The Influence of Seasonality on the Multi-Spectral Image Segmentation for Identification of Abandoned Land

Eglė Tumelienė, Jūratė Sužiedelytė Visockienė and Vida Malienė
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Eglė Tumelienė: Department of Geodesy and Cadastre, Vilnius Gediminas Technical University, Sauletekio Av. 11, LT-10223 Vilnius, Lithuania
Jūratė Sužiedelytė Visockienė: Department of Geodesy and Cadastre, Vilnius Gediminas Technical University, Sauletekio Av. 11, LT-10223 Vilnius, Lithuania
Vida Malienė: Institute of Land Management and Geomatics, Vytautas Magnus University Agriculture Academy, Studentu 11, Akademija, LT-53361 Kaunas, Lithuania

Sustainability, 2021, vol. 13, issue 12, 1-16

Abstract: Areas of agricultural land in Lithuania have decreased from 2005 to 2021 by up to 2.4%. Agricultural lands that are no longer used for their main purpose are very likely to become abandoned and the emergence of such lands can cause a variety of social, economic, and environmental problems. Therefore, it is very important to constantly monitor changes of abandoned agricultural lands. The purpose of the research is to analyse the influence of seasonality on image segmentation for the identification of abandoned land areas. Multi-spectral Sentinel-2 images from different periods (April, July, and September) and three supervised image segmentation methods (Spectral Angle Mapping (SAM), Maximum_Likelihood (ML), and Minimum distance (MD)) were used with the same parameters in this research. Studies had found that the most appropriate time to segment abandoned lands was in September, according to the SAM and ML algorithms. During this period, the intensity of the green colour was the highest and the colour brightness of abandoned lands differed from the colour intensity of other lands.

Keywords: abandoned land; image segmentation; remote sensing; pixels; classes (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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