Satellite Images In Order Research Articles

CORRELATION OF TECTONICS, SEISMICITY AND GEOTHERMICS OF LESBOS ISLAND USING REMOTE SENSING DATA AND GEOGRAPHICAL INFORMATION SYSTEMS

The development of a specialized Geographical Information System aiming at the better understanding of the relation between tectonics, seismicity and geothermal potential of Lesbos Island is discussed in the present paper. The development of this system was based on the processing and analysis of satellite images in order to identify both tectonic and thermal anomalies for further correlation with available vector and raster data. For this purpose a database including topology, geology, tectonics, seismicity and geothermy, was created. This data set derived from digitizing the topographic and geological maps of HAGS and IGME, from the analysis of the satellite image and from bibliography. As a result of the data processing there were indications about new evidence concerning the tectonics and the geothermy of Lesbos Island.

Species Diversity of Oak Stands and Its Significance for Drought Resistance

Drought periods have an adverse impact on the condition of oak stands. Research on different types of ecosystems has confirmed a correlation between plant species diversity and the adverse effects of droughts. The purpose of this study was to investigate the changes that occurred in an oak stand (Krotoszyn Plateau, Poland) under the impact of the summer drought in 2015. We used a method based on remote sensing indices from satellite images in order to detect changes in the vegetation in 2014 and 2015. A positive difference was interpreted as an improvement, whereas a negative one was treated as a deterioration of the stand condition. The Shannon-Wiener species diversity was estimated using an iterative principal component analysis (PCA) algorithm based on aerial images. We observed a relationship between the species indices of the individual forest divisions and their response to drought. The highest correlation between the index differences and the Shannon-Wiener indices was found for the Green Normalized Difference Vegetation Index (GNDVI) index (+0.74). In addition, correlations were observed between the mean index difference and the percentage shares in the forest divisions of species such as Pinus sylvestris L. (P. sylvestris) (+0.67 ± 0.08) and Quercus robur L. (Q. robur) (−0.65 ± 0.10). Our results lead us to infer that forest management based on highly diverse habitats is more suitable to meet the challenges in the context of global climatic changes, characterized by increasingly frequent droughts.

Detection of Military Underground Structures through the Remote Sensing Investigation of Phenological Cycle of Crops

This paper aims to explore how field spectroscopy is essential for remote sensing studies for the detection and monitoring of various features such military underground structures in Cyprus. A SVC-HR1024 field spectroradiometer was used and in-band reflectances were determined for medium resolution Landsat-7 ETM satellite sensor, in order to study possible differences of the spectral signature of vegetation throughout the phenological cycle of plant growth. In this study, two test areas were identified, analyzed and modelled: 1) Military Structure Area (MSA) where underground structure exists, and 2) Reference Area (RA) where underground military structure does not exist. The Normalized Difference Vegetation Index (NDVI) was applied to Landsat-7 ETM and Sentinel-2 satellite images in order to identify the presence or absence of underground structures in the study area.

Factors Limiting Formation of Community Forestry Enterprises in the Southern Mixteca Region of Oaxaca, Mexico.

Many studies have considered community-based forestry enterprises to be the best option for development of rural Mexican communities with forests. While some of Mexico's rural communities with forests receive significant economic and social benefits from having a community forestry enterprise, the majority have not formed such enterprises. The purpose of this article is to identify and describe factors limiting the formation of community forestry enterprise in rural communities with temperate forests in the Southern Mixteca region of Oaxaca, Mexico. The study involved fieldwork, surveys applied to Community Board members, and maps developed from satellite images in order to calculate the forested surface area. It was found that the majority of Southern Mixteca communities lack the natural and social conditions necessary for developing community forestry enterprise; in this region, commercial forestry is limited due to insufficient precipitation, scarcity of land or timber species, community members' wariness of commercial timber extraction projects, ineffective local governance, lack of capital, and certain cultural beliefs. Only three of the 25 communities surveyed have a community forestry enterprise; however, several communities have developed other ways of profiting from their forests, including pine resin extraction, payment for environmental services (PES), sale of spring water, and ecotourism. We conclude that community forestry enterprise are not the only option for rural communities to generate income from their forests; in recent years a variety of forest-related economic opportunities have arisen which are less demanding of communities' physical and social resources.

