Effectiveness evaluation of a modified EVI-S index in monitoring system of ve­ ge­tation cover

Remote sensing methods are essential for environmental monitoring and assessment. They provide objective data on vegetation cover, enabling the de­tection of changes and evaluation of ecological processes. In Ukraine, located in a temperate climate zone, favorable conditions support vegetation growth and agricultural activities. However, the agricultural sector faces significant environmental challenges, such as climate change, soil degradation, and erosion. Monitoring these processes is crucial for sustainable land use and resource management. To assess changes in vegetation cover and their environmental impact, satellite remote sensing and spectral indices are commonly applied. Widely used indices include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and their modifications. These indices, however, have limitations, including sensitivity to soil background, atmospheric interferen­ce, and saturation at high vegetation densities. To address these limitations, a new index called EVI-S (Enhanced Vegetation Index — Soil Adjusted) has been proposed. It combines the strengths of SAVI and EVI2 while minimizing atmospheric interference. The primary goal is to develop and validate this new index to enhance ecological monitoring. The study area is the Feodosiivska territorial community in Kyiv region, which includes natural ecosystems and agri­cultural lands. The landscape is predominantly flat, typical of the forest-steppe zone. Sentinel-2 satel­lite images, processed using QGIS with the SCP plugin, provided data for calculating NDVI, EVI, EVI2, SAVI, and EVI-S indices. Results indicate that the EVI-S index shows higher maximum and average values compared to conventional indices, suggesting improved sensitivity to dense vegetation. Correlation analysis demonstrated that while traditional indices (EVI, EVI2, SAVI) show strong mutual correlation, NDVI displays slightly lower correlation due to satu­ration issues. Notably, EVI-S exhibits a high correla­tion with NDVI, indicating retained similarity despite improved formulation. EVI-S is particularly effec­tive in analyzing urban and mixed landscapes, where conventional indices may underestimate vegetation presence. Its wider dynamic range and increased sensitivity make it useful for monitoring vegetation growth and biomass, especially in forests and agri­cultural areas. Further studies are needed to assess its application in urban environments, where hetero­ geneous vegetation cover may impact accuracy. The findings demonstrate that EVI-S can complement traditional indices, offering enhanced sensitivity in contexts where accurate vegetation density analysis is necessary. Its practical application is promising for crop monitoring during growth periods and for evaluating dense forest areas.