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Assessment of the Pine Forests Condition Using Forest Factors, Physiological Characteristics and Remote Detection Data

Authors

  • Mariusz Szymanek

    {"en_US":"university of life sciences in lublin."}
    https://orcid.org/0000-0002-3337-0337

  • Wojciech Tanaś

    {"en_US":"university of life sciences in lublin."}
    https://orcid.org/0000-0002-9544-8902

  • Alexander Pugachevsky

    {"en_US":"university of life sciences in lublin."}

  • Alexander Sazonov

    {"en_US":"university of life sciences in lublin."}

  • Sergey Kostyukevich

    {"en_US":"university of life sciences in lublin."}

  • Mikhail Kudin

    {"en_US":"university of life sciences in lublin."}

  • Anatoly Puchilo

    {"en_US":"university of life sciences in lublin."}

  • Oleg Semyonov

    {"en_US":"university of life sciences in lublin."}

  • Vladimir Shukanov

    {"en_US":"university of life sciences in lublin."}

DOI:

https://doi.org/10.2478/agriceng-2021-0003

Keywords:

forest monitoring, biochemical analyzes, vegetation indices, remote sensing

Abstract

This paper evaluates the pathological condition of Belarusian forests with the use of monitoring of traditional forest factors and remote sensing data. The aim of the research was to assess the condition of pine forests to monitor forest degradation based on biochemical analyzes of needle samples and aviation monitoring with the use of monitoring data and remote detection. The remote shooting was carried out quasi-synchronously with the ground sampling of needles using an unmanned aircraft complex of an aircraft type. Based on the results of biochemical analyzes of needle samples, biochemical indicators that characterize the stability and physiological state of pine were determined: the level of peroxidation of membrane lipids; the release of water-soluble substances from plant tissues, which reflect the integrity of the cell walls; the content of photosynthetic pigments in the needles.

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Published

2021-05-24

Issue

Vol. 25 (2021)

Section

Articles

How to Cite

Assessment of the Pine Forests Condition Using Forest Factors, Physiological Characteristics and Remote Detection Data. (2021). Agricultural Engineering , 25, 29-49. https://doi.org/10.2478/agriceng-2021-0003

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