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Algorithm for Statistical Analysis of Multispectral Survey Data to Identify the Anthropogenic Impact of the 19th Century on the Natural Environment

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Abstract

An algorithm for statistical analysis of aerial photography data obtained by unmanned aerial vehicles is proposed. Segmentation of multispectral images by a complex of spectral and textural features makes it possible to identify areas of historical anthropogenic impact on the natural environment. The test site was the territory of the economic district of the Pudemsky ironworks (Udmurt Republic), where the arable lands of factory peasants were located in the first half of the 19th century. The location of the arable land and its configuration were restored as a result of the transformation of historical cartographic materials from 1817–1832. At the first stage of the algorithm, it is supposed to calculate features according to multispectral survey data (Haralick’s features, NDVI index); at the second stage, it is to reduce the number of features by the principal component analysis; at the third stage, it is to segment images based on the received features by the method k-means. The initial data were the results of multispectral imaging in Green, Red, RedEdge, and near infrared (NIR) spectral ranges. The efficiency of the proposed algorithm is shown by comparing the segmentation results with reference data (historical cartographic materials and aerial photographs in the visible range).

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Funding

The study was financially supported by the Russian Foundation for Basic Research (project no. 19-18-00322).

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Authors and Affiliations

  1. Udmurt Federal Research Center Ural Branch, Russian Academy of Sciences, 426067, Izhevsk, Russia

    A. G. Zlobina, A. S. Shaura, I. V. Zhurbin & A. I. Bazhenova

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  1. A. G. Zlobina
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  2. A. S. Shaura
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  3. I. V. Zhurbin
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  4. A. I. Bazhenova
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Corresponding authors

Correspondence to A. G. Zlobina, A. S. Shaura, I. V. Zhurbin or A. I. Bazhenova.

Additional information

Anna Grigorievna Zlobina. Born in 1990. Graduated from the Kalashnikov Izhevsk State Technical University in 2013. Ph.D. thesis was defended in 2017. Researcher at the Udmurst Federal Research Center (Ural Branch, Russian Academy of Sciences). Research interests: information technology, image analysis, and digital signal processing. Author of 24 articles.

Alexander Sergeevich Shaura. Born in 1986. Graduated from the Kalashnikov Izhevsk State Technical University in 2009. Ph.D. thesis was defended in 2012. Senior Researcher at the Udmurst Federal Research Center (Ural Branch, Russian Academy of Sciences). Research interests: artificial intelligence, optimization methods and optimal control, and data mining. Author of 23 articles.

Igor Vitalievich Zhurbin. Born in 1966. Graduated from the Izhevsk Mechanical Institute in 1988. Candidate dissertation defended in 1994, doctoral dissertation in 2006. Chief Researcher at the Udmurt Federal Research Center (Ural Branch, Russian Academy of Sciences). Research interests: information technology, image analysis, and interdisciplinary research. Author of four monographs, four patents of the Russian Federation, and more than 120 articles. Member of the editorial board of the “Historical Informatics. Information Technology and Mathematical Methods in Historical Research and Education” journal. Laureate of the National Prize in the field of protection of the archaeological heritage “Heritage of Generations” (2006). Laureate of the State Prize of the Udmurt Republic in the field of science and technology (2007). Honored Scientist of the Udmurt Republic (2016).

Aigul Ilsurovna Bazhenova. Born in 1990. Graduated from the Kalashnikov Izhevsk State Technical University in 2013. Ph.D. thesis was defended in 2017. Researcher at the Udmurst Federal Research Center (Ural Branch, Russian Academy of Sciences). Research interests: digital signal processing, pattern recognition, and wavelet analysis. Author of 25 articles.

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Zlobina, A.G., Shaura, A.S., Zhurbin, I.V. et al. Algorithm for Statistical Analysis of Multispectral Survey Data to Identify the Anthropogenic Impact of the 19th Century on the Natural Environment. Pattern Recognit. Image Anal. 31, 345–355 (2021). https://doi.org/10.1134/S1054661821020176

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