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

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...

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Bibliographic Details
Published inSustainability Vol. 13; no. 12; p. 6941
Main Authors Tumelienė, Eglė, Visockienė, Jūratė Sužiedelytė, Malienė, Vida
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 20.06.2021
Subjects
Abandoned land
Agricultural land
Cold
Color
Cyclones
Environmental impact
Identification
Image processing
Land area
Rain
Remote sensing
Seasonal variations
Spectra
Vegetation mapping
Web portals
Lithuania
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su13126941