[Текст] : статья / Г. С. Высоцкая, А. П. Шевырногов, М. Ю. Чернецкий // Исследование Земли из космоса. - 2012. - № 6. - С. 77-87
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Шевырногов, А.П.; Чернецкий, М.Ю.; Vysotskaya G.S.
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Multiyear trends of Normalized Difference Vegetation Index and temperature in the south of Krasnoyarsk Krai
/ A. P. Shevyrnogov, M. Y. Chernetskiy, G. S. Vysotskaya // Izv. Atmos. Ocean Phys. - 2013. - Vol. 49, Is. 9. - P. 1047-1056, DOI 10.1134/S0001433813090132
. - ISSN 0001-4338
Кл.слова (ненормированные):
air temperature -- AVHRR -- GIMMS -- MODIS -- NDVI -- northern Eurasia -- radiation temperature of surface -- trends -- Yermakovsk raion
Аннотация: It is found that the nonlinear positive Normalized Difference Vegetation Index (NDVI) trend of mountain taiga in the south of Krasnoyarsk Krai has been negative since 1999-2000. A comparison with the boreal belt of northern Eurasia showed that, though still nonnegative, the positive trend has distinctly decreased to nearly a plateau. A trend analysis of radiation temperature of the surface revealed that the temperature shows a decreasing trend for the selected region of mountainous taiga and an increasing trend for northern Eurasia. We compared air-temperature data from the Yermakovskoe meteorological station to eliminate a preliminary data-processing error. Regression analysis demonstrated a fairly high degree of correlation. Also, we found a quite high degree of correlation between time series and trends of air temperature and NDVI. Trends in air temperature in northern Eurasia according to NCEP-DOE Reanalysis 2 data also confirm the trend found on the basis of satellite data. © 2013 Pleiades Publishing, Ltd.
Scopus,
Полный текст
Доп.точки доступа:
Shevyrnogov, A.P.; Chernetskiy, M.Y.; Vysotskaya, G.S.; Высоцкая, Галина Степановна
Кл.слова (ненормированные):
air temperature -- AVHRR -- GIMMS -- MODIS -- NDVI -- northern Eurasia -- radiation temperature of surface -- trends -- Yermakovsk raion
Аннотация: It is found that the nonlinear positive Normalized Difference Vegetation Index (NDVI) trend of mountain taiga in the south of Krasnoyarsk Krai has been negative since 1999-2000. A comparison with the boreal belt of northern Eurasia showed that, though still nonnegative, the positive trend has distinctly decreased to nearly a plateau. A trend analysis of radiation temperature of the surface revealed that the temperature shows a decreasing trend for the selected region of mountainous taiga and an increasing trend for northern Eurasia. We compared air-temperature data from the Yermakovskoe meteorological station to eliminate a preliminary data-processing error. Regression analysis demonstrated a fairly high degree of correlation. Also, we found a quite high degree of correlation between time series and trends of air temperature and NDVI. Trends in air temperature in northern Eurasia according to NCEP-DOE Reanalysis 2 data also confirm the trend found on the basis of satellite data. © 2013 Pleiades Publishing, Ltd.
Scopus,
Полный текст
Доп.точки доступа:
Shevyrnogov, A.P.; Chernetskiy, M.Y.; Vysotskaya, G.S.; Высоцкая, Галина Степановна
The use of satellite information (MODIS/Aqua) for phenological and classification analysis of plant communities
/ Y. Ivanova [et al.] // Forests. - 2019. - Vol. 10, Is. 7, DOI 10.3390/f10070561
. - ISSN 1999-4907
Кл.слова (ненормированные):
Boreal forests and ecosystems -- Classification of plant communities -- Linear discriminant analysis -- NDVI (normalized difference vegetation index) -- Discriminant analysis -- Radiometers -- Remote sensing -- Time series -- Vegetation -- Average air temperature -- Boreal forests -- Canonical correlations -- Classification analysis -- Linear discriminant analysis -- Normalized difference vegetation index -- Plant communities -- Satellite information -- Forestry
Аннотация: Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003-2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities. © 2019 by the authors.
