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