scholarly journals Ecological assessment of the territorial complexes of Tatarbunars’kyi district of Odessa region

2020 ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Negative Impact ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Economic Structure ◽  
Anthropogenic Factors ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
The Impact

Formulation of the problem. The Tatarbunars’kyi District is located in the southwestern part of Odessa region and reflects the main features of the landscape-economic structure of the region: water, agricultural, resort and environmental areas. On the other hand, the form of land use is characterized by widespread plowing of land with degradation and erosion of soil cover. Land structure and use patterns have a complex negative impact on ecological and economic processes and cannot ensure the sustainable development of the region, in particular it is antagonistic to the unique transitional wetland ecosystems of international importance located within the area. To solve the issues of balanced environmental management and zoning of the landscape and economic structure of the region, Earth remote sensing (ERS) data can be used - spectrozonal satellite imagery and geographic information systems (GIS), which can simultaneously cover the research area as a whole, carry out regular monitoring and significantly reduce costs by expensive expeditionary work. Using space monitoring data allows you to get a large array of characteristics of the state of the territorial complexes of the region. Purpose of the work is: assessment of the ecological state of the landscape economic structure and development of recommendations for the protection of natural and territorial complexes of the Tatarbunar’skyi District of Odessa region based on the use of GIS and remote sensing data. Methods. Landsat8 satellite images with OLI and TIRS sensors, digital terrain models (SRTM) with a spatial resolution of 30 m were used as initial data. The spatial distribution of the population was carried out on the basis of OpenStreetMap data using automatic interpolation using the IDW method. Spatial analysis and data processing were carried out in the QGIS v3.4.6 software package. To quantify the vegetation cover, the Normalized Difference Vegetation Index - NDVI was calculated. Waterlog distribution was estimated using a modified normalized differential moisture index (NDMI). The analysis of the structure of land use and anthropogenic load was carried out on the basis of ranking of territorial objects into homogeneous groups to calculate geoecological coefficients. Results. The article discusses the possibilities of using Earth remote sensing data for a functional assessment of land changes as a result of anthropogenic activities, primarily arable land, analyzes the ecological and economic equilibrium of the region based on geoecological coefficients, identifies areas that are primarily exposed to environmental risks, exogenous processes and the impact anthropogenic factors. Measures are proposed to increase the environmental sustainability of agrolandscapes and the landscape-anthropogenic structure of the region’s lands. A detailed hydrological and morphometric analysis of the catchment basin was carried out. Karachaus within the boundaries of the District. For the catchment estuary, remediation and nature conservation measures based on GIS are proposed and designed.

scholarly journals Efficient land use based on remote sensing data

2021 ◽  
Vol 3 ◽  
pp. 180-185
Author(s):  
Y. M. Kenzhegaliyev ◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Crop Productivity ◽  
The State ◽  
Agricultural Crops ◽  
Earth Remote Sensing ◽  
Spectral Reflectivity ◽  
Sensing Data ◽  
Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

scholarly journals Monitoring of specially protected natural territories of forest-steppe landscapes of the Stavropol upland by means of remote sensing data

2021 ◽  
Vol 27 (1) ◽  
pp. 364-376
Author(s):  
Tigran Shahbazyan
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Anthropogenic Factors ◽  
Forest Steppe ◽  
Landsat 8 ◽  
Sensing Data ◽  
Ndvi Index ◽  
The Impact

The article considers the methodology of monitoring specially protected natural areas using remote sensing data. The research materials are satellite images of the Landsat 5 and Landsat 8 satellites, obtained from the resource of the US Geological Survey. The key areas of the study were 3 specially protected areas located within the boundaries of the forest-steppe landscapes of the Stavropol upland, the reserves «Alexandrovskiy», «Russkiy Les», «Strizhament». The space survey materials were selected for the period 1991–2020, and the data from the summer seasons were used. The NDVI index is chosen as the method of processing the spectral channels of satellite imagery. To integrate long-term satellite imagery into a single raster image, the method of variance of the variation series for the NDVI index was used. The article describes an algorithm for processing satellite images, which allows us to identify the features of the dynamics of the vegetation state of the studied territory for the period 1991–2020. The bitmap image constructed by means of the variance of the NDVI index was classified by the quantile method, to translate numerical values into classes with qualitative characteristics. There were 4 classes of the territory according to the degree of dynamism of the vegetation state: “stable”, “slightly variable”, “moderately variable”, “highly variable”. The paper highlights the factors of landscape transformation, including natural and anthropogenic ones. In the course of the study, the determining influence of anthropogenic factors of transformation was noted. The greatest impact is on the reserve «Alexandrovskiy», the least on the reserve «Russkiy Les», in the reserve «Strizhament» the impact is expressed locally. The paper identifies the leading anthropogenic factors of vegetation transformation, based on their influence on vegetation.

