The use of satellite information (MODIS/Aqua) for phenological and classification analysis of plant communities : научное издание


Тип публикации: статья из журнала

Год издания: 2019

Ключевые слова: Boreal forests and ecosystems, Classification of plant communities, linear discriminant analysis, NDVI (normalized difference vegetation index)

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 Показать полностьюmethods 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.

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Журнал: Forests

Выпуск журнала: Т. 10, 7

Номера страниц: 561

ISSN журнала: 19994907


  • Ivanova Y. (Institute of Biophysics SB RAS,Federal Research Center 'Krasnoyarsk Science Center SB RAS')
  • Kovalev A. (Federal Research Center 'Krasnoyarsk Science Center SB RAS')
  • Yakubailik O. (Institute of Computational Modeling SB RAS,Federal Research Center 'Krasnoyarsk Science Center SB RAS')
  • Soukhovolsky V. (Sukachev Institute of Forest SB RAS,Federal Research Center 'Krasnoyarsk Science Center SB RAS')

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