Processing and Use of Satellite Images in Order to Extract Useful Information in Precision Agriculture

Image analysis methods were developed and diversified greatly in recent years due to increasing speed and accuracy in providing information regarding land cover and vegetation in urban areas. The aim of this paper is to process satellite images for monitoring agricultural areas. Satellite images used in this study are medium and high resolution images taken from QuickBird and SPOT systems. Based on these images, a supervised classification was performed of a very large area, having as result the land use classes. Supervised classification can be defined as the ability to group the pixels that compose the satellite image, digitally, in accordance with their real significance. Gaussian algorithm of maximum similarity (Maximum likelihood) was used, referred to in the specialty literature as maximum likelihood method or probabilistic classification, and based on the use of probability theory (function Gaussian) to compare the spectral values of each pixel in hand with statistical " fingerprint "of each area of interest. Practically, conditional probabilities were calculated of belonging to one class or another. The points in the middle of the group have a higher probability of belonging to the certain class, probability intervals (concentric isolines or contours of equal probability) being delimited graphically by izocontours expressing spectral variations within each set of training.

Application of spectral analysis to discriminate hydrothermal alteration zones at Daralu copper deposit, SE Iran

The aim of this research is discrimination of the hydrothermal alterations related to copper mineralization in the Daralu deposit, which is located in the south of Kerman, SE Iran. The rocks of the surrounding area consist of Eocene volcanics and Oligocene–Miocene intrusive bodies. The main mineralization has occurred in a granodiorite porphyry intrusion and to some extent in the nearby volcanic and pyroclastic rocks. Some methods such as band ratio, principal component analysis (PCA), and supervised classification methods like spectral angle mapper (SAM), spectral feature fitting (SFF), and linear spectral unmixing (LSU) were carried out on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Enhanced Thematic Mapper Plus (ETM+) satellite images in order to detect propylitic, phyllic, and argillic alterations. In addition, potassic alteration was studied by LSU using region of interest (ROI) method for resampling on the basis of the field studies. LSU procedure displayed the best matching with the reality in comparison with the field observations at the Daralu deposit. Two major fault systems with NW-SE and NE-SW trends were determined by remote sensing using linear enhance and sharpen filter by visible near-infrared (VNIR) in ASTER bands. Propylitic, argillic, and phyllic alterations show the same directions with the major faults of the area.

Optimizing the Heliosat-II Method for Surface Solar Irradiation Estimation with GOES Images

. The Heliosat-II method was developed to process Meteosat satellite images in order to assess the solar irradiation at surface. It is modified for its use with GOES satellite images in order to estimate the solar irradiation over French Guiana. Modifications include a change in the calibration formula, in the calculation of the cloud albedo, and in the values of the Linke turbidity factor. These modifications allow the improvement of the performances of the Heliosat-II model in Intertropical zones in French Guiana. The outcomes were compared to coincident measurements of hourly irradiation performed by ground stations. The results show that the use of the maximum apparent albedo as the cloud albedo (ρcmax), along with a fixed value of the Linke turbidity factor produces the best results; the bias was less than 23.61 Wh/m2 (5% of the mean value of the measurements) overall, and the RMSE was less than 109.39 W/m2 (24%) overall when using hourly values. In the case of daily means, the RMSE drops to less than 10.08 W/m2 (10%) overall, whereas the overall bias remains unchanged, as expected. The model performs better in clear skies than in cloudy skies. The results suggest that the Linke turbidity factor used in the Heliosat-II method does not reproduce well the attenuation of the solar radiation in French Guiana. The results were compared to those obtained from Heliosat II method and Meteosat images (HelioClim-3 database) and we found a better accuracy for GOES satellite data, which exhibits smaller viewing angle. The use of the Heliosat II method with GOES images allows assessment of the SSI with a good accuracy and high temporal and spatial resolutions.Résumé. La méthode Heliosat-II a été développée pour traiter des images du satellite Meteosat de façon à estimer l'irradiation solaire en surface. Elle a été modifiée pour être employée avec des images du satellite GOES de façon à estimer l'irradiation solaire au-dessus de la Guyane Française. Les modifications incluent un changement dans la formule d’étalonnage, dans le calcul de l'albédo des nuages et dans les valeurs de Trouble de Linke. Ces modifications permettent l'amélioration des performances du modèle Heliosat-II en zone intertropicale, notamment en Guyane Française. Les résultats ont été comparés à des mesures horaires coïncidentes d'irradiation horaire réalisées par des stations de mesure au sol. Les résultats montrent que l'utilisation de l'albédo apparent maximal en tant qu'albédo des nuages (ρcmax), ainsi qu'une valeur fixe de Trouble de Linke produit les meilleures estimations; le biais est inférieur à 23,61 Wh/m2 (5% de la valeur moyenne des mesures) dans l'ensemble et le RMSE est inférieur à 109,39 Wh/m2 (24%) dans l'ensemble en utilisant des valeurs horaires. Dans le cas de moyennes journalières, le RMSE vaut moins de 10,08 W/m2 (10%) alors que le biais a demeuré inchangé comme attendu. Le modèle fonctionne mieux en ciel clair qu'en ciel couvert. Les résultats suggèrent que le Trouble de Linke utilisé dans la méthode Heliosat-II ne reproduit pas correctement l'atténuation du rayonnement solaire en Guyane Française. Les résultats ont été comparés à ceux obtenus par la méthode Heliosat II avec les images Meteosat (HelioClim 3) et nous avons obtenu une meilleure précision pour les données issues du satellite GOES, qui présente un plus faible angle de visée. L'utilisation de la méthode Heliosat II avec des images GOES permet l'estimation de l'irradiation solaire de surface avec une bonne précision et à des résolutions temporelles et spatiales élevées

