Application Of Remote Sensing Data Research Articles

Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India.

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms-Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( ) algorithm-to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R2 = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R2 = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.

Application of remote-sensing data in geochemical studies of soils of the Yertis River basin within East Kazakhstan

This article presents the results of field research, as well as the results of the use of remote-sensing data in the geochemical study of the soil cover of the Yertis River basin. In the work, the content of the gross form of heavy metals in the soil of the river basin was investigated. The atomic absorption method determined the content of the Poor configuration of elements of elements in soils. Statistical processing of the obtained data from N. A. Plokhinsky was carried out using the Statistica program. Also, using the performed indices in the remote sensing of clay minerals, iron oxides, and carbonates, the spatial distribution and comparison of pollutants in the soil cover of the river basin were revealed. The methods and means of image processing tested in this study can be used to create maps of the distribution of pollutants. Also, the data obtained reflect the patterns of distribution of heavy metals in the soils of the basin and can be used to optimize landscapes and improve the organization of ecological and geochemical monitoring.

Multispectral Remote Sensing Data Application in Modelling Non-Extensive Tsallis Thermodynamics for Mountain Forests in Northern Mongolia.

The imminent threat of Mongolian montane forests facing extinction due to climate change emphasizes the pressing need to study these ecosystems for sustainable development. Leveraging multispectral remote sensing data from Landsat 8 OLI TIRS (2013-2021), we apply Tsallis non-extensive thermodynamics to assess spatiotemporal fluctuations in the absorbed solar energy budget (exergy, bound energy, internal energy increment) and organizational parameters (entropy, information increment, q-index) within the mountain taiga-meadow landscape. Using the principal component method, we discern three functional subsystems: evapotranspiration, heat dissipation, and a structural-informational component linked to bioproductivity. The interplay among these subsystems delineates distinct landscape cover states. By categorizing ecosystems (pixels) based on these processes, discrete states and transitional areas (boundaries and potential disturbances) emerge. Examining the temporal dynamics of ecosystems (pixels) within this three-dimensional coordinate space facilitates predictions of future landscape states. Our findings indicate that northern Mongolian montane forests utilize a smaller proportion of received energy for productivity compared to alpine meadows, which results in their heightened vulnerability to climate change. This approach deepens our understanding of ecosystem functioning and landscape dynamics, serving as a basis for evaluating their resilience amid ongoing climate challenges.

Joint Application of Remote Sensing Data and Ground-Based Climate Monitoring System in Agriculture

Joint Application of Remote Sensing Data and Ground-Based Climate Monitoring System in Agriculture

Dust Outbreaks across East Iran: Application of Multi-Source Remote Sensing Data (AMSR-E and FengYun3-MWRI) on the Effects of Soil Moisture

One of the most significant hydro-meteorological and agricultural variables is soil moisture, yet measuring it remains a difficult task. Due to the significant spatial fluctuation of soil moisture, it is difficult to quantify it in a particular spot or field across a sizable region. Despite the thermal band's limitations in assessing soil moisture, MODIS and AVHRR, which are inappropriate were utilized in this investigation. The study examined the impact of soil moisture on dust outbreak. Soil moisture in the study domain was monitored using field techniques and the hybrid model. It combined multi-sourced remote sensing data, obtained from AMSER-E and FY-3 satellites. AMSER-E satellite measures the light temperature in five frequencies ranging from 6. 9 to 89 GHz based on data obtained from AMSER-E. Findings revealed areas with a spatial scale of 25 km2 has a 12-hour time step or variability in dust storm, thereby influencing soil moisture content within the zone of study. In addition to introducing acceptable potentials of the passive microwave band for accurate and applied monitoring of the soil moisture, the present results are viewed as a reliable source for studies on drought in time scale. The study shows that Zabol in Sistan has the highest annual average of 80.7 dust storm days. Soil moisture estimates serve a great deal for preparing soil moisture maps and the evaluation of temporal and spatial variations of soil moisture in study region to address issues related to dust storms.
 In order to identify the areas affected by dust storms and understand how dust particles are dispersed in the Sistan region, satellite image processing was employed using MODIS 1 sensor images obtained from the TERRA satellite.

