Geoinformation Data Research Articles

GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system

Abstract. A geospatial data processing tool, GEO4PALM, has been developed to generate geospatial static input for the Parallelized Large-Eddy Simulation (PALM) model system. PALM is a community-driven large-eddy simulation model for atmospheric and environmental research. Throughout PALM's 20-year development, research interests have been increasing in its application to realistic conditions, especially for urban areas. For such applications, geospatial static input is essential. Although abundant geospatial data are accessible worldwide, geospatial data availability and quality are highly variable and inconsistent. Currently, the geospatial static input generation tools in the PALM community heavily rely on users for data acquisition and pre-processing. New PALM users face large obstacles, including significant time commitments, to gain the knowledge needed to be able to pre-process geospatial data for PALM. Expertise beyond atmospheric and environmental research is frequently needed to understand the data sets required by PALM. Here, we present GEO4PALM, which is a free and open-source tool. GEO4PALM helps users generate PALM static input files with a simple, homogenised, and standardised process. GEO4PALM is compatible with geospatial data obtained from any source, provided that the data sets comply with standard geo-information formats. Users can either provide existing geospatial data sets or use the embedded data interfaces to download geo-information data from free online sources for any global geographic area of interest. All online data sets incorporated in GEO4PALM are globally available, with several data sets having the finest resolution of 1 m. In addition, GEO4PALM provides a graphical user interface (GUI) for PALM domain configuration and visualisation. Two application examples demonstrate successful PALM simulations driven by geospatial input generated by GEO4PALM using different geospatial data sources for Berlin, Germany, and Ōtautahi / Christchurch, New Zealand. GEO4PALM provides an easy and efficient way for PALM users to configure and conduct PALM simulations for applications and investigations such as urban heat island effects, air pollution dispersion, renewable energy resourcing, and weather-related hazard forecasting. The wide applicability of GEO4PALM makes PALM more accessible to a wider user base in the scientific community.

Improving the methodology for monitoring vegetation cover based on type segmentation

Monitoring is an important element for defining sustainable land cover management strategies. The monitoring technique proposed and developed in this work with integrated processing of remote sensing data has the potential to scale and optimize the interpretation of vegetation classes, making the analysis of the territory more detailed. This approach based on multiclass classification of composite images with data preprocessing based on Google Earth Engine (GEE) cloud platform. The article describes the characteristics and benefits of the uses cloud platform. Three regions, different in their characteristics and climatic features, were chosen as the study area: Voronezh Oblast, Republic of Tatarstan and Perm Krai. The objects of the study were the classes of forest vegetation, arable land and meadow vegetation. The article presents a step-by-step methodology for obtaining and processing Sentinel-2 satellite imagery data. The main steps of the methodology include obtaining a time series of satellite data and processing them, applying a vegetation index to satellite images, selecting reference data for validation, classifying objects using the random forests algorithm. An overview of WorldCover cartographic product from European Space Agency (ESA) and its advantages for use in working with geoinformation data is presented. The classification of target classes of vegetation in GEE cloud platform was carried out. The results of the study contain an analysis of a combination of normalized difference vegetation index (NDVI) for each studied class of vegetation. Implementation of the methodology is important for retrospective analysis and operational monitoring of vegetation cover classes. The method of multi-class segmentation of objects based on time series analysis will significantly improve the accuracy and speed of providing analytics to update information on land use.

