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A remote sensing technique for the assessment of stable interannual dynamical patterns of vegetation / M. Y. Chernetskiy, A. P. Shevyrnogov, N. F. Ovchinnikova // Proceedings of SPIE - The International Society for Optical Engineering. - 2011. - Vol. 8174: Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII (19 September 2011 through 21 September 2011, Prague) Conference code: 87191. - Ст. 1, DOI 10.1117/12.896748
Кл.слова (ненормированные):
EVI -- Forest -- Kernel k-means -- Minimum noise fraction -- MODIS -- NDVI -- Principal component analysis -- Time series -- EVI -- Forest -- Kernel k-means -- Minimum noise fraction -- MODIS -- NDVI -- Principal Components -- Agriculture -- Ecosystems -- Hydrology -- Principal component analysis -- Remote sensing -- Satellite imagery -- Space optics -- Time series -- Vegetation
Аннотация: The time series of various parameters of satellite imagery (NDVI/EVI, temperature) during the growing season were considered in this work. This means that satellite images were considered not like a number of single scenes but like temporal sequences. Using time series enables estimating the integral phenological properties of vegetation. The basis of the developed technique is to use one of the methods of transformation of the multidimensional space in order to get the principal components. The technique is based on considering each dimension of the multidimensional space as satellite imagery for a specific date range. The technique automatically identifies spatial patterns of vegetation that are similar by phenology and growing conditions. Subsequent analysis allowed identification of the belonging of derived classes. Thus, the technique of revealing the spatial distribution of different dynamical vegetation patterns based on the phenological characteristics has been developed. The technique is based on a transformation of the multidimensional space of states of vegetation. Based on the developed technique, areas were obtained with similar interannual trends. В© 2011 SPIE.

Scopus
Держатели документа:
Institute of Biophysics of SB RAS, Krasnoyarsk 660036, Akademgorodok, Russian Federation
V.N. Sukachev Institute of Forest of SB RAS, Krasnoyarsk 660036, Akademgorodok, Russian Federation
Siberian Federal University, Kyrensky st., 26, Krasnoyarsk, 660074, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Chernetskiy, M.Y.; Shevyrnogov, A.P.; Ovchinnikova, N.F.

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A study of forest vegetation dynamics in the south of the Krasnoyarskii Krai in spring / M. Chernetskiy [et al.] // Advances in Space Research. - 2011. - Vol. 48, Is. 5. - P819-825, DOI 10.1016/j.asr.2011.04.032 . - ISSN 0273-1177
Кл.слова (ненормированные):
EVI -- Forestry -- MODIS -- NDVI -- Remote sensing -- Vegetation phenology -- Accurate measurement -- Annual time series -- Carbon exchange -- Data series -- Dynamic state -- Enhanced vegetation index -- EVI -- Forest vegetation -- Global scale -- Growth dynamics -- Interannual variability -- Moderate resolution imaging spectroradiometer -- MODIS -- NDVI -- Normalized difference vegetation index -- Principal components analysis -- Remote sensing applications -- Remote sensing data -- Satellite data -- Spatial structure -- Spring season -- Terrestrial ecosystems -- Vegetation dynamics -- Vegetation phenology -- Biology -- Climate models -- Dynamics -- Ecosystems -- Estimation -- Forestry -- Monitoring -- Principal component analysis -- Radiometers -- Remote sensing -- Satellite imagery -- Timber -- Time series -- User interfaces -- Vegetation -- Carbon -- Ecosystems -- Forests -- Image Analysis -- Plants -- Remote Sensing -- Time Series Analysis
Аннотация: Remote sensing applications have greatly enhanced ability to monitor and manage in the areas of forestry. Accurate measurements of regional and global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate-biosphere interactions. Study of vegetation phenology is required for understanding of variability in ecosystem. In this paper, monitoring of vegetation dynamics using time series of satellite data is presented. Vegetation variability (vegetation rate) in different topoclimatic areas is investigated. Original software using IDL interactive language for processing of satellite long-term data series was developed. To investigate growth dynamics vegetation rate inferred from remote sensing was used. All estimations based on annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Vegetation rate for Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) was calculated using MODIS data. The time series covers spring seasons of each of 9 years, from 2000 to 2008. Comparison of EVI and NDVI derived growth rates has shown that NDVI derived rates reveal spatial structure better. Using long-term data of vegetation rates variance was estimated that helps to reveal areas with anomalous growth rate. Such estimation shows sensitivity degree of different areas to different topoclimatic conditions. Woods of heights depend on spatial topoclimatic variability unlike woods of lowlands. Principal components analysis shows vegetation with different rate conditions. Also it reveals vegetation of same type in areas with different conditions. It was demonstrated that using of methods for estimating the dynamic state of vegetation based on remote sensing data enables successful monitoring of vegetation phenology. В© 2011 COSPAR. Published by Elsevier Ltd. All rights reserved.