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Держатели документа:
Institute of Biophysics SB RAS, Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50-50, Krasnoyarsk, 660036, Russian Federation
Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50, Krasnoyarsk, 660036, Russian Federation
Institute of Computational Modeling SB RAS, Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50-44, Krasnoyarsk, 660036, Russian Federation
Sukachev Institute of Forest SB RAS, Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50-28, Krasnoyarsk, 660036, Russian Federation
Доп.точки доступа:
Ivanova, Y.; Kovalev, A.; Yakubailik, O.; Soukhovolsky, V.
Кл.слова (ненормированные):
Boreal forests and ecosystems -- Classification of plant communities -- Linear discriminant analysis -- NDVI (normalized difference vegetation index) -- Discriminant analysis -- Radiometers -- Remote sensing -- Time series -- Vegetation -- Average air temperature -- Boreal forests -- Canonical correlations -- Classification analysis -- Linear discriminant analysis -- Normalized difference vegetation index -- Plant communities -- Satellite information -- Forestry
Аннотация: Vegetation indices derived from remote sensing measurements are commonly used to describe and monitor vegetation. However, the same plant community can have a different NDVI (normalized difference vegetation index) depending on weather conditions, and this complicates classification of plant communities. The present study develops methods of classifying the types of plant communities based on long-term NDVI data (MODIS/Aqua). The number of variables is reduced by introducing two integrated parameters of the NDVI seasonal series, facilitating classification of the meadow, steppe, and forest plant communities in Siberia using linear discriminant analysis. The quality of classification conducted by using the markers characterizing NDVI dynamics during 2003-2017 varies between 94% (forest and steppe) and 68% (meadow and forest). In addition to determining phenological markers, canonical correlations have been calculated between the time series of the proposed markers and the time series of monthly average air temperatures. Based on this, each pixel with a definite plant composition can be characterized by only four values of canonical correlation coefficients over the entire period analyzed. By using canonical correlations between NDVI and weather parameters and employing linear discriminant analysis, one can obtain a highly accurate classification of the study plant communities. © 2019 by the authors.
Scopus,
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Держатели документа:
Institute of Biophysics SB RAS, Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50-50, Krasnoyarsk, 660036, Russian Federation
Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50, Krasnoyarsk, 660036, Russian Federation
Institute of Computational Modeling SB RAS, Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50-44, Krasnoyarsk, 660036, Russian Federation
Sukachev Institute of Forest SB RAS, Federal Research Center 'Krasnoyarsk Science Center SB RAS', Academgorodok 50-28, Krasnoyarsk, 660036, Russian Federation
Доп.точки доступа:
Ivanova, Y.; Kovalev, A.; Yakubailik, O.; Soukhovolsky, V.
Identification of vegetation types and its boundaries using artificial neural networks
/ M. Saltykov, O. Yakubailik, S. Bartsev // IOP Conference Series: Materials Science and Engineering : Institute of Physics Publishing, 2019. - Vol. 537: International Workshop on Advanced Technologies in Material Science, Mechanical and Automation Engineering - MIP: Engineering-2019 (4 April 2019 through 6 April 2019, ) Conference code: 149243, Is. 6, DOI 10.1088/1757-899X/537/6/062001
. -
Кл.слова (ненормированные):
Forestry -- Pixels -- Satellite imagery -- Vegetation -- Boreal forests -- Mixed forests -- Multi-spectral imagery -- Satellite images -- Spectral channels -- Trained neural networks -- Vegetation index -- Vegetation type -- Multilayer neural networks
Аннотация: The applicability of artificial neural networks (ANN) for the identification of vegetation types using satellite multispectral imagery was studied. The study was focused on the three main vegetation types found in the south of the Krasnoyarsk Region: mixed forest, boreal forest and grassland. Sentinel-2 satellite images were used as a data source for the neural networks. It was shown that vegetation type can be identified pixel-by-pixel using 12 spectral channels and simple feed forward ANN with good quality and reliability. Analysis of the input layer of the trained neural networks allowed several spectral bands to be selected that were the most valuable for the ANN decision and not used in the classic NDVI vegetation index. © 2019 IOP Publishing Ltd. All rights reserved.