МОНІТОРИНГ РУХУ ОБ’ЄКТІВ ЗАСОБАМИ ГЕОІНФОРМАЦІЙНИХ СИСТЕМ

One of the parameters that allow you to identify the object of observation are its coordinates. In turn, the quality of identification - the probability of correctly recognizing the objects of observation - directly depends on the accuracy of determining its coordinates. In view of the growth in the number of observation objects and, accordingly, the sources of radio radiation, the density of their location in space increases, which leads to a constant increase in the requirements for the accuracy of the location of determination. This makes the task of developing and improving methods for determining the coordinates of observation objects urgent when creating geo-information systems. The purpose of the research is to create a system for monitoring moving objects based on geoinformation data. During the research and processing of their results, methods of digital and simulation modeling and software emulation of the work of algorithms were used. During the research, the principles of building geoinformation systems were analyzed and areas of their application were identified. The paper analyzes the technologies for determining the coordinates of moving objects for the construction of geoinformation systems. In the work, the methods of determining the coordinates of moving objects were investigated and a comparative analysis of the methods of determining the coordinates of moving objects based on GPS, Wi-Fi and ZigBee technology was carried out. The paper presents the advantages and disadvantages of the specified technologies for determining the coordinates of moving objects in geoinformation systems. As a result, the choice of software for developing a system for monitoring moving objects was justified. In the work, the method of determining the coordinates of moving objects was improved due to the combination of data from various coordinate determination technologies, which made it possible to increase accuracy. The developed software can be used to determine the coordinates of moving objects for the construction of geographic information systems.

AGRO-ECOLOGICAL CLASSIFICATION OF AGRICULTURAL LAND USING MACHINE LEARNING, GIS AND REMOTE SENSING DATA

The paper is devoted to agro-ecological classification of agricultural land using modern methods of geo-information data analysis and machine learning.
 Background. There are few works in the literature, which cover the issues of accuracy of machine learning (MLL) models for agro-ecological grouping of agricultural land. A large number of raster information layers are used to improve the accuracy of land classification from satellite images. This considerably increases the time of training and testing MMOs, producing thematic maps of agricultural land classification. This approach requires considerably high computing resources and a considerable amount of computer RAM. Raster GIS data models occupy a much larger volume than vector models. In this regard, research on automated agro-ecological (grouping, classification) of agricultural land using vector GIS models is of practical importance.
 Purpose. The aim of the study is to apply GIS methods, remote sensing (ERS) data and machine learning methods for agricultural land grouping.
 Materials and methods. The materials used were synthesized multispectral high spatial resolution Sentinel-2A images, maps of vegetation indices NDVI (Normalized Difference Vegetation Index), OSAVI (Optimized Soil Adjusted Vegetation Index), EVI2 (Enhanced Vegetation Index2), NDWI (Normalized Difference Water Index), SAVI, PVI, GDVI, MCARI, NDRE, TSAVI; topographic map, ALOS DSM (30 m/pixel) and ALOS PALSAR (12.5 m/pixel) satellite images, soil map and field survey results. Field measurements were carried out using the Triump-2 satellite geodetic receiver and included determination of coordinates of characteristic points of land plot boundaries, relief elements, and soil survey.
 Digital spatial model of land use was created using GIS ArcGIS and QGIS, Python engineering libraries were used in the machine learning process.
 Results. Agro-ecological grouping of lands was realized by the example of the farm "Zerno Sibiri" of Novosibirsk region using the following machine learning methods: Random Forest (RF) method, Decision Tree (DT), k-nearest neighbours method (KNN). The best accuracy is the RF machine learning model. The accuracy of the model averaged 97.9% (with training 99.9%, testing 98.8%, and cross validation 95.0%). The Root Mean Square Error (RMSE) is 0.006: for training sample 0.001; test sample 0.076; validation sample 0.123 respectively). The mean kappa coefficient was 0.97 (1.00 for the training sample; 0.982 for the test sample, and 0.927 for the validation sample). 
 Conclusion. The offered method of agro-ecological grouping of agricultural lands by means of GIS, RS data and machine learning methods enabled to distinguish informative quantitative indicators of the relief. The main essence of the proposed method is to create a machine learning model (MLM) based on a spatial dataset. The spatial dataset is formed using geoinformation analysis methods and includes: geomorphometric maps, maps of agrometeorological parameters, soil map, on-farm land management map and operational-territorial units of land classification. The application of vector data model allowed for agro-ecological grouping of agricultural lands in automated mode, to accelerate labor-intensive process of raster data recognition, to increase objectivity of the work. The suggested method of agro-ecological agro-ecological grouping of lands allows taking into account the totality of relief and soil-ecological conditions indicators with the help of geoinformation analysis methods, remote sensing and machine learning.

Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City

As one of the most developed coastal cities, Shanghai experiences long-term ground surface settlement disasters during urban expansion periods, which has adverse effects on economic development. To date, many studies regarding Shanghai’s ground surface sedimentation have been conducted with microwave remote sensing technology. However, the systematic and timely analysis of the time series deformation results and risk evaluation is still absent. Therefore, we focused on the following aspects in this study: Firstly, revealing in detail the time series deformation characteristics during 2016–2022 with Sentinel-1A images and verifying the deformation results with different InSAR technologies and SAR data. Secondly, fully discussing the reasons for ground sedimentation from the aspects of subway construction, land use type, monthly rainfall, and human activities, and studying the correlation between surface deformation and rainfall with the singular spectrum analysis (SSA) method. Finally, conducting a risk evaluation and risk level division using the entropy method, combining the long time series deformation results and geoinformation data. Meanwhile, the following conclusions were reached: 1. There are six typical deformation areas, distributed in the Baoshan District, Minhang District, and Jinshan District of Pudong New District from 2016 to 2022. The maximum annual rate is −32.3 mm/a, and the maximum cumulative sedimentation reaches −188.6 mm. 2. Ground sedimentation is mainly due to engineering construction during city development and verifies the weak correlation between surface deformation and rainfall. 3. We obtained different levels of geological hazard risk areas, and Huangpu, Yangpu, Hongkou District, the northwest area of Pudong New Area, and the vicinity of Dishui Lake belong to higher-risk areas. The above time series deformation research results and systematic analysis of induced factors, and the higher-risk-area division, will provide valuable insights for urban risk management.

Effects of built environment on metro ridership at a microscopic scale: a case study of Xi’an, China

ABSTRACT Few studies have examined the relationship at the microscopic spatial scale. In this study, multiple sources of data including mobile phone signal data, automatic fare collection system data, geo-information data, and street-view image data are combined to measure metro ridership and built environment at the plot or block scale. The Random Gradient Boosting Decision Tree was used to explore relationship between the built environment and ridership. The results show the following: (1) the relationship between built environment and ridership shows different types of curves. (2) The path distance to the metro station and the visual perception of road space have more significant impacts on ridership than road network density. (3) The location of the grid also affects grid-level metro ridership. The results suggest that planners should consider the locational factors, pay attention to the different effective thresholds of different variables on ridership and the longitudinal landscaping of non-motorized urban roads.

Difference between spatial perception and built environments in leisure behavior approach to the life-course

The influence of leisure behavior on the built environment can be blurred by an individual"s or a household’s lifestyle and can vary depending on the life course. This study aimed to understand the mechanism of difference between spatial perception and built environments in leisure behavior approach to the life course. Data collected from 854 subjects, including leisure behavior information for a month and geo-information data, were used to identify the characteristics of differences between spatial perception and built environments and to understand leisure behavior relating to this using multivariate analysis of variance. First, the parenting groups showed a relatively higher spatial perception than non-parenting groups. Income-related characteristics were observed as important variables that influenced spatial perception without regard to parenting or non-parenting. Secondly, walking for leisure did not show much difference in behavior from the perspective of the life-course. In addition, in low quality built environments, even though the spatial perception was high, walking for leisure showed that there was no behavioral difference. It was also found that the parenting and the non-parenting groups with the low spatial perception and high built environments showed differences in leisure behavior generation in all leisure behavior modes.

Geophysical research in the pre-Carpathian hydrosphere situation for the environmental civil protection purposes

Object of research is to develop and carry out a methodology investigation of the regional ecological changes in the Pre-Carpathian hydro-lithosphere in a state of emergency caused by flooding. Method involves obtaining the results by integrating experimental field-path studies and theoretical model research, laboratory scientific comparison, the analysis and synthesis of final results. The studied model is the flooding in the Carpathian region east of the Carpathian arc in Ukraine, flooding parts of Pre-Carpathians Lviv region — industrially and recreationally important regions: Stebnyk, Borislav and Drohobych cities. Scientific novelty: the article aims to develop a method for an integrated approach to study of the territory in the question of flooding through a combined interpretation of geophysical, geological, and chemical data when considering a strategically important Pre-Carpathian region. The theoretical and practical significance of the results obtained is substantiating the feasibility and possibility of the complex application of geophysical-geological and chemical data for the practical interpretation of geoinformation data. Results will be of use to applied research in geophysics and ecology and contribute to improving of the ecological situation in selected regions. Obtained results contribute to the development of geoinformation technologies for complex geophysical-geological and chemical research in the environmental safety of territories in the event of emergencies.