Capacidad de carga turística de las playas del Partido de Mar Chiquita, Provincia de Buenos Aires, Argentina

Mar Chiquita beaches’ geomorphology and anthropization is diverse. From south to north, this area includes the following coastal areas: Playa Dorada, Santa Elena, Santa Clara del Mar, Camet Norte, La Caleta, Mar de Cobo and Balneario Parque Mar Chiquita. This section is one of the most affected areas by erosion in the Buenos Aires Province. Erosion here is caused mainly by southerly storms and constant changes in the coastal landscape resulting from urbanization and the coastal defense work. Planning the maximum tolerable capacity for a given space and environmental land management help this phenomenon not to increase.This study was focused on determining the Tourism Carrying Capacity (TCC) for the Mar Chiquita beaches by using aerial photographs and satellite images in order to determinate the maximum amount of visitors in this resource without affecting its quality and analyze the evolution of municipal management in relation to coastal tourism management. TCC for the studied area was stated at 1,887.07 visitors/day in 2011, compared to 943.55 visitors for 1955.

위성영상을 활용한 생태피복도 작성과 E-GIS DB 구축

In this research, the ground coverage, the Albedo distribution, moisture content distribution, NDVI distribution of Busan Metropolitan City and the entire Gyeongnam region were calculated with the validation review using satellite images in order to establish Urban Environment & Ecology GIS DB. With Busan Metropolitan City and the entire Gyeongnam being the subjects, the satellite images of 2002 and 2012 were used to create the ground coverage, Albedo, NDVI distribution. The changes in the city development of the past 10 years and the ecological changes were compared and analyzed and by integrating DEM, the geographical information, it was meshed into a unit of <TEX>$500m{\times}500m$</TEX> in order to establish one integrated Urban Environment & Ecology GIS DB.

Inversion of top of atmospheric reflectance values by conic multivariate adaptive regression splines

Spatial technologies offer high flexibility to handle substantial amount of spatial data and wide range of modelling capabilities. Remotely sensed data are the most significant data source used in spatial technologies. However, it is often associated with uncertainties due to atmospheric effects (i.e. absorption and scattering by atmospheric gases and aerosols). Methods based on rigorous treatment of radiative transfer models still have some drawbacks in the inversion of top of atmospheric reflectance values to surface reflectance values on large numbers of satellite images. In this paper, our aim is to represent a more flexible (adaptive) approach for the regional atmospheric correction by employing nonparametric regression splines within the frame of inverse problems and modern techniques of continuous optimization. To achieve this objective, atmospheric correction models obtained through conic multivariate adaptive regression splines, which is an alternative method to multivariate adaptive regression splines by constructing a penalized residual sum of squares as a Tikhonov regularization problem, are applied on a set of satellite images in order to convert the top of atmospheric reflectance values into surface reflectance values. The results are compared with the ones obtained by both multivariate adaptive regression splines and a radiative transfer-based method.

