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Topographic maps used as the cartographic basis of thematic maps are created on the basis of data from the certain time period and the existing topographic base for the study area was published in 1975-1990, which does not allow visualizing recent changes of the area condition. One way to solve this problem is to use up-to-date data sources. The problem is successfully solved on the basis of visual and automated use of satellites remote sensing data of medium, high and ultra-high spatial resolution. The purpose of this article is to conduct comparative analysis of the Earth remote sensing data using the example of Sentinel 2, Landsat-8 satellite images, to consider methods for processing satellite images and use of remote sensing data to update vector layers of the cartographic basis of thematic maps. As part of the tasks of actualization of the vector layers for updating the cartographic basis of the study area, in this work was used archival and operational medium-resolution data obtained from Landsat and Sentinel-2 series satellites and Sentinel-2 image services, as well as Airbus WorldDEM4Ortho DEM. The work used the methods of GIS technologies and remote sensing, classification of remote sensing data and comparative analysis, visual and automated interpretation. A set of Python scripts has been generated to filter the collection of Sentinel-2 images and to determine cloudiness and cloud shadow. The use of remote sensing data made it possible to cover spacious and inaccessible areas of mountainous regions, significantly reducing the costs of data and processing. As a result, the updated cartographic basis of the studied area was created. The cartographic basis can be used for subsequent analysis of the dynamics and trends in changes of natural and social-economic objects. [ABSTRACT FROM AUTHOR]

Приведены результаты использования методов геоинформационного и статистического анализа для изучения спектрально-отражательных характеристик девяти наиболее типичных сельскохозяйственных культур Беларуси. Коэффициенты спектральной яркости и значения нормализованного относительного вегетационного индекса (NDVI) извлечены из мультиспектральных спутниковых снимков Landsat-8 в программном пакете ENVI (версия 5.2) и проанализированы на основе методов зональной статистики в программном комплексе ArcGIS (версия 10.2) и математико-статистического анализа в программе Statistica (версия 10). С применением корреляционного анализа осуществлена верификация спутниковых данных с соответствующими полевыми измерениями, а именно установлена достоверная сильная положительная линейная связь между значениями индекса NDVI, измеренными в полевых условиях специализированным прибором GreenSeeker, и значениями индекса NDVI, рассчитанными по спутниковым данным Landsat-8. Оценка характера распределения коэффициентов спектральной яркости и средних значений индекса NDVI в зависимости от вида сельскохозяйственной культуры проведена на основе дисперсионного анализа, позволившего выявить скрытые в спектральных данных закономерности. В частности, в результате применения процедуры множественных сравнений с помощью тестов post hoc установлено, какие именно виды культур достоверно отличались друг от друга и в какие даты наблюдались эти различия. Полученные научные результаты были систематизированы и представлены в виде таблиц. Содержащиеся в них данные позволили усовершенствовать алгоритм автоматизированного распознавания рассматриваемых в исследовании сельскохозяйственных культур.

The paper describes assessment of spatial biomass of top wood layer based on combination of high-resolution Landsat-8 satellite images and selected ground forest inventory data measurements. Test area is one of forestry of Khanty-Mansiysk region. Segmentation of satellite images for spectral homogeneous land sites (segments) mapping is applied. Land category, dominated specie, age and wood stock volume for these sites are defined. Ground forest inventory data and segments used for selection of segments for dominated specie classification and validation of obtained map. The first, nine types of land cover are classified, four of them belong to forest cover with dominating of pine, spruce, cider and birch. The reference sample is updated by segments of such non-forest classes as fires, cuts and other non-forested lands, swamps, water internal bodies. Twelve spectral metrics are used for classification: reflectance in blue, green, red and near-infrared bands of Landsat-8. There are following vegetation seasons: and of winter, beginning of spring and middle of summer. The most significant informative metrics are the reflectance in the NIR band of the spring image, also green and red bands of the summer image. Random Forest algorithm is applied for training classification. The total accuracy of land categories and dominated species classification is 86,3%. Cross-validation of the classification based on the control sample was 0.712. In the second stage, we used regression models to relate the reflectance in the red band of the winter image with the taxation characteristics of the wood stock and age of the forest species in the selected reference segments. The level of relationship between the reflectance and wood stock values were equal to 0.80 for pine, 0.56 for dark coniferous species and 0.73 for birch. Between the reflectance and the specie height is following 0.75 for pine, 0.61 for birch and 0.64 for dark coniferous species. A check with control data showed that the error in estimating the wood stock above 250 m3 / ha for birch is 15.4%, for pine – 19.0% and for dark coniferous species – 5.5%. We used regional growth tables and the mean heights reconstructed from the regression equations for calculation mean specie ages. Then the age groups (according regional felling age) for each species are determined and the wood stocks are converted into wood biomass by conversion coefficients. As a result, maps of mean ages, heights, wood stock in m3/ha and biomass in t/ha were created. Based on these maps quarter assessments of the areas and stocks of the main dominated forest species of our test area, including felling age forest stands, were carried out.

The paper describes assessment of spatial biomass of top wood layer based on combination of high-resolution Landsat-8 satellite images and selected ground forest inventory data measurements. Test area is one of forestry of Khanty-Mansiysk region. Segmentation of satellite images for spectral homogeneous land sites (segments) mapping is applied. Land category, dominated specie, age and wood stock volume for these sites are defined. Ground forest inventory data and segments used for selection of segments for dominated specie classification and validation of obtained map. The first, nine types of land cover are classified, four of them belong to forest cover with dominating of pine, spruce, cider and birch. The reference sample is updated by segments of such non-forest classes as fires, cuts and other non-forested lands, swamps, water internal bodies. Twelve spectral metrics are used for classification: reflectance in blue, green, red and near-infrared bands of Landsat-8. There are following vegetation seasons: and of winter, beginning of spring and middle of summer. The most significant informative metrics are the reflectance in the NIR band of the spring image, also green and red bands of the summer image. Random Forest algorithm is applied for training classification. The total accuracy of land categories and dominated species classification is 86,3%. Cross-validation of the classification based on the control sample was 0.712. In the second stage, we used regression models to relate the reflectance in the red band of the winter image with the taxation characteristics of the wood stock and age of the forest species in the selected reference segments. The level of relationship between the reflectance and wood stock values were equal to 0.80 for pine, 0.56 for dark coniferous species and 0.73 for birch. Between the reflectance and the specie height is following 0.75 for pine, 0.61 for birch and 0.64 for dark coniferous species. A check with control data showed that the error in estimating the wood stock above 250 m3 / ha for birch is 15.4%, for pine – 19.0% and for dark coniferous species – 5.5%. We used regional growth tables and the mean heights reconstructed from the regression equations for calculation mean specie ages. Then the age groups (according regional felling age) for each species are determined and the wood stocks are converted into wood biomass by conversion coefficients. As a result, maps of mean ages, heights, wood stock in m3/ha and biomass in t/ha were created. Based on these maps quarter assessments of the areas and stocks of the main dominated forest species of our test area, including felling age forest stands, were carried out.