The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km2) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m2. This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers).

Geoinformation assessment of digitalization in the urban space of Kaliningrad

ABSTRACT Cities around the globe that have residential areas spanning several eras of architecture face the challenge of developing a comfortable urban environment. New urban spaces must accommodate the needs of a modern urban dweller constantly engaged with information and communication technologies and the Internet of Things. A viable urban development strategy is the smart-city model based on a digital city ecosystem using urban informatics technologies. The study case is Kaliningrad, a city with an over 760-year history of development. We present a comparative analysis of its housing development dynamics, tourist zones and mobile internet coverage in the residential areas. Our findings identify digitally advanced and peripheral inner-city areas and, using geoinformation data, showcase the development of digital urban space.

Methodology for Assessing the Transport Accessibility of Capital Objects in a Megacity Based on Geoinformation Data

Purpose: to present the author's methodology and the test results for calculating integral indicators of transport accessibility on the basis of weighted normalized private indicators for three housing estates in Moscow.Methods: the study is based on the application of methods for collecting factual material, its processing, systematic, comparative historical and structural-functional analysis, which were supplemented by multivariate analysis of secondary information using content analysis of existing methods for calculating indicators of transport accessibility of capital objects. The results and conclusions of the research are based on the use of the author's methodology for calculating integral indicators of transport accessibility based on weighted normalized private indicators for three housing estates in Moscow. The analysis of a possible set of criteria for assessing transport accessibility of housing estates in Moscow metropolis was carried out on the basis of the use of a geographic information system database GIS NextGIS QGIS.Results: a review of methodological approaches to the calculation of objective quantitative indicators characterizing the transport accessibility of capital objects is carried out; the author's methodology for calculating the integral indicators of the transport accessibility of residential complexes in Moscow is presented and tested on the basis of weighted normalized private criteria / indicators. The use of the authors’ methodology for calculating integral indicators of transport accessibility based on weighted normalized private criteria / indicators made it possible to calculate the values of indicators of transport accessibility for three housing estates in Moscow, calculate an integrated score for a set of transport accessibility criteria for each housing estate, to give a comparative quantitative assessment of their transport accessibility, to conduct a rating of housing estates in terms of their transport accessibility.Conclusions and Relevance: the presented results of approbation of the author's methodology for calculating the integral indicators of transport accessibility for housing estates in Moscow allow to conduct a comparative and dynamic analysis of housing estates (or larger units) transport accessibility. The results of such an analysis can be applied in order to develop programs for transport infrastructure development of the megacity as a whole, its certain districts and city parts, as well as to assess such programs efciency. The authors see the directions for future research in the defnition and calculation of indicators based on the city dwellers perception of the transport accessibility

Development potentials of Krasnoyarsk open public spaces

The open public spaces (OPS) of Krasnoyarsk are analyzed from the perspective of the global trend in the comfortable urban environment formation. The given classification reflects the unique natural landscapes and objects of the existing landscape-planning structure of the city. The methodological approach to the complex analysis of their current state is based on the assessment of the territory development potentials and is carried out, inter alia, with the use of geoinformation data analysis. The unique and unified factors influencing the development of the OPS are identified and recommendations are given for their transformation into indicators and parameters of the environment comfort. The principles are formulated and the prospects for the further development of OPS in Krasnoyarsk are outlined.

