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Requirements for multispectral remote sensing data used for the detection of arable land colonization by tree and shrubbery vegetation

A.Y. Denisova1, A.A. Egorova1, V.V. Sergeyev1,2, L.M. Kavelenova1

Samara National Research University,
443086, Samara, Russia, Moskovskoye Shosse 34,  
IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS,
443001, Samara, Russia, Molodogvardeyskaya 151

 PDF, 1565 kB

DOI: 10.18287/2412-6179-2019-43-5-846-856

Pages: 846-856.

Full text of article: Russian language.

We discuss requirements for the multispectral remote sensing (RS) data utilized in the author's technique for estimating plant species concentration to detect arable land colonization by tree and shrubbery vegetation. The study is carried out using available high-resolution remote sensing data of two arable land plots. The paper considers the influence of resolution, combinations of spectral channels of RS data, as well as the season RS data is acquired on the quality of identification of elementary vegetation classes that form the basis of the plant community – a fallow land. A fallow land represents a piece of arable land that has not been cultivated for a long time. The study was conducted using a technology that is based on image superpixel segmentation. We found out that for determining tree and shrub vegetation, it is preferable to use RS data acquired in autumn, namely, in late September. The combination of red and blue spectral channels turned out to be the best for the analysis of tree-shrub vegetation against the background of grassy plant communities, and the presence of a near-infrared channel is necessary to range the various grassy plant communities in different classes. RS data with a spatial resolution of 2.5 m can be used to define tree-shrub plant communities with a high closeness of crowns (90 % or more), but cannot be used to classify isolated trees. Trees and shrubs (with a height of 8 m) can be classified in images with a spatial resolution of 0.8 m. An increase in spatial resolution does not improve the quality of the classification. The highest accuracies achieved for the land areas studied are 90 % and 83 %. Therefore, the suggested technology can be used in arable land expertise.

arable land, fallow land, multispectral remote sensing data, shooting season, spatial resolution, spectral channel, superpixel, vegetation class.

Denisova AY, Egorova AA, Sergeyev VV, Kavelenova LM. Requirements for multispectral remote sensing data used for the detection of arable land colonization by tree and shrubbery vegetation. Computer Optics 2019; 43(5): 846-856. DOI: 10.18287/2412-6179-2019-43-5-846-856.

The work was supported by the RFBR grants #16-29-09494, #19-37-90116 and the Russian Federation Ministry of Science and Higher Education within a state contract with the "Crystallography and Photonics" Research Center of the RAS under agreement 007-ГЗ/Ч3363/26.


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