Scopus
Держатели документа:
Institute of Biophysics of SB RAS, Akademgorodok 50/50, Krasnoyarsk 660036, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Chernetskiy, M.; Pasko, I.; Shevyrnogov, A.; Slyusar, N.; Khodyayev, A.

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Multi-satellite data merge to combine NOAA AVHRR efficiency with landsat-6 MSS spatial resolution to study vegetation dynamics / A. Shevyrnogov, P. Trefois, G. Vysotskaya // Advances in Space Research. - 2000. - Vol. 26, Is. 7. - P1131-1133, DOI 10.1016/S0273-1177(99)01130-8 . - ISSN 0273-1177
Кл.слова (ненормированные):
AVHRR -- Landsat multispectral scanner -- NOAA satellite -- satellite data -- spatial resolution -- vegetation dynamics
Аннотация: Spectral range and good replication provided by NOAA AVHRR data make possible to study evolution of vegetation cover and temperature in time. However, the spatial resolution even of 1000 m does not allow accurate location of the changes. The new developed NOAA NDVI image provides better apparent spatial resolution by the following procedure: - resample the NOAA image to 100 m pixel size; - georeference both NOAA and MSS (Landsat-6) to common UTM coordinates; - extract the brightness component from MSS (using the first principal component); - merge NOAA NDVI and MSS brightness images. The resulting image features the color of NOAA NDVI and topographic details of MSS intensity. This work offers relevant techniques to extract information from satellite imagery to apply to plant dynamics investigation. (C) 2000 COSPAR. Published by Elsevier Science Ltd.

Scopus
Держатели документа:
Institute of Biophysics of SB RAS, 660036, Krasnoyarsk, Russian Federation
Royal Museum of Central Africa, Brussels, Belgium
Inst. of Compl. Modelling SB RAS, 660036, Krasnoyarsk, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Shevyrnogov, A.; Trefois, P.; Vysotskaya, G.

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The analysis of seasonal activity of photosynthesis and efficiency of various vegetative communities on a basis NDVI for modeling of biosphere processes / Yu. D. Ivanova [et al.] // Advances in Space Research. - 2007. - Vol. 39, Is. 1. - P95-99, DOI 10.1016/j.asr.2006.02.028 . - ISSN 0273-1177
Кл.слова (ненормированные):
NDVI -- Plant communities -- Synoptic parameters -- Biomass -- Climatology -- Parameter estimation -- Photosynthesis -- Plants (botany) -- Statistical methods -- Time series analysis -- NDVI -- Plant communities -- Seasonal activity -- Synoptic parameters -- Vegetation
Аннотация: NDVI (Normalized Difference Vegetation Index) is proposed as an area-dependent climatic variable, which reflects climatically significant events and processes. NDVI is taken as a simple quantitative indicator of the amount of photosynthetically active biomass. Mean values of NDVI have been calculated for the period between 1996 and 2001. NDVI time series have been analyzed in conjunction with meaningful synoptic parameters that influence the behavior of plants in different plant communities of Eastern Siberia (tundra, taiga, and steppe). Based on GIS technologies, statistical tests have been carried out and correlations between the study parameters have been found. В© 2007.

Scopus
Держатели документа:
Institute of Biophysics, Russian Academy of Sciences, Siberian Branch, Akademgorodok, Krasnoyarsk, 660036, Russian Federation
Krasnoyarsk State Technical University, Kirensky 26, Krasnoyarsk, 660074, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Ivanova, Yu.D.; Bartsev, S.I.; Pochekutov, A.A.; Kartushinsky, A.V.

