The south of the Russian Far East is distinguished by diversity of natural conditions for the presence of vectors and circulation of pathogens, primarily tick-borne infections. Despite the relatively low proportion of tick-borne encephalitis in the structure of tick-borne infections and the rather low incidence rate compared to other Russian regions, the disease here has epidemiological significance, which is associated with its severe course. Therefore, it is important to identify local areas of greatest epidemic manifestation of the disease and potential drivers influencing the spread of tick-borne encephalitis.  This study uses data on population incidence in the municipal districts of Khabarovsk Krai, Amur Oblast, Jewish Autonomous Oblast and Zabaikalsky Krai between 2000 and 2020. Based on Kulldorf spatial scanning statistics, a temporally stable cluster of virus circulation in the population in the southwest of Zabaikalsky Krai was identified, which existed during 2009-2018. Regression modeling using zero-inflated negative binomial regression based on a set of environmental and socio-economic predictors allowed to identify variables determining the probability of infection: the share of forest, the amount of precipitation in the warm period, population density, as well as variables reflecting population employment and socio-economic well-being.

 Despite the fact that tick-borne encephalitis is a natural focal disease and may be characterized by natural periods of increased incidence, the influence of the social component can have a strong impact on the epidemiological manifestation. The identified spatio-temporal differences within the study region and potential drivers must be taken into account when developing a set of preventive measures.

Seagrass meadow is one of the blue-carbon ecosystems capable of absorbing and storing carbon more effectively in the bodies and sediments than terrestrial ecosystems. However, nationwide data on its carbon stock remains elusive due to limitations and challenges in data collection and mapping. Seagrass percent cover and biomass, which were closely related with above-ground carbon stock, can be effectively mapped and monitored using remote sensing techniques. Therefore, this study aimed to compare the accuracy of 4 scenarios as well as assess the performance of random forest and stepwise regression methods, for mapping seagrass percent cover and biomass in Nusa Lembongan, Bali, Indonesia. The scenarios were experimented using only atmospherically corrected images, sunglint, water, as well as sunglint and water column corrected images. Furthermore, WorldView-3 images and in-situ seagrass data were used, with the image corrected by applying the scenarios. Random forest and stepwise regression methods were adopted for mapping and modelling. The optimum mapping scenario and method were chosen based on R², RMSE, and seagrass spatial distribution. The results show that the atmospherically corrected image produced the best seagrass percent cover and biomass map. Range of R² using random forest and stepwise regression model was 0.49–0.64 and 0.50–0.58, with RMSE ranging from 18.50% to 21.41% and 19.36% to 20.72%, respectively. Based on R², RMSE, and seagrass spatial distribution, it was concluded that the random forest model produced better mapping results, specifically for areas with high seagrass percent cover.

There are obstacles in estimating environmental dynamics behind its convenience, beginning with the development of effective policies for sustainable urban development. The objectives of this research were to comprehend the ability and performance of ecological indices integration and to identify the spatial distribution of changes from 2018 to 2021 in Pekanbaru City, Riau province, Indonesia. This study employed remote sensing data to create ecological parameters including the build-up index, vegetation index, soil index, and moisture index, as well as principal component analysis to generate ecological index integration. The findings indicate a correlation of over 90% among these parameters from 2018 to 2021. Overall, there has been a significant decrease in the ecological quality index’s high-quality categories, such as good and excellent, covering a total of 19.6% over 127 km². Conversely, the poor ecological quality category increased to 2.2%, encompassing an area of 15 km², up from the initial 21.2% covering 122 km². Additionally, the fair and moderate categories also experienced increases of 4% and 13.4%, respectively, reaching 28 km² and 84 km². The study area’s ecological quality is largely affected by increased anthropogenic activities, leading to a drastic decrease in the presence of ecological quality in the good and excellent categories. The importance of spatial planning is emphasized to incorporate aspects of ecological assessment rather than solely focusing on increasing economic activity. This outcome can be used to respond to the concept of sustainable development by caring for the ecological environment, particularly in urban areas, and mitigating ecological damage.

Tidal estuaries play a crucial role, serving as major hubs for economic activities while also contributing to the preservation of natural diversity and bioproductivity. In Russia, these estuaries are primarily located in remote regions of the European North and the Far East, making them vital for energy and transportation usage as they essentially form the ‘cores’ of territorial development along the Northern Sea Route.

 To facilitate the development of energy and navigation infrastructure in tidal estuaries, as well as to plan and implement environmental protection measures, it is essential to have a comprehensive understanding of their hydrological regime. Unlike regular river flow, tidal estuaries exhibit more complex hydrodynamics, influenced by both river and marine factors. Due to the considerable challenges of conducting field hydrological studies in remote areas, numerical hydrodynamic modelling has emerged as a valuable method for obtaining information on the flow and water level regime in tidal estuaries. This paper presents an application of one-dimensional HEC-RAS and two-dimensional STREAM_2D CUDA numerical models to investigate the parameters of reverse currents in the hypertidal Syomzha estuary flowing into the Mezen Bay of the White Sea. The limitations and accuracy of the models are discussed, along with the potential for their improvement considering recent advancements in understanding the hydraulics of reverse currents.

