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 WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING

2018 ◽  
Vol 15 (1) ◽  
pp. 63
Author(s):  
Destri Yanti Hutapea ◽  
Octaviani Hutapea
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Original Image ◽  
Least Significant Bit ◽  
Confidential Information ◽  
Sensing Data ◽  
Remote Sensing Satellite

Remote sensing satellite imagery is currently needed to support the needs of information in various fields. Distribution of remote sensing data to users is done through electronic media. Therefore, it is necessary to make security and identity on remote sensing satellite images so that its function is not misused. This paper describes a method of adding confidential information to medium resolution remote sensing satellite images to identify the image using steganography technique. Steganography with the Least Significant Bit (LSB) method is chosen because the insertion of confidential information on the image is performed on the rightmost bits in each byte of data, where the rightmost bit has the smallest value. The experiment was performed on three Landsat 8 images with different area on each composite band 4,3,2 (true color) and 6,5,3 (false color). Visually the data that has been inserted information does not change with the original data. Visually, the image that has been inserted with confidential information (or stego image) is the same as the original image. Both images cannot be distinguished on histogram analysis.  The Mean Squared Error value of stego images of  all three data less than 0.053 compared with the original image.  This means that information security with steganographic techniques using the ideal LSB method is used on remote sensing satellite imagery.

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 Estimation of Total Suspended Sediment Solid in Porong River Waters Using Multitemporal Satellite Imagery

2021 ◽  
Vol 936 (1) ◽  
pp. 012006
Author(s):  
Z N Ghuvita Hadi ◽  
T Hariyanto ◽  
N Hayati
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Satellite Imagery ◽  
River Estuary ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Sample Point ◽  
Sensing Data ◽  
Google Earth Engine

Abstract Monitoring the concentration of Total Suspended Solid (TSS) is one method to determine water quality, because a high TSS value indicates a high level of pollution. Remote sensing data can be used effectively in generating suspended sediment concentrations. Nowdays, Google Earth Engine platform has provided a large collection of remote sensing data. Therefore, this study uses Google Earth Engine which is processed for free and aims to calculate the TSS value in the Kali Porong area. This research was conducted multitemporal in the last ten years, namely from 2013-2021 using multitemporal satellite imagery landsat-8 and sentinel-2 by applying empirical algorithms for calculating TSS. The results of this study are the value of TSS concentration at each sample point and a multitemporal TSS concentration distribution map. The year 2016, 2017, and 2021, the distribution of TSS concentration values was higher than in other years. At the sample point, the lowest TSS concentration value was 16.55 mg/L in 2013. Meanwhile, the highest TSS concentration value of 266.33 mg/L occurred in 2014 precisely in the Porong River estuary area which is the border area between land and water. the sea so that a lot of TSS material is concentrated in the area due to waves and ocean currents.

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 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 The estimation of the impact of anthropogenic and natural factors on state of the forests using remote sensing data in Staritsky forestry of Tver region

2021 ◽  
pp. 127-137
Author(s):  
Александра Федоровна Мейсурова ◽  
Наталья Юрьевна Сметанина
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Timber Harvesting ◽  
The State ◽  
Common Variants ◽  
Sensing Data ◽  
Natural Factors ◽  
Tver Region ◽  
The Impact

Проведена оценка влияния антропогенных и природных факторов на состояние лесов Старицкого лесничества Тверской области на основе серий спутниковых изображений Santinel-2 за период с 2019 по 2021 гг. Использованы распространенные варианты комбинаций каналов для интерпретация основных видов лесоизменений: рубки - комбинация 4,3,2 «естественные цвета»; подтопление - комбинация 5,6,2 - «здоровая растительность» с преобладанием фиолетовых оттенков; ветровалы и буреломы - комбинация 5,4,3 - «искусственные цвета» с преобладанием красного цвета. Выяснено, что общая площадь лесоизменений в лесничестве составила 2246,9 га. Наибольшее воздействие на состояние лесов изученной территории оказывает вырубка лесных насаждений с целью заготовки древесины. Общая площадь вырубленных лесов составила 92% от общей площади всех лесоизменений. An assessment of the influence of anthropogenic and natural factors on the state of the forests of the Staritsa Forestry of the Tver Region was carried out in a series of Santinel-2 satellite images for the period from 2019 to 2021. Common variants of canal combinations were used to interpret the main types of forest changes: felling - a combination of 4,3,2 "natural colors"; flooding - a combination of 5,6,2 - "healthy vegetation" with a predominance of purple tints; windblows and windbreaks - a combination of 5,4,3 - "artificial colors" with a predominance of red. The total area of forest changes in the forestry was 2246.9 hectares. The greatest impact on the state of forests in the studied area are done by the timber-harvesting activities. The total area of deforestation was 92% of the total area of all forest changes.

scholarly journals Depth Faults Along The Regmatic Network Of The Chatkalo-Kuramin Region And Their Relationship With Seismicity

2020 ◽  
Vol 02 (12) ◽  
pp. 68-76
Author(s):  
Madinabonu Zaxritdinovna Fazliddinova ◽  
◽  
Akram Bayramovich Goipov ◽  
Maftuna Asad qizi Saidova ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Comprehensive Analysis ◽  
Landsat 8 ◽  
Strong Earthquakes ◽  
Sensing Data ◽  
Aster Gdem ◽  
Digital Elevation ◽  
Lineament Analysis

Lineaments were identified using LANDSAT-8 satellite images and digital elevation models obtained from the ASTER GDEM satellite over the Chatkal-Kuramin region. Taking into account the stock materials and a comprehensive analysis of the results of processing remote sensing data, a map of lineaments of a 1: 100,000 regmatic network was compiled. Based on the automated visual lineament analysis in the Geomatica PCI program, lineaments of the regmatic network were obtained, which are located in the focal zones of strong earthquakes.

scholarly journals Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

2021 ◽  
Vol 13 (10) ◽  
pp. 2014
Author(s):  
Celina Aznarez ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Juan Pablo Pacheco ◽  
Javier Senent-Aparicio
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Ecosystem Services ◽  
Water Resources ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Erosion Control ◽  
Sensing Data ◽  
Extreme Precipitation Events ◽  
The Impact

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

scholarly journals Mapping Allochemical Limestone Formations in Hazara, Pakistan Using Google Cloud Architecture: Application of Machine-Learning Algorithms on Multispectral Data

2021 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Muhammad Fawad Akbar Khan ◽  
Khan Muhammad ◽  
Shahid Bashir ◽  
Shahab Ud Din ◽  
Muhammad Hanif
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Learning Algorithms ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Sensing Data ◽  
Fossiliferous Limestone

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

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