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Multi-satellite data merge to combine NOAA AVHRR efficiency with Landsat-6 MSS spatial resolution to study vegetation dynamics [Text] / A. . Shevyrnogov, P. . Trefois, G. . Vysotskaya ; ed. , P Trefo // REMOTE SENSING FOR LAND SURFACE CHARACTERISATION. Ser. ADVANCES IN SPACE RESEARCH : PERGAMON PRESS LTD, 2000. - Vol. 26: A3 1 and A3 2 Symposia of COSPAR Scientific Commission A held at the 32nd COSPAR Scientific Assembly (JUL 12-19, 1998, NAGOYA, JAPAN), Is. 7. - P. 1131-1133, DOI 10.1016/S0273-1177(99)01130-8. - Cited References: 2 . - ISBN 0273-1177

РУБ Engineering, Aerospace + Remote Sensing

Аннотация: Spectral range and good replication provided by NOAA AVHRR data make possible to study evolution of vegetation cover and temperature in time. However, the spatial resolution even of 1000 m does not allow accurate location of the changes. The new developed NOAA NDVI image provides better apparent spatial resolution by the following procedure: - resample the NOAA image to 100 m pixel size; - georeference both NOAA and MSS (Landsat-6) to common UTM coordinates; - extract the brightness component from MSS (using the first principal component); - merge NOAA. NDVI and MSS brightness images. The resulting image features the color of NOAA NDVI and topographic details of MSS intensity. This work offers relevant techniques to extract information from satellite imagery to apply to plant dynamics investigation. (C) 2000 COSPAR. Published by Elsevier Science Ltd.

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Держатели документа:
RAS, SB, Inst Biophys, Krasnoyarsk 660036, Russia
Royal Museum Cent Africa, Brussels, Belgium
RAS, SB, Inst Computat Modelling, Krasnoyarsk 660036, Russia
ИБФ СО РАН
ИВМ СО РАН : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Shevyrnogov, A...; Trefois, P...; Vysotskaya, G...; Trefo, , P \ed.\

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The influence of altitudinal zonality on spectral characteristics (MODIS/Terra) mountain forests of Western Sayan / N. Kukoba, Y. Ivanova, I. Botvich // Climate Change Impacts on High-Altitude Ecosystems . - 2015. - P637-643, DOI 10.1007/978-3-319-12859-7_26 . - ISBN 9783319128597 (ISBN); 9783319128580 (ISBN)
Кл.слова (ненормированные):
Mountain forests of Siberia -- Net primary production (NPP) -- Vegetation indices
Аннотация: The purpose of this study is to find a relationship between changes in spectral characteristics (MODIS/Terra) of mountain forests and the altitude at which they grow. In the study area, which is located in the West Sayan Mountains (in South Siberia), the types of forest ecosystems change markedly with altitude. The study uses the data of the MODIS-NPP model intended for the evaluation of global net production. Results of the study show that the best approach to dividing mountain forests into different types is to use 8-day composites of satellite data collected at the beginning of the growing season (April-May). This is the time when the most significant differences are recorded between vegetation indices, including Normalized Difference Vegetation Index (NDVI), LAI, and EVI of the mountain forests growing in different altitudinal zones. © Springer International Publishing Switzerland 2015.

Scopus
Держатели документа:
Siberian Federal University, Krasnoyarsk, Russian Federation
Institute of Biophysics SB RAS, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Kukoba, N.; Ivanova, Y.; Botvich, I.

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Evaluation of the seasonal dynamics of crop yield in agrocenoses on the basis of satellite data and mathematical models / T. I. Pis’man, I. Y. Botvich, A. F. Sid’ko // Biol. Bull. - 2015. - Vol. 42, Is. 6. - P589-594, DOI 10.1134/S1062359015660048 . - ISSN 1062-3590
Аннотация: An integrated approach based on satellite remote-sensing data and the results of analysis of mathematical models has been tested for applicability in the evaluation of crop yield and total phytomass of agrocenosis, as well as identifying its type. The dynamics of the normalized difference vegetation index (NDVI) and the total aboveground phytomass of agrocenosis proved to be qualitatively similar. An analysis performed using a mathematical model and taking into account air temperature showed the possibility of making and refining the prognosis of crop yield. In this course, the vegetative and generative parts of the agrocenosis were distinguished, and it was found that model data matched ground survey data under optimal environmental conditions. © 2015, Pleiades Publishing, Inc.