Morphological periodization of large-scale residential development in Krasnoyarsk

This article is dedicated to examination and testing of the modern methods of morphological analysis of the territories of large-scale residential development. The urban planning theory marks the emergence of works on the study of morphotypification of residential construction of different periods. A number of recent publications provide the analysis of the morphotypes of urban environment using GIS technologies to determine such parameters as the density of urban fabric, porosity and ratio of open and closed spaces, number of storeys in a building; nature of the relative positions of buildings in a residential group, neighborhood unit, etc. The author outlines the new vectors in studying construction patterns and evolution of topography of large-scale residential development plan. The goal of this research lies in determination of the morphological periods of large-scale residential development in Krasnoyarsk based on the analysis of topology, study of the structure of the plan based on geoinformation data analysis. The article aims to determine the construction periods of large-scale residential development through the use of mathematical analysis; analyze the topology of the plan of living quarters of various construction periods; establish the morphotypes of residential planning units that are characteristic to the periods of large-scale residential construction; substantiate the morphological periods of neighborhood units of large-scale residential development. The author describes the typical morphotopes of residential development of the city based on determination of the preferred geometry and number of storeys in a building: “ribbon”, “dot”, “spline”, “L-shaped”, “U-shaped”, “circuit”. The article conducts a comprehensive calculation of morphotopes based on the key indicators, and reveals the morphotypes characteristic to different periods of large-scale residential construction: 1st period (1960–1975) – “ribbon development”; 2nd period (1975–1990) – “sporadic building” due to varying geometry of the building plan; 3rd  period (1990–2010) – “mixed housing” due to coexistence of different types of buildings; 4th period (2010–2020) – “homogeneous development” due to the presence of typical, recurring morphotope. Substantiation is given to morphological periods of residential construction.

Spatial Planning for Urban Resilience in the Face of the Flood Risk: Institutional Actions, Opportunities and Challenges

The research was inspired by the increasing impact of extreme weather events and changing climate patterns on flood-prone regions and cities, and the consequent human and economic costs. Despite global efforts for flood resilience and climate adaptation involving climate analysts, economists, social scientists, politicians, hydrological engineers, spatial planners, and policymakers, it is only partially clear how best to construct resilience measures and implement concrete initiatives. The complexity of institutions is a key factor that is often neglected, and which needs further investigation. The thesis examines the institutional arrangements that determine the role of spatial planning in managing flood risk, through an in-depth case study of Guangzhou, one of the most vulnerable cities in China and globally. The thesis employs theories of historical institutionalism, planning procedure and planning tools, policy framing and collaborative governance, to explore the mechanisms and factors that influence the creative planning and design process. Content analysis, GIS-based mapping, stakeholder analysis and TOWS analysis are used to investigate data from official policy documents, grey literature, geo-information data and interview scripts. The findings indicate that institutional arrangements, such as long-established planning traditions, formal planning procedures and tools, policy framing patterns and contextual organisational factors, determine spatial planning’s role in managing flood risk. They do this through (1) the extent of the changeability of an established planning system towards expanded flood resilience measures; (2) the performance of cross-level communication and boundary-spanning work between planning and water management; (3) the legal framework that planners and hydrological engineers follow; and (4) the capacities of planning and water management institutions to work on flood issues. This research shows how to apply knowledge from policy science, political science, institutional science and administration, to analyse the nature of the planning process in tackling the urgent challenge of flood risk and climate change.

Spatial Planning for Urban Resilience in the Face of the Flood Risk

The research was inspired by the increasing impact of extreme weather events and changing climate patterns on flood-prone regions and cities, and the consequent human and economic costs. Despite global efforts for flood resilience and climate adaptation involving climate analysts, economists, social scientists, politicians, hydrological engineers, spatial planners, and policymakers, it is only partially clear how best to construct resilience measures and implement concrete initiatives. The complexity of institutions is a key factor that is often neglected, and which needs further investigation. The thesis examines the institutional arrangements that determine the role of spatial planning in managing flood risk, through an in-depth case study of Guangzhou, one of the most vulnerable cities in China and globally. The thesis employs theories of historical institutionalism, planning procedure and planning tools, policy framing and collaborative governance, to explore the mechanisms and factors that influence the creative planning and design process. Content analysis, GIS-based mapping, stakeholder analysis and TOWS analysis are used to investigate data from official policy documents, grey literature, geo-information data and interview scripts. The findings indicate that institutional arrangements, such as long-established planning traditions, formal planning procedures and tools, policy framing patterns and contextual organisational factors, determine spatial planning’s role in managing flood risk. They do this through (1) the extent of the changeability of an established planning system towards expanded flood resilience measures; (2) the performance of cross-level communication and boundary-spanning work between planning and water management; (3) the legal framework that planners and hydrological engineers follow; and (4) the capacities of planning and water management institutions to work on flood issues. This research shows how to apply knowledge from policy science, political science, institutional science and administration, to analyse the nature of the planning process in tackling the urgent challenge of flood risk and climate change.