Application of Remote Sensing Data for Preliminary Exploration by Determining the Spectral Reflectivity of Outcrops and Soils at the Tochka Deposit (EKR)

Using the example of remote methods of Earth exploration and the use of satellite images obtained by the Sentinel-2 satellite apparatus, the possibility of conducting primary geological exploration to identify geological anomalies (objects) and drawing up a more accurate plan for fieldwork is considered. The geological and geochemical characteristics of the studied area are presented. Interpretation and analysis of the data obtained using multispectral remote sensing of the earth has been carried out. An innovative methodology capable of detecting spectral signatures of lithium-containing pegmatites is presented, as well as well-known remote sensing algorithms for detecting halos of changes associated with these pegmatite bodies. The method of identifying the main spectral wavelengths of reflection and absorption of some minerals is described. It is shown that with the use of this technique, the time of geological exploration is significantly reduced and the financial costs of conducting geological exploration and depreciation of the technical means used are significantly reduced. And also, it is planned to improve this methodology

Application of ASTER Remote Sensing Data to Porphyry Copper Exploration in the Gondwana Region

Porphyry copper ore is a vital strategic mineral resource. It is often associated with significant hydrothermal alteration, which alters the original mineralogical properties of the rock. Extracting alteration information from remote sensing data is crucial for porphyry copper exploration. However, the current method of extracting hydrothermal alteration information from ASTER remote sensing data does not consider the influence of disturbing factors, such as topography, and ignores the weak report of surface minerals, which has significant limitations. Therefore, this paper selects the Gondwana region of the East Tethys–Himalayan tectonic domain as the study area, combines waveform calculation with principal component analysis methods, proposes a spectral feature-enhanced principal component analysis (EPCA) method, and constructs a model to complete the automatic selection of principal components for each scene image. The results show that the etching information extracted by the EPCA method is significantly better than the traditional Crosta method in terms of etching area and spatial aggregation and discovers several prospective mineralization areas that have not yet been explored and exploited, such as Sakya and Xietongmen counties in Rikaze, providing theoretical support for subsequent mineralization exploration and large-scale mineral extraction. Meanwhile, obtaining the alteration information of the whole area can help to understand the distribution of mineralizing elements from a macroscopic perspective in the future, which is of great scientific significance in order to deeply analyze the formation process of metal deposits in mineralizing areas and improve the theory of porphyry mineralization.

Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning

ABSTRACT Ongoing climate change has accelerated the outbreak and expansion of climate-sensitive infectious diseases such as dengue fever. Dengue fever will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. By predicting the spatiotemporal distribution of dengue fever outbreaks, we can effectively implement dengue fever prevention and control. Our study aims to predict the spatiotemporal distribution of dengue fever outbreaks in Chinese Taiwan using a U-Net based encoder – decoder model with daily datasets of sea-surface temperature, rainfall, and shortwave radiation from Remote Sensing (RS) instruments and dengue fever case notification data. Although the prediction accuracy of the proposed model was low and the overlapping areas between the ground truth and pixelwise prediction were few, some of the pixels were located nearby the ground truth, suggesting that the application of RS data and deep learning may help to predict the spatiotemporal distribution of dengue fever outbreaks. With further improvements, the deep learning model might effectively learn a small amount of training data for a specific task.

Remote Sensing-Based Yield Estimation of Winter Wheat Using Vegetation and Soil Indices in Jalilabad, Azerbaijan

Concerns about the expanding human population’s adequate supply of food draw attention to the field of Food Security. Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. However, given the breadth and number of these regions, field research would be an expensive and time-consuming endeavour. With the advent of remote sensing and optical sensors, it is now possible to acquire diverse data remotely, quickly, and inexpensively. This study investigated the limitations and capabilities of remote sensing data application in the field of planning Food Security. As a result, Sentinel 2 and Shuttle Radar Topography Mission (SRTM) data were used to estimate winter wheat yields with a high degree of accuracy (98.03%) using the Mamatkulov technique and the MEDALUS model, which was both free and widely available. This method can make it possible to make predictions about the productivity of newly created crop fields or for which we do not have information about the productivity of previous years, without the need to wait for building regression models or any field studies. Considering the outcome, wide-range and larger analyses on this topic can be carried through.