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Держатели документа:
Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Akademgorodok 50/50, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Pis’man, T. I.; Botvich, I. Y.; Sid’ko, A. F.

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Simulation of phenodynamics of deciduous tree species / V. G. Sukhovol’skii [и др.] // Russ. J. For. Sci. - 2017. - Is. 4. - С. 293-302, DOI 10.7868/S0024114817040052 . - ISSN 0024-1148
Кл.слова (ненормированные):
Boreal forests -- Climate -- Phenological models -- Satellite data
Аннотация: Here we show the new approach to analysis of seasonal phenological dynamics of wooded plants. Our model was premised on representation of phenological processes of trees in boreal domain by a process of energy production during the vegetation season and partial release of accumulated energy for survival during resting. We introduced the energy balance equation combining weather and phenological indicators and linking them throughout a year. The model was parameterized using data of phenological studies of birch, aspen, and Siberian larch in Stolby Nature Sanctuary (55°38? - 55°58? N, 92°38? - 93°05? E) during 1951-2012 which allow high accuracy simulation of phenological stages. Coefficients of the equation may be considered as indicators of sensitivity of wooded plants to climate. We show that remotely sensed data on phenological dates may be used in calculations using the model of energy balance. Daily MODIS/Terra images of NDVI (Normalized Difference Vegetation Index) of the studied deciduous forests were used. © 2017, Izdatel'stvo Nauka. All rights reserved.

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Держатели документа:
Forest Institute, Siberian Branch of the Russian Academy of Sciences, Academgorodok 50 bldg. 28, Krasnoyarsk, Russian Federation
Institute of Biophysics, Siberian Branch of the Russian Academy of Sciences, Aсademgorodok 50 bldg. 50, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Sukhovol’skii, V. G.; Ivanova, Y. D.; Ovchinnikova, T. M.; Botvich, I. Y.

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    Assessment of the state of forest vegetation in Krasnoyarsk Territory (Stolby Nature Reserve) according to satellite data / T. I. Pisman, I. Y. Botvich, A. P. Shevyrnogov // Sovrem. Probl. Distancionnogo Zondirovania Zemli kosm. - 2018. - Vol. 15, Is. 5. - С. 169-178, DOI 10.21046/2070-7401-2018-15-5-130-140 . - ISSN 2070-7401
   Перевод заглавия: Оценка состояния лесной растительности красноярского края (заповедник «Столбы») по спутниковым данным
Кл.слова (ненормированные):
Anthropogenic factors -- Climate -- Coniferous and deciduous vegetation -- Modis -- NDVI trends -- Satellite sounding -- Stolby Nature Reserve
Аннотация: The variability of the state of forest vegetation was studied basing on the analysis of NDVI time series (2003–2016) of coniferous and deciduous stands and climate in Krasnoyarsk Territory (Stolby Nature Reserve). The initial data were 8-day Modis satellite information (MOD09Q1 product) and meteorological information from terrestrial weather stations. It was revealed that the trends of the averaged NDVI of forest vegetation for the period May – September and the maximum NDVI were negative. An analysis of the relationship between the dynamics of NDVI forest vegetation and the hydrothermal factor on the territory of the reserve in the 14-year cycle revealed an insignificant correlation between these variables. The negative NDVI trends of coniferous and deciduous stands indicate degradation processes. Deterioration of the state of forest vegetation in the study area detected by satellite data is explained by a combination of factors: climate change, anthropogenic impact of Krasnoyarsk and presence of old-aged forest. © 2018 Space Research Institute of the Russian Academy of Sciences. All rights reserved.

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Держатели документа:
Institute of Biophysics SB RAS, Krasnoyarsk, 660036, Russian Federation

Доп.точки доступа:
Pisman, T. I.; Botvich, I. Y.; Shevyrnogov, A. P.

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10.