scholarly journals Land use analysis of Lake Khanka basin using remote sensing data

2021 ◽  
Vol 895 (1) ◽  
pp. 012007
Author(s):  
K Yu Bazarov ◽  
E G Egidarev ◽  
N V Mishina
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Paddy Field ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Open Pit ◽  
Geoinformation Systems ◽  
Sensing Data ◽  
Land Use Structure ◽  
Lake Khanka

Abstract The paper presents results of the analysis of the land use map compiled for transboundary Lake Khanka Basin using remote sensing data and geoinformation systems. The map reflects the distribution of 12 land categories in Lake Khanka basin in 2017 (arable land, abandoned arable land, paddy field, abandoned paddy field, shrubs and sparse growth, forest land, open pit, settlements, meadows and pastures, wet meadows and marshes, water bodies, forest cuttings and fire sites). The data of land use structure in the whole Lake’s watershed, in its Russian and Chinese parts are given. Data on the distribution of different land categories in the administrative territories of the rank of districts (Russia) and counties (China) are also presented. The analysis of land use structure showed that about 50 % of the Chinese part of the basin is covered by anthropogenically transformed natural complexes. The share of such lands in the territory of Russia amounts to 28 %. Agriculture is the most important factor in the change of natural complexes in Lake Khanka basin. Before early 1990s, the area of farmland had increased in the basin on both sides of the border, after that there was a significant reduction in cultivated lands, which had lasted for 10 years in the territory of China and for 20 years in Russia. Over the past decade, the area of cultivated areas in the basin and adjacent territories has extended again, which indicates an increase of anthropogenic impact and requires serious attention to monitoring of the ecological state of lands in the basin.

scholarly journals Methods of decoding of the geoecological conditions of territories of mining complexes based on the data of earth remote sensing

2019 ◽  
Author(s):  
А.В. Иванов ◽  
А.В. Стриженок ◽  
И.К. Супрун
Keyword(s):  
Remote Sensing ◽  
Environmental Monitoring ◽  
Industrial Waste ◽  
Negative Impact ◽  
Remote Sensing Data ◽  
The State ◽  
Economic Damage ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
The Earth

В последние десятилетия наблюдается устойчивая тенденция к значительному увеличению объемов добычи и переработки минерального сырья, что, в свою очередь приводит к увеличению объемов промышленных отходов, преобладающим способом утилизации которых является наземное размещение в виде техногенных массивов. Такие техногенные массивы особенно сильно подвержены риску возникновения чрезвычайных ситуаций, которые могут повлечь за собой значительный социальный, экологический и экономический ущерб. В этой связи особую актуальность для общества, экономики и государства приобретает разработка и внедрение на территории воздействия хранилищ промышленных отходов систем экологического мониторинга, позволяющих оперативно выявлять источники техногенной нагрузки и осуществлять своевременную их ликвидацию. Одной из наиболее инновационных и развивающихся сфер экологического мониторинга на сегодняшний день является мониторинг состояния компонентов природной среды на основании данных дистанционного зондирования земли. На сегодняшний день мониторинг состояния природных объектов, подверженных негативному воздействию предприятий минерально-сырьевого комплекса, с использованием данных дистанционного зондирования земли представляет значительный практический и научный интерес. Все разнообразие приемов и способов дешифрирования космоснимков сводится к двум основным методам: визуальному и автоматизированному (компьютерному). Под визуальным дешифрированием понимается процесс, выполняемый оператором. В противоположность этому автоматизированное (компьютерное) дешифрирование это программная обработка снимков на компьютере при помощи специальных программ. Космическая съемка заняла прочное место в системе средств, применяемых при проведении мониторинга окружающей среды. Перечень тематических задач, решаемых по данным дистанционного зондирования Земли огромен, а мониторинг экологической ситуации территорий, подверженных негативному воздействию объектов минерально-сырьевого комплекса, на основании данных дистанционного зондирования Земли является перспективным и активно развивающимся методом экологического мониторинга. В данной работе представлены основные методы дешифрирования геоэкологических условий территорий горнопромышленных комплексов на основании данных дистанционного зондирования земли There are a steady tendency to significantly increase the volume of extraction and processing of mineral raw materials on our planet in recent decades. It leads to an increase in the volume of industrial waste, which predominant method of disposal is placement on the Earth surface in the form of anthropogenic arrays. Such anthropogenic arrays are especially subjecting to the risk of emergencies, which can entail significant social, environmental and economic damage. In this regard, the development and implementation of environmental monitoring systems on the territory of the impact of industrial waste storages, which can quickly identify sources of anthropogenic load and carry out their timely elimination, is of particular relevance to society, the economy and the state. Monitoring of the state of components of the environment based on remote sensing data is one of the most innovative and developing areas of environmental monitoring today. Today, monitoring the state of natural objects exposed to the negative impact of the enterprises of the mineral resource complex using data from remote sensing of the earth is of significant practical and scientific interest. The whole variety of techniques and methods for decoding satellite images is reduced to two main methods: visual and automated (computer). Visual decryption refers to the process performed by the operator. In contrast, automated decryption is the software processing of images on a computer using special programs. Space imagery has taken a strong place in the system of tools used in environmental monitoring. The list of thematic tasks to be solved according to the Earths remote sensing data is huge, and monitoring the environmental situation of territories exposed to the negative impact of the mineral resources complex, based on the Earths remote sensing data, is a promising and actively developing method of environmental monitoring. This paper presents the main methods of decoding of the geo-ecological conditions of territories of mining complexes based on the data of earth remote sensing.