Dimensionality-Transformed Remote Sensing Data Application to Map Soil Salinization at Lowlands of the Syr Darya River

The problem of saving soil resources and their reclamation measures under current climate change conditions attracts the world community’s close attention. It is relevant in the Syr Darya River’s lowlands, where the secondary soil salinization processes have intensified. The demand for robust methods to assess soil salinity is high, and the primary purpose of this study was to develop a quantitative analysis method for soil salinity estimation. We found a correspondence between the sum of salts in a topsoil layer to the Landsat 8 data in the Tasseled cap transformation of the image values. After testing several methods, we built a prediction model. The K-nearest neighborhood (KNN) model with a coefficient of determination equal to 0.96 using selected predictors proved to be the most appropriate for soil salinity assessment. We also performed a quantitative assessment of soil salinity. A significant increase in a salt-affected area and the mean soil sum expressing an intensification of secondary soil salinization from 2018 to 2021 was found. The increasing temperature values, decreasing soil moisture, and agricultural use affect the extension of salt-affected ground areas in the study area. Thus, the soil moisture trend in the Qazaly irrigation zone is negative and declining, with the highest peaks in early spring. The maximum temperature has a mean value of 15.6 °C (minimum = −15.1 °C, maximum = 37.4 °C) with an increasing trend. These parameters are evidence of climate change that also affects soil salinization. PCA transformation of the Landsat-8 satellite images helped to remove redundant spectral information from multiband datasets and map soil salinity more precisely. This approach simultaneously extends mapping opportunities involving visible and invisible bands and results in a smaller dataset.

Application of Remote Sensing Data in Crop Yield and Quality: Systematic Literature Review

Purpose: Covering current state of the art in the field of application of remotely sensed data in crop quality improvement. Methodology/Approach: Systematic literature review using novel text mining techniques. Findings: Relevance of topic, measured by number of relevant studies, is rising, best performing input data types and modelling techniques are identified. Research Limitation/Implication: Review to a certain point of time in a rapidly evolving field of research. Originality/Value of paper: There was no similar review article on the topic at the time of conducting this research.

RESEARCH ON VALIDITY MEASUREMENT OF MEGACITIES RENEWAL POLICIES AND SUITABILITY SCALE FOR URBAN REMOTE SENSING DATA APPLICATIONS: TAKING BEIJING’S DUAL CONTROL POLICY AS AN EXAMPLE

Abstract. Urban renewal has become the application hotspot of urban remote sensing development in recent years. However, scientific distribution of weight relationship between indicators reflecting progress and effectiveness of different urban renewal goals and tasks, and comprehensive evaluation of effectiveness of policies in the existing indicator system require further in-depth research. In particular, there is no clear standard for application caliber of remote sensing data of different cities in spatial indicators. Taking Beijing’s dual control policy as an example, this paper measures effectiveness of mega-city renewal policies represented by Beijing based on analytic hierarchy process, and analyzes types of remote sensing data suitable for urban renewal. In addition, the suitability scale of urban remote sensing data application is given through case studies. By analyzing the actual validity of Beijing’s dual-control policy implementation, it is concluded that the application scale of urban remote sensing data for urban renewal suitability is remote sensing image big data > remote sensing data artificial intelligence algorithm > remote sensing image historical data > remote sensing big data. It is necessary to focus on automatic identification and exploration of spatial changes based on remote sensing, design and calculation of spatial indicators of urban physical examination based on remote sensing in the future. Furthermore, work needs to be carried out to further improve evaluability, accuracy and objectivity of practical application of remote sensing data in different cities for urban renewal, and explore establishment of a clear urban physical examination system and remote sensing data application system guidelines.

EVALUATION OF ZY1-02D HYPERSPECTRAL SATELLITE SURFACE REFLECTANCE PRODUCTS

Abstract. ZY-1 02D satellite is an important satellite in China space infrastructure planning. The Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources of the People’s Republic of China (LASAC) undertook the construction of the satellite hyperspectral data processing system and produced surface reflectance products. Reflectance is the basis of hyperspectral remote sensing data application, which is directly related to the efficiency and quality of hyperspectral application. At present, there is no comprehensive and in-depth study on the accuracy evaluation of the atmospheric correction reflectance of ZY1 - 02D satellite hyperspectral data. In view of this problem, Accuracy Verification of LASAC products using accuracy, precision, uncertainty, spectral curve correlation and spectral angle chord four indicators based on surface reflectance produced by AERONET ground measured data. The results show that the average values of accuracy, precision, uncertainty of LASAC products are 3.2 %, 2.02 % and 4.13 %, respectively. The spectral curve correlation and spectral chord angle are 0.998 and 0.979, respectively. The research result can provide technical reference for the processing and application of ZY1 - 02D hyperspectral image surface reflectance products.