Spatial Planning for Urban Resilience in the Face of the Flood Risk

The research was inspired by the increasing impact of extreme weather events and changing climate patterns on flood-prone regions and cities, and the consequent human and economic costs. Despite global efforts for flood resilience and climate adaptation involving climate analysts, economists, social scientists, politicians, hydrological engineers, spatial planners, and policymakers, it is only partially clear how best to construct resilience measures and implement concrete initiatives. The complexity of institutions is a key factor that is often neglected, and which needs further investigation. The thesis examines the institutional arrangements that determine the role of spatial planning in managing flood risk, through an in-depth case study of Guangzhou, one of the most vulnerable cities in China and globally. The thesis employs theories of historical institutionalism, planning procedure and planning tools, policy framing and collaborative governance, to explore the mechanisms and factors that influence the creative planning and design process. Content analysis, GIS-based mapping, stakeholder analysis and TOWS analysis are used to investigate data from official policy documents, grey literature, geo-information data and interview scripts. The findings indicate that institutional arrangements, such as long-established planning traditions, formal planning procedures and tools, policy framing patterns and contextual organisational factors, determine spatial planning’s role in managing flood risk. They do this through (1) the extent of the changeability of an established planning system towards expanded flood resilience measures; (2) the performance of cross-level communication and boundary-spanning work between planning and water management; (3) the legal framework that planners and hydrological engineers follow; and (4) the capacities of planning and water management institutions to work on flood issues. This research shows how to apply knowledge from policy science, political science, institutional science and administration, to analyse the nature of the planning process in tackling the urgent challenge of flood risk and climate change.

Determination of the level of regional morbidity of the population based on data on anthropogenic load

The aim of the work is to determine the external, anthropogenic environmental factors that negatively affect the morbidity of the population and associated nosologies. The knowledge gained in this area will allow us to identify the adverse impacts of man-made nature and identify tools for environmental quality management to promote public health. Geoinformation data will help to identify the areas with the most intense environmental situation in the Murmansk region. It will also identify the dominant diseases in these areas. Mining and metallurgical enterprises mainly contribute to the pollution of the territory of the region: Kola MMC, Severonikel, Phosagro, Kovdor mining and processing plant, North-Western Phosphorus Company, Olkon and Rusal, as well as the nuclear fleet and fish processing enterprises, power supply facilities. The air basin is dominated by fluoride compounds, which are the emissions of ethylene into the atmosphere. Data on the demographic situation and medical statistics, including data on general morbidity and mortality, were obtained from the Territorial Body of the Federal State Statistics for Murmansk Region, Ministry of Health of the Russian Federation, Department of Monitoring, Analysis and Strategic Development of Health Care, Federal State Budgetary Institution “Central Research Institute for the Organization and Informatization of Health Care” of Ministry of Health of the Russian Federation and Ministry of Natural Resources and Ecology of Murmansk Region. The medical and environmental analysis of adverse factors in the regions of Murmansk region was carried out taking into account the environmentally dependent diseases of the adult population. The assessment of the morbidity of the population in the Murmansk region showed that several areas with a tense environmental situation are identified in the region. Diseases of the respiratory system, digestive system, genitourinary system, and musculoskeletal system are leading in the structure of overall morbidity.

