| Abstract [eng] |
This master's thesis analyzes the application of remote sensing for monitoring forest changes using satellite data (Sentinel-2, Landsat, MODIS). The study area selected is the Švenčionys district – one of the most forested regions in Lithuania, characterized by high ecological sensitivity and diverse anthropogenic and natural influences. The main objective of the research is to assess forest cover condition and compare the capabilities of different satellite datasets in identifying potential degradation or regeneration processes through the use of spectral vegetation indices and geoinformation methods. The analysis was based on satellite data from May 2024 and employed four main vegetation indices: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Normalized Burn Ratio (NBR), and the Normalized Vegetation Index (NVI). All satellite imagery was processed in the QGIS environment. The results demonstrated that remote sensing, integrated with GIS-based solutions, enables effective identification of forest structure changes and assessment of forest condition at the regional level. The study confirmed that Sentinel-2, Landsat, and MODIS data are suitable for forest change analysis, although their effectiveness varies depending on spatial resolution, spectral characteristics, and the observation period. |