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Держатели документа:
Institute of Biophysics, FRC KSC SB RAS, Akademgorodok 50/50, Krasnoyarsk, 660036, Russian Federation
Institute of Computation Modeling, FRC KSC SB RAS, Akademgorodok 50/44, Krasnoyarsk, 660036, Russian Federation
Доп.точки доступа:
Saltykov, M.; Yakubailik, O.; Bartsev, S.
Кл.слова (ненормированные):
Forestry -- Pixels -- Satellite imagery -- Vegetation -- Boreal forests -- Mixed forests -- Multi-spectral imagery -- Satellite images -- Spectral channels -- Trained neural networks -- Vegetation index -- Vegetation type -- Multilayer neural networks
Аннотация: The applicability of artificial neural networks (ANN) for the identification of vegetation types using satellite multispectral imagery was studied. The study was focused on the three main vegetation types found in the south of the Krasnoyarsk Region: mixed forest, boreal forest and grassland. Sentinel-2 satellite images were used as a data source for the neural networks. It was shown that vegetation type can be identified pixel-by-pixel using 12 spectral channels and simple feed forward ANN with good quality and reliability. Analysis of the input layer of the trained neural networks allowed several spectral bands to be selected that were the most valuable for the ANN decision and not used in the classic NDVI vegetation index. © 2019 IOP Publishing Ltd. All rights reserved.
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Держатели документа:
Institute of Biophysics, FRC KSC SB RAS, Akademgorodok 50/50, Krasnoyarsk, 660036, Russian Federation
Institute of Computation Modeling, FRC KSC SB RAS, Akademgorodok 50/44, Krasnoyarsk, 660036, Russian Federation
Доп.точки доступа:
Saltykov, M.; Yakubailik, O.; Bartsev, S.
Development of an algorithm for assessing the underlying surface in the areas of felling on heat maps based on remote sensing data
/ A. V. Dergunov [et al.] // International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM : International Multidisciplinary Scientific Geoconference, 2019. - Vol. 19: 19th International Multidisciplinary Scientific Geoconference, SGEM 2019 (30 June 2019 through 6 July 2019, ) Conference code: 150486, Is. 2.2. - P517-524, DOI 10.5593/sgem2019/2.2/S10.063
. -
Кл.слова (ненормированные):
Land surface temperature -- Landsat -- Logging sites -- Thermal map -- Vegetation indices -- Digital storage -- Forestry -- Infrared devices -- Luminance -- Remote sensing -- Restoration -- Satellite imagery -- Thermal logging -- Vegetation -- Anthropogenic disturbance -- Brightness temperatures -- LANDSAT -- Logging sites -- Spectral characteristics -- Thermal maps -- Vegetation index -- Visible and near infrared -- Land surface temperature
Аннотация: Response to anthropogenic disturbances, recorded according to remote sensing of the Earth, has a long-term manifestation not only in the spectral characteristics of the channels of visible and near infrared ranges of satellite imagery, but also in the study of the temperature field. The paper deals with the local excess temperature of the underlying surface in the areas of cutting the territory of the Angara region, compared with the background values identified by satellite data Landsat 5 and 8 for the seventeen-year period. Estimates of the relative difference in the temperature of the underlying surface of the cutting area in comparison with the average background values are obtained. To this end, a number of software products to automate the archiving and conversion of satellite information has been developed. These software products are designed for calculation of the radio-brightness temperature of the underlying surface of disturbed and undisturbed areas of forest vegetation in the pre-selected areas of the territory. They are also used to average the obtained data of radio-brightness temperature and to calculate the difference between the average values of the radio-brightness temperature of the underlying surface of the disturbed areas relative to the undisturbed, that is, the background. This approach can significantly reduce the processing time of a large amount of information and optimize the amount of data storage. Separately, the study area was analyzed according to the NDVI vegetation index. The data obtained demonstrate a high rate of recovery of grass cover and grass-shrub layer immediately after damage to forest vegetation. It is shown that during the considered period of time (17 years) the value of excess temperature decreases, which is determined by the processes of vegetation restoration, including stand on felling. It is established that the increased temperatures of the underlying surface in the place of cuttings are remained for at least 15 years, and the temperature increase over the background values in the conditions of the observed successional processes is not less than 10%. As a limiting factor in the restoration of the temperature background of the underlying surface, fires can act. © SGEM2019. All Rights Reserved.