Оценка интенсивности транспортных потоков с использованием геоинформационных систем

The main discussion is about methods for assessing the intensity of traffic flows using geoinformation technologies. The intensity of traffic flows is one of the key indicators that determine the emission from transport in urban areas. In Russia, the growth in the volume and share of motor transport in pollution is increasing under the influence of an increase in the number of cars. This is most obvious examples of it are regions of the Central Federal District, but in the regional centers, under the influence of the improvement in the structure of the vehicle park, the growth of pollution is much slower, and in Moscow it has practically stabilized. At the local level, the determining factor of road traffic pollution is the change in the building density and the transport-planning structure. The collection and calculation of indicators that give an idea of the spatial differentiation of emissions from road transport is a very time-consuming stage of the study. The most common method of obtaining information on the transport and environmental situation in the city is directly field data collection. However, this method is quite time consuming for research. In conditions when the transport infrastructure is developing rapidly, the speed of field observations does not allow promptly updating information on changes in the traffic load of the road network and, as a result, assessing the current ecological situation in the territory. As an alternative to the traditional collection of information, modern sources of geoinformation data can be used. The services, originally developed to provide operational monitoring of the traffic situation and the construction of optimal routes, can also serve as a source of data for models for assessing the intensity of traffic load in environmental studies. The proposed technique has been tested at the level of districts and administrative districts of Moscow. The results obtained are compared with control field observations. The relatively low measurement error when using data from information systems is compensated by the possibility of more efficiently obtaining information about the traffic load on the sections of the road network.

Территориальные аспекты заболеваемости детского населения в Мурманской области

The aim of the work is to study the territorial features of the childhood morbidity and identify unfavorable factors of anthropogenic load that have a negative impact on the health of the child population of the Murmansk region. The obtained geoinformation data will provide an opportunity to determine the areas of the region with the most stressful ecological situation together with the identification of leading diseases in these territories. Along with the unfavorable climatic conditions of the Kola North, the child’s body is also influenced by the high anthropogenic load due to the high urbanization of the region. Large mining, processing and metallurgical centers contribute to the negative environmental impacts. The main sources of stationary pollution include enterprises involved in metallurgical production, mining activities, and the provision of facilities with electricity, gas and steam. The search for the external negative stressors was based on content analysis of the official information open sources, followed by systematization, structural and comparative assessment of the data obtained. The ranking of the results was made according to the morbidity indicators and territorial zoning of unfavorable anthropogenic environmental factors. The data have been presented by the territorial body of medical statistics for the Murmansk region, Rospotrebnadzor, the Ministry of Health of the Russian Federation, the Government of the Murmansk region and the Ministry of Natural Resources, Ecology and Fisheries of the Murmansk Region. An assessment of the morbidity among the child population in the Murmansk region has shown that several areas with a tense ecological situation are identified in the region. Diseases of the respiratory and digestive systems, skin and subcutaneous tissue diseases are in the lead in the structure of the overall morbidity. At the same time, the indicators of the overall morbidity among the child population significantly exceed the national ones.

Satellite-based system of area estimation for main agricultural crops of Ukraine

The article studies one of the most important issues of agricultural production maintenance – development of a system of crops area estimation in Ukraine. The objective of this paper is to describe the similar system that uses high resolution satellite data and operational agrometeorological data from the network of the Hydrometeorological Centre of Ukraine as input information. The system is based on step-by-step solving of the following tasks: obtaining geoinformation data for individual agricultural crops; development of methods for multispectral satellite images classification; development of software applications to automate the process of these images classification with subsequent classification of crop areas. The research uses the following algorithms (or classifiers) to classify the agricultural land: SVM (support vector machine), RF ("random forest") and NN (neural networks). The choice of the most accurate of them formed the basis of the general method of classification. The values of spectral characteristics of red and infrared channels of a complete set of cloudless satellite images during the growing period were used as input data (features). As a result, in 2018 some test calculations were conducted to estimate the area of agricultural crops in Kyiv Region. The results of evaluation of accuracy of the satellite-based agricultural crops area estimation using the statistical data showed that the lowest accuracy is typical for winter wheat and corn. The accuracy of soybeans and spring barley classification is quite low for most of the tested fields. Sunflower and rapeseed crops showed the highest accuracy. In order to improve the accuracy of classification, it is necessary to introduce more classification features (in a temporary aspect) by processing more satellite images during the growing period, and to increase the number of test samples through systematic sampling of ground data across the regions in Ukraine. We suggest using the scheme of main agricultural crops area estimation satellite-based system by the Hydrometeorological Centre of Ukraine.