This article presents an assessment of atmospheric pollutant concentrations based on state-of-the-art geoinformation research methods that utilize Sentinel-5 satellite imagery, the cloud computing platform Google Earth Engine (GEE), and ArcGIS 10.8 software. The spatial distributions of some pollutants (nitrogen dioxide, sulfur dioxide, formaldehyde, carbon monoxide, methane) in the atmosphere are analyzed on the example of the basins of the Zapadnyy Bulganak, Alma, Kacha, Belbek, and Chernaya rivers on the north-western slope of the Crimean Mountains. The concentrations of the average annual and average monthly values of pollutants for each catchment area are compared. The GEE (Google Earth Engine) platform is used for extracting annual and monthly average rasters of pollutant substances, while ArcGIS is utilized for enhanced data visualization and in-depth analytical processing. Background concentrations of pollutants within protected natural areas are calculated. By comparing the spatial and temporal distribution of pollutant values with the background concentrations within these protected areas, a complex index of atmospheric pollution is constructed. The spatial and temporal variability of nitrogen dioxide (NO₂) concentrations has been thoroughly examined. Based on the regression analysis (R > 0.85), the field of values of the total amount of emissions (which are analyzed for only six points in the study area and in the surrounding areas) was restored on the basis of the spatial and temporal heterogeneity of the field of distribution of nitrogen dioxide values (NO₂). Since air pollution can have negative consequences, both for human health and for the ecosystem as a whole, this study is of great importance for assessing the ecological situation within the river basins of the north-western slope of the Crimean Mountains. This work also contributes to a general understanding of the problem of gas emissions, whose study is becoming increasingly relevant. The aim of this research is to assess the potential application of Sentinel-5 satellite imagery for air quality assessment and pollution analysis within the river basins of the north-western slopes of the Crimean Mountains. The significance of this study lies in the innovative use of Sentinel-5 satellite imagery to investigate air pollution in extensive regions where a regular network of observation points is lacking.

This study conducts an in-depth analysis of anthropogenic transformation and air pollution within the confines of the Fatala River Basin situated in the Republic of Guinea, Africa. The foundation of this investigation relies upon interdisciplinary geoinformatics methodologies and data acquired through remote sensing, specifically drawing from Sentinel-5P and Sentinel-2 satellite datasets. The primary objectives encompass scrutinizing the extant ecological conditions characterizing the Fatala River Basin and assessing the anthropogenic influences within its geographic expanse. The utilization of remote sensing data, as facilitated by the Sentinel-5P satellite, emerges as a potent instrument for meticulously monitoring environmental transformations. A comprehensive analysis of the designated area, conducted through remote sensing methodologies, is employed to ascertain the concentrations of various atmospheric constituents, including nitrogen dioxide, sulfur dioxide, formaldehyde, methane, ozone, and carbon monoxide. The findings gleaned from this inquiry reveal that, notwithstanding the dynamic growth of the extractive industry centered around the world’s preeminent bauxite province, the Fouta Djallon–Mandingo, the anthropogenic transformation of the Fatala River Basin exerts a comparatively minor influence on air quality. Statistical assessments, including correlation analysis, conducted between computed parameters delineating anthropogenic alterations within the Fatala River Basin, and a comprehensive atmospheric pollution index elucidate a lack of a significant nexus. It has been determined that air pollution within the Fatala River Basin is notably influenced by topographical features and the transport of contaminants from adjacent river basins. Consequently, this article makes a substantial contribution to our comprehension of the contemporary ecological state of the Fatala River Basin in the Republic of Guinea. It also holds significant importance in elucidating the ecological challenges specific to the researched region.