Análisis multitemporal del cambio de uso del suelo, en el Paisaje Terrestre Protegido Miraflor Moropotente Nicaragua, 1993-2011

Ruiz, V., Savé, R., Herrera, A. 2013. Multitemporal analysis of land use change in the Terrestrial Protected Landscape Miraflor Moropotente Nicaragua, 1993-2011. Ecosistemas 22(3):117-123. Doi.: 10.7818/ECOS.2013.22-3.16. Multitemporal analysis to detect changes between different reference dates, deducing the evolution of the natural environment or the impact of human action on the environment.The purpose of the study was to evaluate the change in land use in the Miraflor Moropotente Terrestrial Landscape in the period 1993-2011, through satellite images in order to determine the status of landscape fragmentation. Changes in land use classification were derived from three Landsat TM images with a spatial resolution of 30 meters taken in February 1993, April 2000 and January 2011. We conducted a field verification to identify land cover and corroboration in satellite images. Fragmentation was performed with the calculation of metrics and indices of landscape-level fragmentation. The main results show that changes in land use are determined by anthropogenic degradation, mainly in the conversion of native vegetation to agricultural areas and the expansion of livestock. Population growth and monocultures are putting pressure on the forest, transformed areas for forestry to agricultural crops. Coverage changes meant a fragmented landscape with varying degrees of disruption, leading to a decrease in surface natural habitats, reducing the size of the fragments and isolates thereof.

Towards an Intelligent Predictive Model for Analyzing Spatio-Temporal Satellite Image Based on Hidden Markov Chain

Nowadays remote sensing is an important technique for observing Earth surface applied to different areas such as, land use, urban planning, remote monitoring, real time deformation of the soil that can be associated with earthquakes or landslides, the variations in thickness of the glaciers, the measurement of volume changes in the case of volcanic eruptions, deforestation, etc. To follow the evolution of these phenomena and to predict their future states, many approaches have been proposed. However, these approaches do not respond completely to the specialists who process yet more commonly the data extracted from the images in their studies to predict the future. In this paper, we propose an innovative methodology based on hidden Markov models (HMM). Our approach exploits temporal series of satellite images in order to predict spatio-temporal phenomena. It uses HMM for representing and making prediction concerning any objects in a satellite image. The first step builds a set of feature vectors gathering the available information. The next step uses a Baum-Welch learning algorithm on these vectors for detecting state changes. Finally, the system interprets these changes to make predictions. The performance of our approach is evaluated by tests of space-time interpretation of events conducted over two study sites, using different time series of SPOT images and application to the change in vegetation with LANDSAT images.

Long Term Monitoring of Air Pollution on Monuments and Cultural Heritage Sites in Cyprus Using Satellite Remote Sensing

Although cultural heritage sites are documented and preserved, to date there has been limited monitoring and documentation of how cultural heritage sites are affected by air pollution. This paper aims to introduce a new approach for monitoring air pollution for areas near cultural heritage sites by using satellite remotely sensed data. This approach provides a cost-effective tool for local authorities and government agencies to identify the most polluted cultural heritage sites and make decisions regarding the conservation of these sites. Archived data may be used in order to study long term the impact of air pollution to cultural heritage sites. The study area includes significant open air monuments of Cyprus located in the four main cities of the island. In this paper the Limassol Castle is used as a focused case study. Three years of MODIS satellite data was evaluated and analyzed in order to categorize high risk long-term areas. Ground measurements using sun-photometers, spectro-radiometers and particulate matter (PM10) laser photometer were also utilized in the study. The darkest pixel atmospheric correction in conjunction with the use of the radiative transfer equation was applied to retrieve the aerosol optical thickness (AOT) from Landsat TM/ETM+ satellite images in order also to cross-validate the AOT values found from MODIS and sun-photometers. The results of the study indicate that air pollution is high in all the major cities in Cyprus near cultural heritage sites. The results also found that the Limassol castle was most affected by air pollution, while the other cultural heritage sites exhibited similar results.

