dobyn hydrographic station on the water level during the satellite’s flight. For the April–November period, linear dependences were obtained between the proportion of shoals flooding and the river water level. The reliability of the linear approximation for seasonal data was high and varied from R² = 0.83 (2022) to 0.96 (2020). The entire period (2018–2022) of the analysis was characterized by R² = 0.93. The use of the linear regression equation as a model for predicting the water level in the river based on the proportion of flooding of test shoals provided a sufficiently high value (0.74) of the Nash–Sutcliffe hydrological forecasting efficiency parameter, which indicates the practical significance of the developed methodology.

Satellite images and the Normalized Difference Vegetation Index (NDVI) are often used to monitor the state of vegetation cover and a significant amount of information has now been accumulated. When processing long-term time series of satellite data, mathematical difficulties arise, for example, in data clustering. The solution to the problem can be a parameterization of the satellite monitoring information using characteristic moments. For each pixel position, hundreds of NDVI values from satellite data can be reduced to several characteristic functional parameters, in particular to the extreme NDVI and the average multiyear NDVI value, as well as other summing characteristics. This opens the way for constructing pseudocolor composite images and their subsequent clustering using any standard algorithms. This research examined Southern Kazakhstan, with a total area of more than 700 thousand km². Using Google Earth Engine, time series of NDVI from Sentinel-2 scenes (resolution 10 m) for the period April–October 2018–2022 (about 160 covers) served as the basis for describing the state of vegetation cover. An additional parameter was the multiyear maximum of the Vegetation Soil Salinity Index (VSSI). The RGB channels of the pseudocolor image were based on Sentinel-2 monitoring data from April–October 2017–2022 and included Red, a multiyear maximum of VSSI; Green, a multiyear maximum of NDVI; and Blue, a multiyear average of NDVI. The resulting pseudocolor image displayed in detail the state of vegetation, with a clear separation of agricultural vegetation from natural, with a ranking of irrigated arable land according to the features of growth and development of agricultural crops. This information can serve as a basis for segmentation of preprocessed satellite data for analyzing the vegetation state in South Kazakhstan in various applied tasks. As an example, using unsupervised ISODATA classification, salinity of irrigated arable land of the Kyzylkum rural district of the Zhetysay district of Turkestan region was estimated. The results demonstrated the prospects of such an analysis method and clarified the known results obtained earlier using MODIS satellite data.