Affiliation:
1. Agricultural Research and Development Station Lovrin
2. University of Life Sciences "King Michael I" from Timisoara
Abstract
The study analyzed forest vegetation in the "Bazos Dendrological Park" area, Timis County, Romania, in order to describe the seasonal variation of the vegetation through imaging analysis based on satellite images (Sentinel 2). The study took place in the period 2021-2022, and each year 7 sets of images (T1 - T7) were taken between the months of April and August. NDMI, NDVI and NBR indices were calculated from the analysis of satellite images. Among the calculated indices, very strong correlations were found between NBR and NDMI (r=-0.928, year 2021), between NBR and NDVI (r=0.947, year 2021; r=0.928, year 2022). Moderate correlations were found between NDVI and NDMI (r=-0.769, year 2021), and weak correlations were found between NDMI and t (r=-0.655, year 2021), between NDVI and NDMI (r=0.617, year 2022). Other weak intensity correlations were also recorded. The variation of the NDVI indices in relation to NDMI and the NBR index in relation to NDMI or to NDVI was described by polynomial equations of 2nd degree, under statistical safety conditions (p les than 0.001, R2>0.9 for the year 2021; p=0.007, R2 >0.9 in the case of NDVI vs NDMI; p=0.014, R2=0.877 in the case of NBR vs NDVI, respectively p less than 0.001, R2 bigger than 0.9 in the case of NBR vs NDMI for the year 2022). In relation to the time interval (t, days), spline models faithfully described the variation of the calculated indices during the study period, under statistical safety conditions ( ? = .0 0061 in the case of NDMI vs t, ? = 0017.0 in the case of NDVI vs t, ? = 0067.0 in the case of NBR vs t, under the conditions of 2021; ? = 0317.0 in the case of NDMI vs t, ? = 0024.0 in the case of NDVI vs t, ? = 0077.0 in the case of NDMI vs t, under the conditions of 2022).
Reference14 articles.
1. [1] Lechner A.M., Foody G.M., Boyd D.S., Applications in remote sensing to forest ecology and management, One Earth, vol. 2/issue 5, pp 405-412, 2020.
2. [2] Popescu C.A., Herbei M.V., Sala F., Remote sensing in the analysis and characterization of spatial variability of the territory. A study case in Timis County, Romania, Scientific Papers: Management, Economic Engineering in Agriculture & Rural Development, vol. 20/issue 1, pp 505-514, 2020.
3. [3] Mohammadpour P., Viegas D.X., Viegas C., Vegetation Mapping with Random Forest using Sentinel 2 and GLCM texture feature - A case study for Lousa Region, Portugal, Remote Sensing, vol. 14, 4585, 2022.
4. [4] Zhang L., Hu Q., Tang Z., Using Sentinel-2 imagery and Machine Learning Algorithms to assess the inundation status of Nebraska Conservation Easements during 2018�2021, Remote Sensing, vol. 14, 4382, 2022.
5. [5] Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings third Earth Resources Technology Satellite-1 Symposium, Greenbelt 1974, NASA SP�351/issue 1, pp 3010-3017, 1974.