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

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).