Geoinformation technologies in the process of terrain slope detection in the area of Fruska Gora with reference to potential development of slope processes

This research aims to describe the analysis of geoinformation technologies and systems and its usage in detection of terrain slope with reference to timely detection and mapping sites with a high risk of slope movement and activation of landslides. Special attention is referred to the remote sensing technology and data acquisition. In addition to acquisition, data processing is performed: the production of digital terrain model, calculating of the vegetation index NDVI (Normalized Difference Vegetation Index) based on satellite image and analyses of pedology maps. The procedures of processing the satellite images in order to identify locations of high risk of slope processes are described. Several factors and identifiers are analyzed and used as input values in automatic processing which is performed through a unique algorithm. Research results are presented in raster format. The direction of further research is briefly defined.

Quaternary palaeoenvironments and multi-storey valley fill architecture along the Mezen and Severnaya Dvina river valleys, Arkhangelsk region, NW Russia

The modern Severnaya Dvina and Mezen river systems in the Arkhangelsk region, NW Russia, are located within extensive palaeovalley systems. The palaeovalleys form depressions in bedrock and have controlled the drainage systems in the area at least since the Last Interglacial. Vertically stacked marine to fluvial sediments reflect deposition during fluctuating climate and sea levels.A compilation of lithostratigraphical data collected during the last decade has been coupled with bedrock topography and geomorphology from satellite images in order to describe the valley fill architecture for the two valley systems. Each system has been divided into a number of depositional units (storeys) separated by incision/non-deposition and used to investigate the timing of aggradational versus incisional phases. Time constraints for each phase are provided by optically stimulated luminescence (OSL) ages, and aggradation and incision are linked to independent records of climate and sea level change.The pattern of aggradation and erosion is regional and primarily driven by episodes of increasing and decreasing sediment supply. Aggradation is correlated to times of deglaciation with high sediment supply from the ice margin, release of sediment from ice-dammed lakes and low vegetation and degradation of permafrost on the flood plain. Incision is related to cold intervals with low sediment supply, delayed incision due to isostatic uplift and drainage of ice-dammed lakes. Relative sea level change controls the distribution of marine deposits, which show significant regional variations due to variable isostatic response across the region. Sea level change plays a limited role for fluvial aggradation/incision in the study area.

Spatial and temporal anomalies of sea surface temperature in global scale (by space-based data)

The work presents the data on the spatial distribution of temperature anomalies in the Global Ocean, based on the long-term measurements with the AVHRR satellite equipment. It is noteworthy that such results can be only obtained by using long time series of satellite images. To obtain them by ship-borne methods is actually impossible. The proposed anomaly criterion is equal to a difference between minimum/maximum and average temperature, normed for the mean square deviation. The difference is calculated from the whole time series (with regard to a season). Based on this criterion, anomalies can be distinguished on the background of usual seasonal dynamics at various absolute values of deviations of the measured parameter. Besides, rare, single, fluctuations can be also revealed. Based on the proposed criterion, connected zones have been defined. These zones occur almost in every part of the Global Ocean. The data obtained have been analyzed in association with the CZCS (1978–1986) and SeaWiFS satellite images in order to compare them with the areas of anomalous dynamics of chlorophyll concentration and the regions quasistationary in the seasonal dynamics of chlorophyll. Anomalies in the temperature dynamics are of exceptional importance for the formation of climatic deviations; they affect the ocean–atmosphere interaction and reflect variations in the hydrological structure of the surface waters. The work presents a comparison with the dynamics of chlorophyll concentration distribution determined from the SeaWiFS data for the Atlantic Ocean.

Application of Remote Sensing Data for Oil Spill Monitoring in the Guanabara Bay, Rio De Janeiro, Brazil

ABSTRACT The rupture of a pipeline transporting hydrocarbons at high temperature caused the spill of 1,300 m3 of fuel oil near the Guanabara Bay shoreline, on January 18, 2000. Immediately after the accident, an emergency effort was undertaken to obtain satellite images in order to monitor the spill's location and movement. One Landsat-5/TM (Thematic Mapper) and 15 Radarsat-1 images were acquired This paper describes the results obtained with the interpretation of the first three images acquired after the spill. The low frequency of acquisition and the prevailing cloud cover limited the use of visible and infrared spaceborne sensors. Image processing techniques were applied to highlight oil-covered areas on the water surface. Ambiguities in the oil detection were resolved with the aid of ancillary information such as location of knows sources of pollution; aerial inspection information and GPS controlled aerial photos taken from helicopters. The RADARSAT-1 images show clearly the area affected by the spill accident allowing the differentiation of oil-free water areas, fluvial discharge or effluents areas, and oil-covered water areas. One limitation to oil detection using the Landsat-5/TM image was the presence of clouds and haze near the spilled area. In order to improve the visual discrimination of patterns on the water surface related to oil, many RGB combinations of the seven TM bands were evaluated. A supervised classification procedure was applied resulting in the following classes: oil-free water, fluvial discharge, bay areas affected by haze and oil-covered water. The GIS integration of the classification results with ancillary information, such as location of knows sources of pollution; aerial inspection information and GPS controlled aerial photos acquired simultaneously with image acquisition, allowed the final selection of oil-covered areas. The results obtained helped PETROBRAS to optimize the emergency response procedures and subsequent cleaning efforts.