The Restoration Dynamics of Fallow Vegetation in the Steppe Zone of the Khakassia Republic Based on Terrain and Satellite Data / I. Y. Botvich, T. M. Zorkina // Biophysics. - 2019. - Vol. 64, Is. 2. - P309-315, DOI 10.1134/S0006350919020039 . - ISSN 0006-3509
Кл.слова (ненормированные):
fallow lands -- long-term variability (structure -- MODIS -- NDVI -- phytomass) -- projective cover -- restoration of natural vegetation -- satellite and terrain research methods
Аннотация: Abstract: The dynamics and specific features of the restoration of forbs–grass–wormwood and wormwood–grass phytocoenoses on fallow lands in the Altai region, the Republic of Khakassia, were determined on the basis of terrain and satellite data. The species composition, structure, and phytomass of the phytocoenoses were revealed. A gradual formation of structural elements of steppe communities in the studied areas was determined. This work showed the usefulness of time series of satellite data on the NDVI (Normalized Difference Vegetation Index) obtained with the use of MODIS (Moderate Resolution Imaging Spectroradiometer) for the study of specific features of restored fallows. In general the biological parameters, projective cover, and phytomass determine the value of the NDVI. Interannual NDVI variability reflects the rate and time period of fallow restoration. From a certain point, the parameters increased and became close to the steppe (control variant). It has been revealed that not only abiotic factors (climate and soils), but also biotic parameters (grazing and recreational load) affect the NDVI. In this connection, the duration of restoration stages does not always correspond to the published data. They vary under different conditions. Climatic data of the Abakan meteorological station (index 29862 in the network of the World Meteorological Organization) for the period from 2000 to 2017 were statistically treated. The long-term annual average norms of temperatures and precipitation amounts (year and month) for the World Meteorological Organization base period of 1961–1990 were calculated. The dynamics of the temperature and precipitation, using long-term series of data, has been analyzed. © 2019, Pleiades Publishing, Inc.

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Держатели документа:
Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Division of Federal Research Center Krasnoyarsk Scientific Center, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, 660036, Russian Federation
Cherepnin Herbarium, Astaf’ev Krasnoyarsk State Pedagogical University, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Botvich, I. Y.; Zorkina, T. M.

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11.

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.

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12.

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.

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13.

Estimation of the spatial distribution of spring barley yield using ground-based and satellite spectrophotometric data / A. P. Shevyrnogov [et al.] // IOP Conference Series: Earth and Environmental Science : Institute of Physics Publishing, 2019. - Vol. 315: International Scientific Conference on Agribusiness, Environmental Engineering and Biotechnologies, AGRITECH 2019 (20 June 2019 through 22 June 2019, ) Conference code: 152072, Is. 3. - Ст. 032023, DOI 10.1088/1755-1315/315/3/032023
Кл.слова (ненормированные):
Biotechnology -- Environmental technology -- Photomapping -- Seed -- Crop development -- Field experience -- Optical characteristics -- Precision agriculture technology -- Resource-saving technologies -- Spatial resolution -- Spring barley yields -- Vegetation index -- Spatial distribution
Аннотация: The article presents a method for estimating the spatial distribution of spring barley yield, based on the use of optical ground and satellite spectral data (PlanetScope data with a spatial resolution of 3 meters). This approach is highly relevant for the development of precision agriculture technologies. Yield mapping is carried out on the basis of data on the spatial distribution of the actual yield and the spatial distribution of the spectral optical characteristics. The method's characteristic feature is the use of the integral values of vegetation indices (NDVI, MSAVI2, ClGreen) at various stages of crop development. The method was tested on the basis of stationary field experience, where traditional agriculture (deep plowing) is compared with resource-saving technologies (subsurface and surface plowing, and direct seeding with zero tillage). © 2019 IOP Publishing Ltd. All rights reserved.

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Держатели документа:
Institute of Biophysics SB RAS, Federal Research Center, Krasnoyarsk Science Center, SB RAS, Krasnoyarsk, Akademgorodok, 660036, Russian Federation
Krasnoyarsk State Agrarian University, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Shevyrnogov, A. P.; Yu Botvich, I.; Yemelianov, D. V.; Larko, A. A.; Ivchenko, V. K.; Demianenko, T. N.

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14.

