scholarly journals Monitoring of glaciers in the Polar Urals using remote sensing Data

2020 ◽  
Vol 217 ◽  
pp. 04003
Author(s):  
Natalia Martynova ◽  
Valentina Budarova ◽  
Artem Sheremetinsky ◽  
Nikita Mezentsev
Keyword(s):  
Remote Sensing ◽  
Technological Progress ◽  
Satellite Images ◽  
Research Question ◽  
Remote Sensing Data ◽  
Field Work ◽  
Sources Of Information ◽  
Sensing Data ◽  
Polar Urals ◽  
The Polar Urals

The development of technological progress provides more opportunities for indirect monitoring of changes in the environment. Remote sensing is one of The most accessible and reliable sources of information. In this work, we used satellite images from the Landsat family. The theoretical justification of the research question is given. The research methodology was developed. Collection and processing of satellite images for various time periods. A series of schematic maps based on remote sensing Data has been created. As a result of digitization of satellite images, 9 glacier contours were obtained by year. We determined the area of the Romantics glacier and found that it lost at least 60% of its original area. These studies were used to build a series of cartographic schemes that clearly show the reduction of the glacier area. It is concluded that the use of remote sensing allows you to solve problems, monitoring the object. The use of this method allows not only to save time for field work, but also material costs for expedition equipment and various equipment. This method can be tested on any objects.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Summaries of certain spatial patterns retrieved from multidate remote-sensing data

2004 ◽  
Vol 2004 (2) ◽  
pp. 287-300
Author(s):  
Hema Nair
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Spatial Patterns ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Geographic Area ◽  
Image Mining ◽  
Sensing Data

This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as “Y isF” can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.

scholarly journals Recognition of “flowering” processes on the base of remote sensing data in shallow water ponds on the example of the Azov Sea

2020 ◽  
Vol 175 ◽  
pp. 12013 ◽  
Author(s):  
Marina Ganzhur ◽  
Nikita Dyachenko ◽  
Olga Smirnova ◽  
Anna Poluyan ◽  
Natalya Panasenko
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Shallow Waters ◽  
Sea Of Azov ◽  
The Sea Of Azov ◽  
Sensing Data ◽  
Azov Sea

This work considers to the processes of «bloom» phytoplankton processes that cause hypoxic phenomena in shallow waters the example of the Sea of Azov. For the accumulation of information, multichannel satellite images of remote sensing are taken as a basis. In the process, the task of programmatically highlighting the contours of the areas of «bloom» is implemented.

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.

Estimating basin-wide air temperature by partial integration of remote sensing data

2018 ◽  
Vol 55 (10) ◽  
pp. 1196-1206
Author(s):  
Vedran Ivezic ◽  
Damir Bekic ◽  
Igor Kerin
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Standard Methods ◽  
Sensing Data ◽  
Interpolation Methods ◽  
Partial Integration

A comparison of various methods that enable temporally continuous computation of basin-wide air temperature is presented. An approach that combines remote sensing data with measurements at meteorological stations for obtaining basin-wide air temperature is proposed and compared to the standard interpolation methods of point measurements. For a basin of over 1000 km2, the proposed approach provides significantly more reliable air temperature rasters (average Δ = 9%) than the standard interpolation methods (average Δ = 25%), all by using satellite images and measurements from only two meteorological stations in comparison to standard methods using measurements from 10 meteorological stations.

scholarly journals Remote monitoring of the state of hydrocarbon pollution of the geological environment and assessment of the risks of dangerous situations in areas of long-term oil and gas production and compact living of the population

2020 ◽  
pp. 14-26
Author(s):  
Volodymyr Filipovych ◽  
Anton Mychak ◽  
Alexandr Kudryashov ◽  
Ruslan Shevchuk
Keyword(s):  
Remote Sensing ◽  
Remote Monitoring ◽  
Oil And Gas ◽  
Remote Sensing Data ◽  
Field Work ◽  
Gas Production ◽  
Hydrocarbon Production ◽  
Sensing Data ◽  
Space Data

The results of the analysis of geoecological problems of territories of long-term hydrocarbon production are presented. Based on the analysis of ground-based and remote studies, the possibilities of space data were determined during the eco-monitoring of hydrocarbon deposits. A methodological and technological scheme for assessing the risks of harming the environment is proposed. The list of tasks that can be solved using Earth remote sensing data:- control of environmental pollution by hydrocarbons (oil, gas) at different stages of the functioning of the oil and gas complex - from the search and exploitation of deposits, to the transportation, storage and processing of petroleum products;- flooding and flooding of territories of oil production by underground waters;- identification, mapping, field studies, discontinuous violations of various ranks, are ways of migration of oil and gas in the upper layers of the earth's crust;- identification and mapping of technogenic sources of gas contamination of the surface layer of the atmosphere, namely:- non-geometrical wells, oil and gas pipelines;- buried under modern sediments, pit-well houses, mines.Satellite monitoring consists of 4 stages. At the first, preliminary stage, objects of remote monitoring are determined, a base of satellite and thematic data is formed, the foundations of a future thematic GIS are laid. At the second stage, the actual detection (decryption) of objects and territories polluted with hydrocarbons is carried out. At the next, third stage, a set of field work is carried out in order to validate the research methodology and verify the data obtained using ground-based measurements.At the fourth, final stage, based on an analysis of all the information received, an assessment of the risks of dangerous situations is carried out and their possible consequences are predicted.The possibilities of assessing the risks of dangerous situations in areas of long-term hydrocarbon production according to remote sensing data are considered. A review of hazardous situations resulting from hydrocarbon production in the city of Borislav is given .; recommendations are proposed to reduce the risk of their occurrence.

scholarly journals Application of Artificial Intelligence Algorithms to Control the Use of Flood-prone Areas

2021 ◽  
pp. 6-24
Author(s):  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Large Spatial Scale ◽  
Sensing Data ◽  
Hazardous Area ◽  
Flood Prone Areas

This article examines the possibility of using artificial intelligence tools to analyze the use of territories prone to flooding during floods. A modern system for monitoring the economic use of flood-prone areas should be based on the use of Earth remote sensing data. The analysis of satellite images, being a laborious task, can be automated through the use of specially trained convolutional neural networks of semantic segmentation based on the algorithm proposed in this article. In this work, on the previously identified flooding zones, using remote sensing data, development objects are automatically determined (segmented) for different times and, by combining information at different times, an assessment of the intensity of this construction in the inter-flood period is made. To form a training sample, a survey of several settlements in the Trans-Baikal Territory was carried out using unmanned aerial vehicles. The neural network was configured using the Python language and the PyTorch library. To select the best convolutional neural network configuration, various combinations of architectures and encoder types were tested for performance and accuracy. The best result in terms of speed and accuracy was shown by the U-Net architecture, built using a convolutional neural network with an SE-ResNeXt50 encoder. According to satellite images of high spatial resolution for the Aginskoye village of Trans-Baikal Kray, a development map was drawn in the flood hazardous area in 2013 and 2019. The objects of development in the period between floods were identified. The results of the study can make it possible to consider a number of important factors when planning the rational use of flood-prone areas in order to improve the quality of life in the region. The obtained maps of the development of flood-prone zones of a large spatial scale are planned to be recommended in the work of state authorities in the field of water resources protection and elimination of natural disasters.

Sign in / Sign up

Export Citation Format

Share Document