Application of Remote Sensing Data for Assessment of Bark Beetle Attacks in Pine Plantations in Kirkovo Region, the Eastern Rhodopes

Intensive forest afforestation with native pine species was developed in the 1960s on degraded and deforested lands in the region of the Eastern Rhodopes (south-eastern Bulgaria). Severe damage by wet snow was registered in the coniferous forests of the Rhodopes in March 2015. In the following years, bark beetle attacks were registered on the broken and felled fresh wood. As a result, bark beetle infestation spots appeared in the pine plantations. In the period 2019–2021, damage caused by bark beetles was assessed in the region of State Forestry Kirkovo (the Eastern Rhodopes, south-eastern Bulgaria). An integrated approach using the data of the information system of the Executive Forest Agency (ISEFA), remote sensing data obtained by an “eBee SQ” unmanned aerial vehicle (UAV) equipped with a “Parrot Sequoia” multispectral camera, and subsequent terrestrial observations, was applied. ISEFA data showed that there was no serious damage caused by abiotic and biotic factors in the pine forests of SF Kirkovo until 2014. Snow damage in 2015 affected 513 ha of pine plantations, and bark beetle infestations reached up to 1316 ha in 2016. In 2019, a total of 226.87 ha of pine plantations were captured in three localities—Fotinovo, Kirkovo, and Kremen. The relative share of damage caused by bark beetles was greater in P. sylvestris plantations (15.3–23.0%), compared to damage in P. nigra (2.3%). Four different categories of normalised difference vegetation index (NDVI) were separated in bark beetle infestation spots—living trees, dead trees, grass and shrub vegetation, stones and rocks. The NDVI values in locations with living trees varied between 0.500 (spaces between tree crowns) and 0.700 (central part of the crown projection) (an average of 0.617). In the locations with dead trees, the average values of NDVI of lying trees was 0.273, and in standing trees, NDVI varied between 0.275 (central part of crown projections) and 0.424 (spaces between tree crowns). In the locations with grass and shrub vegetation, stones and rocks, the average NDVI was 0.436 and 0.329, respectively. In the field study, average defoliation of 31.2–32.3% was registered in P. sylvestris plantations, and 47.4% in P. nigra plantations. Defoliations mainly were caused by pine processionary moth (Thaumetopoea pityocampa) and fungal pathogens (Dothistroma septosporum and Lecanosticta acicola). The damage was caused by Ips acuminatus (in P. sylvestris only), and I. sexdentatus, Tomicus piniperda and T. minor (in P. sylvestris and P. nigra). Infestations by other xylophages, such as Phaenops cyanea, Rhagium inquisitor, and Pissodes spp., were also found on pine stems.

Study on the Aridity in the North-Western (NW) Part of Bangladesh through Combined Application of Remote Sensing, GIS and Geophysical Data

In the present paper, hydro-environmental behaviour of two adjacent but characteristically different land areas of (i) semi-arid Barind Tract and (ii) Ganges flood plain situated in the north-western part of Bangladesh have been studied and possible mechanisms of variability in the two study sites have been investigated. Time series remote sensing data supplemented with climatic, geophysical and groundwater data and other ancillary information have been analysed for the purpose. Coupling of Landsat TM derived vegetation cover information with meteorological data depicts higher sensitivity of Barind Tract than the other study site towards vegetation cover changes in response to rainfall amount. Temporal sequence of albedo value as derived from Landsat TM data manifests appropriate seasonal trend that is consistent with field observations. Analysis of rainfall data over a period of about 50 years depicts substantially higher rainfall in both the study sites as compared to a typical semiarid region. However, temporal distribution of rainfall events is mostly concentrated within very short period of time in the year. Lithological information on the subsurface configuration depicts the existence of a relatively impermeable clayey layer near the top surface in these areas. Relatively higher thickness of this impermeable clayey layer over the semiarid Barind areas has been noticed that slows down the infiltration process significantly as compared to that in adjacent floodplain areas. Analysis of drainage network exhibits relatively higher drainage density in the Barind Tract area as compared to that in floodplain area. The drainage pattern shows predominantly unidirectional and distributary nature of drainage in the Barind Tract area and in general, distribution is oriented towards basin direction and most of the branches of the drainage system. Ultimately, relatively higher elevation, steeper slope result in relatively quicker surface runoff to carry out excess rain water to the nearest basins, results in decreased infiltration of rainwater. Eventually, the conversion efficiency of Barind Tract area to transform intercepted rainwater into underground reservoir water through infiltration seems to be lower as compared to that of floodplain areas.