The Information Content of Spectral Vegetation Indices in the Interpretation of Satellite Images of Cultivated Fields / T. I. Pisman [et al.] // Biophysics. - 2019. - Vol. 64, Is. 4. - P588-592, DOI 10.1134/S0006350919040158 . - ISSN 0006-3509
Кл.слова (ненормированные):
bare fallows -- Keywords: sod fields -- NDSI -- NDVI -- Sentinel-2
Аннотация: Abstract—The results of satellite monitoring of vegetation on unused agricultural lands during the growing season of 2018 are presented. Sod fields of different ages (2, 7, and 20 years) and bare fallows on the land used by the Krasnoyarsk Research Institute of Agriculture were the objects of the study. Satellite data with high spatial resolution (Sentinel-2 Earth remote sensing satellites) at the pre-processing Level-1C (https://earthexplorer.usgs.gov/) were used for the interpretation of sod field and fallow images. These data were used to calculate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Soil Index (NDSI). Algorithms and software for the processing of Sentinel-2 satellite data were developed. The possibility of using NDVI dynamics for assessment and monitoring of the condition of sod fields and bare fallows has been demonstrated. The applicability of the NDSI soil index for assessment of the status of arable land has been demonstrated. © 2019, Pleiades Publishing, Inc.

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Держатели документа:
Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Akademgorodok, 50/50, Krasnoyarsk, 660036, Russian Federation
Agricultural Research Institute, Svobodnyi pr., 66, Krasnoyarsk, 660041, Russian Federation

Доп.точки доступа:
Pisman, T. I.; Shevyrnogov, A. P.; Larko, A. A.; Botvich, I. Y.; Emelyanov, D. V.; Shpedt, A. A.; Trubnikov, Y. N.

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15.

Estimation of the spatial distribution of spring barley yield (Krasnoyarsk Territory) from ground and satellite spectrophotometric data / I. Yu. Botvich [и др.] // Sovrem. Probl. Distancionnogo Zondirovania Zemli kosm. - 2019. - Vol. 16, Is. 5. - С. 183-193, DOI 10.21046/2070-7401-2019-16-5-183-193 . - ISSN 2070-7401
Кл.слова (ненормированные):
Barley -- Crop yield -- Growing season -- PlanetScope -- Precision farming -- Spectroradiometer -- Types of tillage
Аннотация: The paper presents a method for estimating the spatial distribution of spring barley yield, implemented based on the use of optical ground and satellite spectral data (PlanetScope with a spatial resolution of 3 meters). This approach is highly relevant for the development of precision farming technologies. Yield mapping is carried out on the basis of the data on spatial distribution of actual yield and spatial distribution of spectral optical characteristics. A feature of the method is the use of the integral values of vegetation indices (NDVI, MSAVI2, ClGreen) at various stages of crop development. Testing of the method was performed on the basis of stationary field experience, when traditional agriculture (deep plowing) was compared with resource-saving technologies (flat-cut, surface treatments and direct seeding at zero tillage). As a result, a method for estimating the spatial distribution of spring barley yield, implemented using optical ground and satellite spectral data (PlanetScope with a spatial resolution of 3 meters) was developed. The prediction of barley yields at the end of July on the basis of a linear regression model was performed, the values of the integral under the NDVI curve in different periods of time were used as parameters. The type of a multiple linear model for predicting barley with 7 variables was established (the coefficient of determination is 0.73; the root-mean-square error is 1.5). The spatial distribution of barley yield by satellite (PlanetScope) and ground data was mapped. The resulting yield maps will be used when planning work for the next year. © 2019 Space Research Institute of the Russian Academy of Sciences. All rights reserved.

Scopus,
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Держатели документа:
Institute of Biophysics SB RAS, Krasnoyarsk, 660036, Russian Federation
Krasnoyarsk State Agrarian University, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Botvich, I. Yu.; Emelyanov, D. V.; Larko, A. A.; Malchikov, N. O.; Ivchenko, V. K.; Demyanenko, T. N.; Shevyrnogov, A. P.

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16.

