Authors :
Elena Diana BOBRIC 1,2,3 , Alexandra Petronela STOLERIU 1,2 , Lilian NIACŞU 1,3 , George Ștefan ALION 1 , Iuliana Gabriela BREABĂN 1,2,3
1 “Alexandru Ioan Cuza” University of Iasi, Faculty of Geography and Geology, Department of Geography;
2 Institute of Interdisciplinary Research - ICI-UAIC, CERNESIM Department;
3 “Alexandru Ioan Cuza” University of Iasi, Faculty of Geography and Geology, Doctoral School of Geosciences
E-mail  : * Corresponding author: stoleriualexandra@yahoo.com, +40 749 422 879 (A.P.S.); iulianab2001@gmail.com, +40 748 557 429 (I.G.B.).
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Abstract
Remote sensing technology offers the possibility to monitor biophysical attributes and changes in plant biomass and productivity during the growing season, which can enable sustainable management. Recent advances in satellite remote sensing technology have produced innovative sensors for monitoring the Earth's surface, with increasing spatial and temporal resolution of available satellite images, such as those provided by the Sentinel-2, creating new opportunities for environmental monitoring and the generation of accurate datasets. This study aimed to assess vegetation condition during the spring, summer and autumn seasons in a protected area near Iasi, ROSCI0058, using biophysical indices derived from Sentinel-2 satellite imagery. The study area was chosen due to the existence of signals indicating the possibility of changes in the type and health status of vegetation within the site of Community importance. The analysis was based on a series of vegetation-specific spectral indices such as: normalized differential vegetation index (NDVI), leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), fraction of photosynthetically active absorbed radiation (FAPAR) and fraction of canopy cover (FCOVER), derived from Sentinel-2 high-resolution images. The time series of satellite images used covers the phenophase periods specific to the spontaneous flora in the period 2020-2022. With SNAP software the Sentinel-2 images were pre-processed to convert the reflectance of the ToA images to BoA, vegetation indices were calculated, after which final distribution maps were created with ArcGIS. The results indicate that the highest values for NDVI, LAI, FAPAR, FCOVER, CAB and CW did not follow a pattern, they occurred at different times of the year, as follows: in the spring season, the highest value was on April 10, 2020; in the summer season, highlighting the values of July 9, 2021 while for the fall, the year 2022 recorded the highest values on September 7, the results being directly proportional to the variation of climatic parameters. The analysis also considered the type of land use, with non-irrigated arable land having the highest values for various indices. The results highlight the potential of Sentinel-2 images for these types of studies, as they can be used to observe and assess the health of the vegetation cover.
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