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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 5, pp. 125-135

Pícea ábies stress levels classification by spectral features of remote sensing data

V.A. Siliuk 1 , H.S. Litvinovich 1 , I.I. Bruchkouski 1 , L.V. Katkovsky 1 , M.Yu. Belyaev 2 , E.E. Sarmin 2 
1 A.N. Sevchenko Institute of Applied Physical Problems of Belarusian State University, Minsk, Belarus
2 S.P. Korolev Rocket and Space Сorporation “Energia”, Korolev, Russia
Accepted: 19.08.2022
DOI: 10.21046/2070-7401-2022-19-5-125-135
The work is dedicated to the studies of spectral features of Pícea ábies needles and the search for attributes of stress and dryness of the needles in reflectance spectra in the visible and NIR spectral range. The results of laboratory measurements of needles reflectance spectra for different stress levels (healthy, stressed, ill) are presented, the following vegetation indices are evaluated, which determine whether the needles belong to one of the selected stress levels: TVI, SR800/500, NDVI, SR800/635, ND790/670, SR800/675, SR800/650, ND800/675, ND800/650, ND800/500, SR800/470, ND800/635, ND800/470. For these indices, the ranges of values corresponding to different stress levels of the needles are determined. The proposed method makes it possible to separate healthy needles from needles at the initial stage of stress, when they are still green, which increases the accuracy of the method compared to a visual evaluation of a tree by a forest pathologist. The results of applying the classifier, developed on the basis of the proposed method, to airborne remote sensing data of coniferous forests are presented. The uncertainty evaluated as a percentage of the total number of those spectra for which the assigned stress levels do not match when classified by the proposed method and the alternative method (without training) is 10,1 %.
Keywords: Pícea ábies, reflectance, vegetation indices, vegetation dryness, spectra, remote sensing, airborne measurements, methods of classification
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