scholarly journals Study on the impact of coastal land use change on environment based on remote sensing data

2021 ◽  
Vol 267 ◽  
pp. 01061
Author(s):  
Liu Yong ◽  
Yunlin Chen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Economic Development ◽  
Coastal Zone ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Target Object ◽  
Surface Features ◽  
Sensing Data ◽  
The Impact

The coastal zone is the bridge between the ocean and the mainland, the junction of the two ecosystems, the focus of the economic development of coastal cities and the gathering place of ports. Remote sensing technology uses the detector to receive the electromagnetic wave from the target object. After processing the information, it can distinguish the attributes of the target object. It is widely used in marine development, aerospace understanding, resource exploration and other fields.In this paper, the coastal zone of Shangyu Economic Development Zone on the south coast of Hangzhou Bay is taken as the research area. Using multi-source remote sensing data, information extraction, change monitoring and analysis are carried out from the perspective of marine and land ecosystems, and the impact of coastal development on the coastal zone is discussed. The main conclusions are as follows: (a) Using visual interpretation method, it is found that the coastline of the study area changes obviously, and the decrease trend is below the total coastline length; Fractal dimension index is used to characterize the natural condition of coastline. The total coastline length, natural coastline and artificial coastline all increase, which means that the amount of beach sediment deposition and the degree of artificial intervention have increased in this stage. (b) The object-oriented method is used to extract the land use classification of the coastal zone in the study area. Cultivated land is the main land type in the study area, and the impervious surface is the fastest growing. The degree of artificial development of the whole study area is gradually increasing, and the coastal beach area is greatly reduced, and the impervious surface area is greatly increased. Wetland and impervious surface are the two most dramatic changes in the study period. Wetland is mainly transformed into other surface features, while impervious surface is mainly transformed into other surface features.

scholarly journals The impact of aeration on ecological state of lake Telbyn in Kyiv

2021 ◽  
Vol 30 (1) ◽  
pp. 179-189
Author(s):  
Viktor I. Vyshnevskyi ◽  
Vladislav A. Zhezherya ◽  
Inna M. Nezbrytska ◽  
Olena P. Bilous
Keyword(s):  
Remote Sensing ◽  
Water Surface ◽  
Negative Impact ◽  
Remote Sensing Data ◽  
Bottom Layer ◽  
Small Cells ◽  
Ecological State ◽  
Sensing Data ◽  
Artificial Aeration ◽  
The Impact

Lake Telbyn is considered to be one of the largest lakes located in the eastern part of Kyiv. The artificial aeration of this lake was started at the end of 2016 by using of 8 aerators, which has been continuing so far. The main perpose of this measure is improving the ecological state of the lake mostly for recreational use. There were carried out a field study of the lake and the analysis of remote sensing data. Physical and chemical characteristics of water, phytoplankton biomass, chlo- rophyll a concentration and some other parameters at the different depths were studied. It was found out that artificial aeration has a positive effect on the ecological state of the lake. The water aeration causes the blur of thermocline whereas the impact on its depth is not essential. Under impact of aeration the concentration of dissolved oxygen become larger, mostly in the bottom layer. The highest concentration of ammonium nitrogen in a warm period is observed in the bottom layer of the lake. The deep location of aerators causes the increasing of concentration in bottom layer. At the same time there is not visible impact on concentration near the surface. The similar result was obtained for the concentration of inorganic phosphorus. The impact of aeration on algal bloom is not such essential as on hydrochemical characteristics. The artificial aeration causes negative impact on the phytoplankton abundance and less effect on their biomass. It means the larger effect on the algae with small cells. In other words the aeration has larger impact on green algae than on blue-green ones. The use of remote sensing data showed that ecological state of Lake Telbyn during the aeration period improved comparably with other lakes of Kyiv. As a result of aeration, the view of water surface of the lake became more similar to water surface of the Dnipro River, which flows through the city.

