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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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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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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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