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Держатели документа:
Federal Research Center Krasnoyarsk Science Center of the SB RAS, Krasnoyarsk, Russian Federation
Institute of Computational Modelling SB RAS, Krasnoyarsk, Russian Federation
Sukachev Institute of Forest SB RAS, Krasnoyarsk, Russian Federation
Siberian Federal University, Krasnoyarsk, Russian Federation
Доп.точки доступа:
Dergunov, A. V.; Krasnoshchokov, K. V.; Ponomarev, E. I.; Yakubailik, O. E.
Кл.слова (ненормированные):
Land surface temperature -- Landsat -- Logging sites -- Thermal map -- Vegetation indices -- Digital storage -- Forestry -- Infrared devices -- Luminance -- Remote sensing -- Restoration -- Satellite imagery -- Thermal logging -- Vegetation -- Anthropogenic disturbance -- Brightness temperatures -- LANDSAT -- Logging sites -- Spectral characteristics -- Thermal maps -- Vegetation index -- Visible and near infrared -- Land surface temperature
Аннотация: Response to anthropogenic disturbances, recorded according to remote sensing of the Earth, has a long-term manifestation not only in the spectral characteristics of the channels of visible and near infrared ranges of satellite imagery, but also in the study of the temperature field. The paper deals with the local excess temperature of the underlying surface in the areas of cutting the territory of the Angara region, compared with the background values identified by satellite data Landsat 5 and 8 for the seventeen-year period. Estimates of the relative difference in the temperature of the underlying surface of the cutting area in comparison with the average background values are obtained. To this end, a number of software products to automate the archiving and conversion of satellite information has been developed. These software products are designed for calculation of the radio-brightness temperature of the underlying surface of disturbed and undisturbed areas of forest vegetation in the pre-selected areas of the territory. They are also used to average the obtained data of radio-brightness temperature and to calculate the difference between the average values of the radio-brightness temperature of the underlying surface of the disturbed areas relative to the undisturbed, that is, the background. This approach can significantly reduce the processing time of a large amount of information and optimize the amount of data storage. Separately, the study area was analyzed according to the NDVI vegetation index. The data obtained demonstrate a high rate of recovery of grass cover and grass-shrub layer immediately after damage to forest vegetation. It is shown that during the considered period of time (17 years) the value of excess temperature decreases, which is determined by the processes of vegetation restoration, including stand on felling. It is established that the increased temperatures of the underlying surface in the place of cuttings are remained for at least 15 years, and the temperature increase over the background values in the conditions of the observed successional processes is not less than 10%. As a limiting factor in the restoration of the temperature background of the underlying surface, fires can act. © SGEM2019. All Rights Reserved.
Scopus,
Смотреть статью
Держатели документа:
Federal Research Center Krasnoyarsk Science Center of the SB RAS, Krasnoyarsk, Russian Federation
Institute of Computational Modelling SB RAS, Krasnoyarsk, Russian Federation
Sukachev Institute of Forest SB RAS, Krasnoyarsk, Russian Federation
Siberian Federal University, Krasnoyarsk, Russian Federation
Доп.точки доступа:
Dergunov, A. V.; Krasnoshchokov, K. V.; Ponomarev, E. I.; Yakubailik, O. E.