Satellite Remote Sensing of Water Quality Variation in a Semi-Enclosed Bay (Yueqing Bay) under Strong Anthropogenic Impact

The semi-enclosed bays impacted by heavy anthropogenic activities have weak water exchange and purification capacities. Most of the sea bays have suffered severe eutrophication, water quality deterioration, ecosystem degradation and other problems. Although many countries and local governments have carried out corresponding environmental protection actions, the evaluation of their effectiveness still requires monitoring technology and data support for long-term water environment change. In this study, we take Yueqing Bay, the fourth largest bay in China, as a case to study the satellite-based water quality monitoring and variation analysis. We established a nutrient retrieval model for Yueqing Bay to produce a long-term series of nutrient concentration products in Yueqing Bay from 2013 to 2020, based on Landsat remote sensing images and long-term observation data, combined with support vector machine learning and water temperature and satellite spectra as input parameters, and then we analyzed its spatiotemporal variations and driving factors. In general, nutrient concentrations in the western part of the bay were higher than those in the eastern part. Levels of dissolved inorganic nitrogen (DIN) were lower in summer than in spring and winter, and reactive phosphate (PO4-P) levels were lower in summer and higher in autumn. In terms of natural factors, physical effects (e.g., seasonal variations in flow field) and biological effects (e.g., seasonal differences in the intensity of plankton photosynthesis) were the main causes of seasonal differences in nutrient concentration in Yueqing Bay. Nutrient concentration generally increased from 2013 to 2015 but decreased slightly after 2015. Over the past decade, the economy and industry of Yueqing Bay basin have developed rapidly. Wastewater resulting from anthropogenic production and consumption was transported via streams into Yueqing Bay, leading to the continuous increase in nutrient concentrations (the variation rates: aDIN>0, aPO4−P>0), which directly or indirectly caused high nutrient concentrations in some areas of the bay (e.g., Southwest Shoal at the mouth of Yueqing Bay). After 2015, the various ecological remediation policies adopted by cities around Yueqing Bay have mitigated, to some extent, the increasing nutrient concentration trends (the variation rates: aDIN<0, aPO4−P<0), but not significantly (P > 0.1). The environmental restoration of Yueqing Bay also requires continuous and long-term ecological protection and restoration work to be effective. This research can provide a reference for ecological environment monitoring and remote sensing data application for similar semi-enclosed bays, and support the sustainable development of the bay.

An Overview of Remote Sensing Data Applications in Peatland Research Based on Works from the Period 2010–2021

In the 21st century, remote sensing (RS) has become increasingly employed in many environmental studies. This paper constitutes an overview of works utilising RS methods in studies on peatlands and investigates publications from the period 2010–2021. Based on fifty-nine case studies from different climatic zones (from subarctic to subtropical), we can indicate an increase in the use of RS methods in peatland research during the last decade, which is likely a result of the greater availability of new remote sensing data sets (Sentinel 1 and 2; Landsat 8; SPOT 6 and 7) paired with the rapid development of open-source software (ESA SNAP; QGIS and SAGA GIS). In the studied works, satellite data analyses typically encompassed the following elements: land classification/identification of peatlands, changes in water conditions in peatlands, monitoring of peatland state, peatland vegetation mapping, Gross Primary Productivity (GPP), and the estimation of carbon resources in peatlands. The most frequently employed research methods, on the other hand, included: vegetation indices, soil moisture indices, water indices, supervised classification and machine learning. Remote sensing data combined with field research is deemed helpful for peatland monitoring and multi-proxy studies, and they may offer new perspectives on research at a regional level.

