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Informatics and Automation, 2022, Issue 21, volume 2, Pages 427–453
DOI: https://doi.org/10.15622/ia.21.2.8
(Mi trspy1196)
 

Artificial Intelligence, Knowledge and Data Engineering

Analysis of multi-temporal multispectral aerial photography data to detect the boundaries of historical anthropogenic impact

A. Shaura, A. Zlobina, I. Zhurbin, A. Bazhenova

Udmurt Federal Research Center of the Ural Branch of the Russian Academy of Sciences
Abstract: The article presents the application of a statistical analysis algorithm for multi-temporal multispectral aerial photography data to identify areas of historical anthropogenic impact on the natural environment. The investigated site is located on the outskirts of the urban-type village of Znamenka (Znamensky District, Tambov Region) in a forest-steppe zone with typical chernozem soils, where arable lands were located in the second half of the 19th - early 20th centuries. Grown vegetation as a result of secondary succession in abandoned areas can be a sign for identifying traces of historical anthropogenic impact. Distinctive signs of such vegetation from the surrounding natural environment are its type, age and growth density. Thus, the problem of detecting the boundaries of anthropogenic impact on multispectral images is reduced to the problem of vegetation classification. The initial data were the results of multi-temporal multispectral imaging in green (Green), red (Red), edge of red (RedEdge) and near-infrared (NIR) spectral ranges. The first stage of the algorithm is the calculation of the Haralick texture features on multispectral images, the second stage – reduction in the number of features by the principal component analysis, the third stage – the segmentation of images based on the obtained features by the k-means method. The effectiveness of the proposed algorithm is shown by comparing the segmentation results with the reference data of historical cartographic materials. The study of multi-temporal multispectral images makes it possible to more fully characterize and take into account the dynamics of phytomass growth in different periods of the growing season. Therefore, the obtained segmentation result reflects not only the configuration of areas of an anthropogenic transformed natural environment, but also the features of overgrowth of abandoned arable land.
Keywords: multispectral survey, texture segmentation, Haralick texture features, principal component analysis, clustering, k-means, multi-temporal data, growing season, secondary succession.
Funding agency Grant number
Russian Science Foundation 19-18-00322
The research was supported by the Russian Science Foundation under grant 19-18-00322 «Comparative historical research of anthropogenic landscapes in different regions by means of unmanned aerial vehicles (the Tambov region and Udmurtia, mid-XVIII - early XX centuries)».
Received: 20.07.2021
Document Type: Article
UDC: 004.93
Language: Russian
Citation: A. Shaura, A. Zlobina, I. Zhurbin, A. Bazhenova, “Analysis of multi-temporal multispectral aerial photography data to detect the boundaries of historical anthropogenic impact”, Informatics and Automation, 21:2 (2022), 427–453
Citation in format AMSBIB
\Bibitem{ShaZloZhu22}
\by A.~Shaura, A.~Zlobina, I.~Zhurbin, A.~Bazhenova
\paper Analysis of multi-temporal multispectral aerial photography data to detect the boundaries of historical anthropogenic impact
\jour Informatics and Automation
\yr 2022
\vol 21
\issue 2
\pages 427--453
\mathnet{http://mi.mathnet.ru/trspy1196}
\crossref{https://doi.org/10.15622/ia.21.2.8}
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