Range collapse of a tropical cervid (Cervus eldi) and the extent of remaining habitat in central Myanmar

AbstractThe thamin (Cervus eldi) is an endangered species of deer whose present range is greatly reduced from its original distribution covering the deciduous forests throughout south‐east Asia. We mapped the present distribution of thamin using ground surveys and tied this information to habitat types derived from satellite images in order to detect patterns that might indicate the landscape features which precipitated the decline. We conducted a survey of 24 out of 28 Myanmar townships that were reported to contain thamin in 1992, and evidence of thamin were found in 23 of these townships, predominately in mixed deciduous forests where dipterocarp trees were present. There was no significant correlation between the number of thamin detected and forest remaining in the township, or the size of the human or livestock population. A landcover classification of Landsat Thematic Mapper images indicated 58% of the study area contained deciduous forest, of which 12% was dipterocarp forest. Forest tracts containing thamin were digitized and landscape analyses were conducted on a resampled habitat map that emphasized dry and dipterocarp deciduous forest. Of six landscape variables measured only core area size was a significant predictor for the presence of thamin. None of the unsurveyed indaing forest tracts possessed a core area large enough to support thamin. The pattern of thamin decline matches predictions that peripheral, rather than central, populations are more likely to persist in declining species.

Revising Techniques for Estimating Land Parcel Characteristics using Digital Spatial Data

Abstract Characteristics of individual land parcels were estimated for an area in Missouri using two methods: digital spatial data and an accepted system of landowner questionnaires. For the digital data, a geographically imprecise map of land ownership parcels was digitized for a 24210 ac study area. Using a GIS, this was overlaid on a classified digital satellite image in order to estimate parcel characteristics such as mean area, size distribution, and percent of a given ownership covered by forest and/or agriculture. At the same time, a currently used ground-based questionnaire survey which is used for the estimation of the same parameters as well as socioeconomic owner characteristics was conducted. Results from the two methods were comparable for estimating parcel characteristics. The digital approach is less costly and labor-intensive, but does not provide the same socioeconomic data provided by the questionnaire. North. J. Appl. For. 11(3):80-86.

NEW METHOD OF QUANTITY ESTIMATION OF INTRA FIELD VARIABILITY BY OPTICAL CHARACTERISTICS OF SOWINGS FOR PRECISION FARMING

The scientific and technological progress in the agricultural production is based on the application of digital technologies of “smart agriculture” and precision farming, which is its key segment. Precision farming is a global trend of the adaptation of agricultural technologies for crop production to within-field spatial variability of crop formation conditions. It is necessary to quantify the degree of heterogeneity of these conditions and determine the boundaries of their variability in a given agricultural field. Based on the analysis of the available approaches to the within-field variability assessment, the paper substantiates the expediency of searching for new, more precise and inexpensive methods for solving this problem. Due to the large area of Russia, there is no alternative to the technology of remote sensing of the Earth in the information support of precision farming. The proposed new method of within-field heterogeneity detection is based on the use of optical criteria (reflection indexes), characterizing specific and non-specific features of spectral characteristics of crop canopy under the impact of various stress factors. Under controlled conditions, using a special spectrometer, a specific list of reflection indices and a set of quantitative indicators for each criterion was obtained to assess the physiological state of wheat under optimal conditions and nitrogen and water deficiency. The obtained results provide new possibilities for automating the process of interpretation of hyperspectral satellite images in order to identify and reveal the boundaries of the negative effects of stressors that inhibit the growth and development of crops. The relevant algorithmic scheme of the method implementation is considered in the paper.