Locally adaptive change-point detection (LACPD) with applications to environmental changes

We propose an adaptive-sliding-window approach (LACPD) for the problem of change-point detection in a set of time-ordered observations. The proposed method is combined with sub-sampling techniques to compensate for the lack of enough data near the time series’ tails. Through a simulation study, we analyse its behaviour in the presence of an early/middle/late change-point in the mean, and compare its performance with some of the frequently used and recently developed change-point detection methods in terms of power, type I error probability, area under the ROC curves (AUC), absolute bias, variance, and root-mean-square error (RMSE). We conclude that LACPD outperforms other methods by maintaining a low type I error probability. Unlike some other methods, the performance of LACPD does not depend on the time index of change-points, and it generally has lower bias than other alternative methods. Moreover, in terms of variance and RMSE, it outperforms other methods when change-points are close to the time series’ tails, whereas it shows a similar (sometimes slightly poorer) performance as other methods when change-points are close to the middle of time series. Finally, we apply our proposal to two sets of real data: the well-known example of annual flow of the Nile river in Awsan, Egypt, from 1871 to 1970, and a novel remote sensing data application consisting of a 34-year time-series of satellite images of the Normalised Difference Vegetation Index in Wadi As-Sirham valley, Saudi Arabia, from 1986 to 2019. We conclude that LACPD shows a good performance in detecting the presence of a change as well as the time and magnitude of change in real conditions.

Application of GIS and Earth Remote Sensing Data for the Evaluation of the Spatiotemporal Spread of Rabies in Eastern Trans-Baikal Region

The aim of the study was to improve monitoring of natural foci of rabies based on the use of modern technologies of cartographic analysis.Materials and methods. A retrospective study was carried out on the data of official registration of rabies in the Trans-Baikal Territory (Eastern Trans-Baikal Region) for 1950–2020. The spatial spread of rabies among animals was compared for two time periods: 1972–1978 (n=157 cases) and 2014–2020 (n=61 cases). Software packages QGIS 3.16.0, ArcMap 10.8.1, ArcScene 10.8.1 and electronic landscape-geographical maps of the world “Natural Earth”, “OpenStreetMap” were used for the mapping. Analyzed territorial units (ATUs) tied to a degree grid (0,5º × 0,5º) were marked on the cartograms. The interdependence between the localization of rabies cases and landscape elements was assessed statistically, comparing between the rabies free and the rabies affected ATUs.Results and discussion. Differences in the landscape confinedness of rabies epizootics in Eastern Trans-Baikal Region at different periods of time have been established. Until 1983, dog rabies prevailed, and epizootics spread beyond the forest-steppe zone. In 1984–2013 rabies among animals was not recorded. In 2014–2020 red foxes (Vulpes vulpes) were the main source of the rabies virus. The area of the epizootic has more than halved in comparison with 1972–1978. The main manifestations of rabies were observed at the junction of lowland and mountainous, steppe and forest areas. The rabies affected ATUs were characterized by more pronounced landscape variables. Possible routes of cross-border spread of the rabies virus have been identified. The connection between the features of the spatiotemporal spread of rabies and genetic variants of the virus, climate change, animal migrations and anthropogenic factors in Eastern Trans-Baikal Region in 1950–2020 is discussed. The results of the study are proposed to be used to adjust the programs for barrier oral vaccination of the foxes.

Application of TLS Remote Sensing Data in the Analysis of the Load-Carrying Capacity of Structural Steel Elements

This paper proposes the use of terrestrial laser scanning (TLS) measurements together with finite element method (FEM) numerical modeling to assess the current technical condition. The main aim of the paper was to evaluate the effect of point cloud size reduction on the quality of the geometric model and the ability to represent the corrosion level in assessing its load-carrying capacity. In this study, a standard scanning was performed on a historical object and a point cloud of a selected corroded element was generated. In order to further process the data, gradual reductions were made in the number of points from which meshes representing the geometry of the selected beam were created. Inaccuracy analyses of the meshes generated on the reduced point clouds were performed. Numerical analysis was then conducted for the selected mesh generated from the reduced point cloud. The results identified the locations of maximum stresses. The presented analysis showed that by developing the presented measurement and computational technique, laser scanning can be used to determine the degree of corrosion of hard-to-reach steel elements.