Use of unmanned aerial vehicles for sensing microrelief during agrocenoses monitoring / A. P. Shevyrnogov, N. O. Malchikov, I. Y. Botvich [et al.] // CEUR Workshop Proceedings : CEUR-WS, 2019. - Vol. 2534: 2019 All-Russian Conference ""Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes"", SDM 2019 (26 August 2019 through 30 August 2019, ) Conference code: 156641. - P485-490
Кл.слова (ненормированные):
Agriculture -- Barley -- Precision farming -- Spectrometry -- UAV -- Vegetation index -- Agriculture -- Antennas -- Data handling -- Spectrometry -- Unmanned aerial vehicles (UAV) -- Vegetation -- Agricultural land -- Barley -- Ground based -- Integral values -- Microrelief -- Precision farming -- Vegetation index -- Vegetation periods -- Monitoring
Аннотация: The use of UAVs for obtaining data on field microrelief within agricultural lands was demonstrated. The work relies on the NDVI integral values during the vegetation period obtained using satellite and ground-based spectrometry. It was established that a change in microrelief has a significant impact on the value of the NDVI integral and yield. The proposed approach can be used in precision farming for planning future agricultural work. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Scopus
Держатели документа:
Institute of Biophysics SB RAS, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Shevyrnogov, A. P.; Malchikov, N. O.; Botvich, I. Y.; Yemelianov, D. V.; Larko, A. A.

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17.

Seasonal Dynamics of Vegetation on Fallow Lands in Krasnoyarsk Forest Steppe According to Terrain and Satellite Data / A. P. Shevyrnogov, T. I. Pisman, N. A. Kononova [et al.] // Izv. Atmos. Ocean. Phys. - 2019. - Vol. 55, Is. 9. - P1353-1361, DOI 10.1134/S0001433819090470. - Cited References:29. - This study was performed according to the Complex Program of Fundamental Research of the Siberian Branch of the Russian Academy of Sciences Interdisciplinary Integration Researches for 2018-2020 (project no. 74) and State Task registration no. AAAA-A17-117013050027-1. . - ISSN 0001-4338. - ISSN 1555-628X

РУБ Meteorology & Atmospheric Sciences + Oceanography
Рубрики:
MODIS
Кл.слова (ненормированные):
fallow lands -- vegetation indices -- terrain spectophotometry -- MODIS -- geobotanical researches -- Krasnoyarsk krai
Аннотация: This article presents investigation data on the seasonal dynamics of productivity, status, and species composition of vegetation on fallow lands in the Krasnoyarsk forest steppe (Middle Siberia) obtained from terrain and satellite materials from 2017. The results of the study of grass plant communities on the basis of geobotanical descriptions and terrain spectrometry were have been used for a more accurate interpretation of cosmic photographs of moderate and low resolution. For studying vegetation on fallow lands, we analyze the seasonal dynamics of various vegetative indices (NDVI, EVI, LSWI, and LAI) and parameters (NPP, FPAR, and LST (land surface temperature)) obtained from MODIS satellite images. Our analysis of satellite data shows the absence of evidences of plowing and mowing in the studied area. A positive correlation is revealed between vegetation indices of biomass (NDVI, EVI, LAI, and NPP) and parameters of hydrothermal conditions (LSWI, FPAR, and LST).

WOS
Держатели документа:
Russian Acad Sci, Siberian Branch, Inst Biophys, Krasnoyarsk, Russia.

Доп.точки доступа:
Shevyrnogov, A. P.; Pisman, T. I.; Kononova, N. A.; Botvich, I. Yu.; Larko, A. A.; Vysotskaya, G. S.; Kononova, Natalia; Complex Program of Fundamental Research of the Siberian Branch of the Russian Academy of Sciences [74, AAAA-A17-117013050027-1]

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18.

Identification of vegetation types and its boundaries using artificial neural networks / M. Saltykov, O. Yakubailik, S. Bartsev // INTERNATIONAL WORKSHOP ADVANCED TECHNOLOGIES IN MATERIAL SCIENCE, : IOP PUBLISHING LTD, 2019. - Vol. 537: International Workshop on Advanced Technologies in Material Science, (APR 04-06, 2019, Krasnoyarsk, RUSSIA). - Ст. 062001. - (IOP Conference Series-Materials Science and Engineering), DOI 10.1088/1757-899X/537/6/062001. - Cited References:8. - The reported study was funded by RFBR and the Russian Geographical Society according to the research project No 17-05-41012. . -

РУБ Engineering, Mechanical + Materials Science, Multidisciplinary

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.