Recent research suggests that climate change is contributing to rising solute concentrations in streams. This study focuses on assessing the concentrations of major elements, nutrients, and dissolved organic carbon (DOC), and their release through the bog-river system in the taiga zone of Western Siberia. The research was carried out in the northeastern part of the Great Vasyugan Mire (GVM), the largest mire system that impacts the quality of river water in the Ob River basin. By using PCA and cluster analysis, we examined the long-term dynamics of the chemical composition of headwater streams of the GVM affected by drainage and wildfires. Our data from 2015-2022 revealed that the concentrations of Са²⁺, Mg²⁺, K⁺, Na⁺, and HCO_3- in stream water from the drained area of the GVM were, on average, 1.3 times lower than those at the pristine site. Conversely, the  concentrations of NH⁺₄, Fe_total, Cl^-, SO₄^2-, NO^-₃, DOC, and COD were higher, indicating the influence of forestry drainage and the pyrogenic factor. Our findings also demonstrated that the GVM significantly impacts the water chemical composition of small rivers. We observed a close correlation in the concentrations of К⁺, Na⁺, Cl^-, Fe_total, NH⁺₄, HCO_3-, and COD between the GVM and the Gavrilovka River waters. PCA analysis revealed that air temperature influences the concentrations of Са²⁺, Mg²⁺, NH₄⁺, NO_3-, HCO_3-, Fe_total, and DOC in the studied streams, with an inverse correlation with river discharge. The removal of major elements, nutrients, and DOC from the drained area of the GVM was most pronounced in April, being twice as high as in the pristine area. However, the total export from the drainage area of the Gavrilovka in April-September 2022 was 1.3 times lower than in the pristine area, amounting to 8487 kg/km², with DOC removal at 42%. 

ERA5 reanalysis is one of the most trusted climate data sources for wind energy modeling. However, any reanalysis should be verified through comparison with observational data to detect biases before further use. For wind verification at heights close to typical wind turbine hub heights (i.e. about 100 m), it is preferable to use either in-situ measurements from meteorological towers or remote sensing data like acoustic and laser vertical profilers, which remain independent of reanalysis. In this study, we validated the wind speed data from ERA5 at a height of 100 m using data from four sodars (acoustic profilers) located in different climatic and natural vegetation zones across European Russia. The assessments revealed a systematic error at most stations; in general, ERA5 tends to overestimate wind speed over forests and underestimate it over grasslands and deserts. As anticipated, the largest errors were observed at a station on the mountain coast, where the relative wind speed error reached 45%. We performed the bias correction which reduced absolute errors and eliminated the error dependence on the  daily course, which was crucial for wind energy modeling. Without bias correction, the error in the wind power capacity factor ranged from 30 to 50%. Hence, it is strongly recommended to apply correction of ERA5 for energy calculations, at least in the areas under consideration.. 

The escalating trend of urbanization in Indonesia, accompanied by the conversion of agricultural land into urbanized areas, necessitates the implementation of zoning regulations. These regulations are crucial to protect agricultural land and safeguard the finite land assets of the country. To ensure the preservation of scarce land resources and guarantee food security, it is paramount for the Indonesian government to establish agricultural land protection areas. This paper presents an innovative approach and integrated methods to define agricultural land protection zones in spatial form. Results of studies landscape structure classification; core farmland accounts for 33.59% of the study region, whereas edge farmland accounts for 36.43%. Furthermore, the corridor farmland area is 0.30%, the discrete farming area is 12.26%, the Edge-Patch area is 3.54%, and the Perforated area is 13.89%. Geographically, the primary agricultural land is stretched out as a continuous area located on the outskirts of Majalengka city. By integrating Geographic Information Systems (GIS), remote sensing, landscape structure, prime farmland identification, and agricultural «land interest» could have a conservationist bent. It can mean protecting specific areas for environmental reasons (reach calculated), the study aims to create optimal farmland protection areas. The techniques outlined here can aid in determining PFPA from a geographical science standpoint, and the research’s findings will be helpful for PFPA planning. 

Forest fires are global phenomena that pose an accelerating threat to ecosystems, affect the population life quality and contribute to climate change. The mapping of fire susceptibility provides proper direction for mitigating measures for these events. However, predicting their occurrence and scope is complicated since many of their causes are related to human practices and climatological variations.  To predict fire occurrences, this study applies a fuzzy inference system methodology implemented in R software and using triangular and trapezoidal functions that comprise four input parameters (temperature, rainfall, distance from highways, and land use and occupation) obtained from remote sensing data and processed through GIS environment. The fuzzy system classified 63.27% of the study area as having high and very high fire susceptibility. The high density of fire occurrences in these classes shows the high precision of the proposed model, which was confirmed by the area under the curve (AUC) value of 0.879. The application of the fuzzy system using two extreme climate events (rainy summer and dry summer) showed that the model is highly responsive to temperature and rainfall variations, which was verified by the sensitivity analysis. The results obtained with the system can assist in decision-making for appropriate firefighting actions in the region.

Land use changes significantly threaten urban areas, especially in developing countries such as Pakistan, impacting the thermal environment and comfort of human life. The ongoing transformations in cities such as Lahore, the second largest and rapidly expanding urban center in Pakistan, are alarming due to the removal of green cover and the disruption of ecological structures. In response to these concerns, this study was conducted to assess and predict the implications of observed land use changes in Lahore. The analysis employed three Landsat images from 1990, 2005, and 2020, using ArcGIS and Idrisi Selva software. The results show that the built-up area increased almost 100% (16.44% to 32.48%) during the last three decades. Consequently, a substantial shift from low to medium and medium to high degrees of LST was observed. The projections indicate a further 50% expansion of the built-up area, encroaching upon green cover until 2050, shifting more areas under a higher LST spectrum. So, the study concludes that Lahore is facing imminent threats from rapid land use changes caused by higher land surface temperature in the study area, necessitating prompt attention and decisive action. The study area is at risk of losing its conducive environment and the desirable uniformity of the thermal environment. Therefore, it is recommended that green cover be strategically enhanced to offset the rise in built-up areas and ensure a sustainable thermal environment.