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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. 3, pp. 88-102

Detection of drying coniferous forests from aerospace data

L.V. Katkovsky 1 , V.A. Siliuk 1 , B.I. Belyaev 1 , M.Yu. Belyaev 2 , E.E. Sarmin 2 , I.I. Bruchkouski 1 , S.I. Guliaeva 1 , G.S. Litvinovich 1 , Yu.S. Davidovich 1 
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: 20.06.2022
DOI: 10.21046/2070-7401-2022-19-3-88-102
The paper presents the methods and the results of remote sensing of conifers in different dryness stages. Laboratory measurements, airborne measurements and satellite data were used. Spectral devices that were used in ground, airborne and satellite measurements are described. One of the approaches to the detection and classification of drying conifers is using vegetation indices. Most informative vegetation indices were determined that show high correlation with dryness stages. The indices can be used for both high spectral resolution data and multispectral satellite data. A comparative analysis of classification accuracy is carried out when using as initial signs reflectance or vegetation indices. Several classifiers are used: linear discriminant analysis, random forest and maximum likelihood. A higher classification accuracy is shown when using vectors of high-dimensional vegetation indices instead of the reflectance of coniferous trees. Another approach is transforming multispectral images in the original spectral space in order to enhance the existing spectral differences and classify the transformed reflectance images. The developed methods demonstrate the possibility of identifying drying coniferous forests areas based on the processing of simultaneous space images of medium spatial resolution (10–30 m) in the visible and near-IR spectral ranges.
Keywords: reflectance, vegetation indices, vegetation dryness, spectra, remote sensing, methods of classification
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