WOS
Держатели документа:
RAS, Inst Biophys, SB, FRC KSC, Akademgorodok 50-50, Krasnoyarsk 660036, Russia.
RAS, Inst Computat Modeling, SB, FRC KSC, Akademgorodok 50-44, Krasnoyarsk 660036, Russia.

Доп.точки доступа:
Saltykov, M.; Yakubailik, O.; Bartsev, S.; Yakubailik, Oleg; RFBRRussian Foundation for Basic Research (RFBR); Russian Geographical Society [17-05-41012]

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19.

Long-Term Dynamics of NDVI-Vegetation for Different Classes of Tundra Depending on the Temperature and Precipitation / A. G. Degermendzhi, G. S. Vysotskaya, L. A. Somova [et al.] // Dokl. Earth Sci. - 2020. - Vol. 493, Is. 2. - P658-660, DOI 10.1134/S1028334X20080048. - Cited References:10 . - ISSN 1028-334X. - ISSN 1531-8354

РУБ Geosciences, Multidisciplinary

Кл.слова (ненормированные):
tundra -- Holdridge -- vegetation -- biotemperature -- precipitation -- NDVI
Аннотация: The tundra was divided into different classes depending on the temperature and precipitation in accordance with the Holdridge classification. Dry, moist, wet, and rainy tundras were distinguished. Datasets on climate variability were obtained from the Climatic Research Unit website () for the period from 2001 to 2017. The long-term (2001-2016) dynamics of phytomass for different tundra classes was studied on the basis of the Normalized Differential Vegetation Index (NDVI). The positive long-term dynamics of NDVI-vegetation for the tundra classes studied was revealed. This trend correlates with the positive dynamics of the mean annual biotemperature. It was shown that the impact of global climate change on vegetation of different tundra classes is ambiguous. For the dry tundra, the increase in NDVI in May and June was higher than for the rainy tundra. This correlates with the fact that the increase in the mean monthly temperatures in May and June on the territory of the dry tundra is greater than on the territory of the rainy tundra.

WOS
Держатели документа:
Russian Acad Sci, Inst Biophys, Siberian Branch, Krasnoyarsk 660036, Russia.

Доп.точки доступа:
Degermendzhi, A. G.; Vysotskaya, G. S.; Somova, L. A.; Pisman, T. I.; Shevyrnogov, A. P.

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20.

Modeling the radial stem growth of the pine (Pinus sylvestris l.) forests using the satellite-derived ndvi and lst (modis/aqua) data / Y. Ivanova, A. Kovalev, V. Soukhovolsky // Atmosphere. - 2021. - Vol. 12, Is. 1. - Ст. 12. - P1-15, DOI 10.3390/atmos12010012 . - ISSN 2073-4433
Кл.слова (ненормированные):
Boreal forests -- Field measurements -- Modeling studies of forest -- Remote sensing data analysis -- Tree and forest functioning -- Tree ring width -- Land surface temperature -- Radiometers -- Satellites -- Time series analysis -- Developed model -- Growing season -- Measurements of -- Normalized difference vegetation index -- Parabolic approximation -- Pinus sylvestris -- Principal Components -- Tree-ring width -- Forestry -- Aqua (satellite) -- boreal forest -- coniferous tree -- growth rate -- land surface -- modeling -- MODIS -- NDVI -- phytomass -- principal component analysis -- remote sensing -- satellite data -- surface temperature -- tree ring -- Pinus sylvestris
Аннотация: The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Scopus
Держатели документа:
Institute of Biophysics SB RAS, Federal Research Center “Krasnoyarsk Science Center SB RAS”, Krasnoyarsk, 660036, Russian Federation
Federal Research Center “Krasnoyarsk Science Center SB RAS”, Krasnoyarsk, 660036, Russian Federation
Sukachev Institute of Forest SB RAS, Federal Research Center “Krasnoyarsk Science Center SB RAS”, Krasnoyarsk, 660036, Russian Federation

Доп.точки доступа:
Ivanova, Y.; Kovalev, A.; Soukhovolsky, V.

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