The territory of the Crimean Peninsula is extensively subject to economic activities. In this connection, there is an ever-increasing impact on the environment. The present paper presents an analysis of the transformation of the area of the largest river basins of the northwestern slope of the Crimean Mountains (basins of the Zapadnyy Bulganak, Alma, Kacha, Belbek, and Chernaya rivers). For this purpose, various indicators characterizing the transformation of the area were calculated: coefficient of anthropogenic transformation; land degradation index; urbanity index; level of anthropogenic transformation; coefficient of absolute and relative intensities of ecological and economic land use distribution. The results show that the anthropogenic transformation of the area defined by the basins of the Zapadnyy Bulganak, Alma, Kacha, Belbek, and Chernaya rivers increases from the southeast to the northwest as the absolute height decreases, and when moving from river source to mouth. Among the basins under consideration, anthropogenic transformation is greatest In the Zapadnyy Bulganak River basin and lowest in the Chernaya River basin. Among the basins under consideration, anthropogenic transformation decreases from north to south in the following order: Zapadnyy Bulganak River basin, Alma River basin, Kacha River basin, Belbek River basin, Chernaya River basin. This is due to reduced impacts of economic activity within each respective basin.

This investigation endeavors to demarcate the boundaries of small- and medium-sized river catchments within the Caspian Sea drainage basin, with a specific focus on the Northeastern Caucasus, Azerbaijan, and Iran regions. A multi-faceted approach was employed, incorporating various remote sensing methods to select key areas, including the catchments of the Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan rivers. Subsequently, geoinformation systems (GISs) and topographic maps were utilized to determine the morphometric characteristics of these catchments, accompanied by an assessment of the accuracy of remote sensing data. The aim of this study is to evaluate the accuracy and suitability of digital elevation models (DEMs) with a spatial resolution of 30 m per pixel (including ASTER DEM, ALOS DEM, NASADEM, Copernicus 30 m DEM, and SRTM 30 m DEM) and 90 m per pixel (Copernicus 90 m DEM and SRTM 90 m DEM) for delineating small- and medium-sized Caspian Sea basin river catchments. For the DEMs that successfully and accurately delineated watershed boundaries, the morphometric characteristics of the river basins were calculated. This research has yielded novel findings regarding the morphometric characteristics (area, perimeter, ruggedness of the catchment line (roundness coefficient), maximum height, minimum height, average height of the river basin, maximum slope of the surface, average slope of the surface, length of the main watercourse, basin shape parameter (catchment elongation coefficient), shape coefficient, length of the river basin, average river basin slope, and average width of the basin) of individual mountainous small- and medium-sized rivers in the Northeastern Caucasus, Azerbaijan, and Iran, with the catchments of the aforementioned rivers serving as exemplars. The practical significance of these results lies in the fact that such detailed morphometric characteristics of catchments have been obtained for the first time, and their boundaries have been clarified (burned out according to various DEMs), which can serve as a basis for decision-making processes and contribute to the development of operational environmental monitoring of the state of rivers and their catchments.

In the context of a changing environment, understanding the interaction between vegetation and climate is crucial for assessing, predicting, and adapting to future changes in different vegetation types. Vegetation exhibits high sensitivity to external environmental factors, making this understanding particularly significant. This study utilizes geospatial analysis techniques, such as geographic information systems, to investigate vegetation dynamics based on remote sensing data and climatic variables, including annual air temperature, annual precipitation, and annual solar radiation. The research methodology encompasses data collection, processing, and analysis, incorporating multispectral imagery and multilayered maps of various parameters. The calculation of the normalized difference vegetation index serves to evaluate changes in vegetation cover, identify areas experiencing variations in green biomass, and establish strategies for the future development of different vegetation types. During the period from 2001 to 2022, the average normalized difference vegetation index value in the Southeastern Crimea region amounted to 0.443. The highest average values were recorded in the year 2006, reaching a magnitude of 0.469. Conversely, the lowest values were observed in the years 2001–2002, constituting 0.397. It has been ascertained that an overarching positive trend in the evolution of NDVI values from 2001 to 2022 is apparent, thus implying a notable augmentation in vegetative biomass. However, adversarial trends manifest in discrete locales adjacent to the cities of Sudak and Feodosia, along with the coastal stretches of the Black Sea. Correlation analysis is employed to establish relationships between vegetation changes and climatic indicators. The findings contribute to our understanding of the vulnerability of various vegetation types and ecosystems in the Southeastern Crimea region. The obtained data provide valuable insights for the development of sustainable vegetation resource management strategies and climate change adaptation in the region.