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JOURNALS // Informatics and Automation // Archive

Informatics and Automation, 2022 Issue 21, volume 2, Pages 427–453 (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.

UDC: 004.93

Received: 20.07.2021

DOI: 10.15622/ia.21.2.8



© Steklov Math. Inst. of RAS, 2024