scholarly journals Online database “See The Sea” for the Caspian Sea

2019 ◽  
Vol 25 ◽  
pp. 79-90 ◽  
Author(s):  
Olga Yu. Lavrova ◽  
Marina I. Mityagina ◽  
Andrey G. Kostianoy
Keyword(s):  
Remote Sensing ◽  
Caspian Sea ◽  
Seismic Activity ◽  
Remote Sensing Data ◽  
Anthropogenic Factors ◽  
Main Task ◽  
The Caspian Sea ◽  
Sensing Data ◽  
Current Century ◽  
The Impact

For many years, the primary environmental problem of the Caspian Sea has been oil pollution, which is associated both with oil production and transportation, as well as changes in sea level, leading to secondary pollution, river runoff and even seismic activity, which provokes natural oil spills from the bottom of the sea. Abnormal bloom of waters every year becomes more and more long and covers more and more areas, and also occurs in areas where it was not previously observed. However, the current state of the sea, and the trends of its evolution has not been studied enough, which determines the relevance of the solution of the main task of the ongoing Russian Science Foundation Project “Assessing ecological variability of the Caspian Sea in the current century using satellite remote sensing data”. Implementation of the proposed project will assess the relative contribution of each of the sources of pollution of the Caspian Sea, which varies in different periods depending on climatic factors, on the intensity of various hydrodynamic and hydrometeorological processes, on seismic activity and human economic activity. The goal of the project is to assess the changes in the ecological state of the Caspian Sea since the beginning of the current century under the impact of natural and anthropogenic factors. This calls for a detailed analysis of large banks of satellite data acquired over the Caspian Sea from 1999 to 2022 jointly with multi-year hydrometeorological and in situ data. The goal is achievable due to powerful capabilities of the “See the Sea” (STS) information portal developed by the Space Research Institute of the Russian Academy of Sciences (IKI RAS) as part of IKI - Monitoring Center for Collective Use. STS offers oceanographers new and unique tools to work with remote sensing data, enabling comprehensive analysis of data different in physical nature, spatial resolution and time of acquisition.

scholarly journals Building and precision assessment of regression models for determining of cereals’ and legumes’ crop yield based on Earth remote sensing data and climatic characteristics

2020 ◽  
Vol 26 (3) ◽  
pp. 159-169
Author(s):  
Alexey Stepanov ◽  
Tatiana Aseeva ◽  
Konstantin Dubrovin
Keyword(s):  
Remote Sensing ◽  
Spring Wheat ◽  
Regression Models ◽  
Arable Land ◽  
Soil Layer ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Climatic Characteristics ◽  
Sown Area

Crop yields are strictly dependent from natural and climatic conditions of the growing region, in addition specific weather conditions in the southern part of the Far East necessitates the analysis of a large number of factors when building a predictive regression model. The article presents regression models for assessing the average productivity of the main crops in Chernigovsky district of Primorsky region: soybean, spring wheat, barley and oat. Between 2012 and 2018 the sown area of these crops ranged from 78 to 86 % of the total sown area in the Chernigovsky district. We used the indicators obtained from Earth remote sensing data (the maximum weekly NDVI per year, calculated from the mask of arable land in the Chernigovsky district) and meteorological characteristics (from 2008 to 2018): hydrothermal Selyaninov coefficient, the duration of the growing season, temperature and humidity of the upper soil layer, photosynthetically active radiation and the Budyko radiation index. Climatic characteristics of arable land, representing reanalysis data and combining ground based and remote observations, were obtained using the Vega–Science web–service. Also, we used data about sown area and gross crop in the Chernigovsky region from 2008 to 2018. It was found that average annual oat yield has the biggest coefficient of variation (31.5 %). The corresponding indicator for the remaining crops is in range from 16 to 18 %. The accuracy analysis of the obtained models showed that the average error of the model in period from 2008 to 2017 was 4.1 % for barley, 5.1 % for oat and spring wheat, and 5.4 % for soybean.

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