Document Type : Research Paper
Authors
Department of Nature engineering, Faculty of Natural Resources and Earth Sciences, University of Shahrekord, Shahrekord, Iran
Abstract
Considering the widespread changes in biodiversity and its vital importance in maintaining ecosystem stability and functionality, precise and continuous assessment of plant diversity indices is essential. Due to temporal, spatial, and economic constraints, field sampling is often difficult and costly in many regions. Therefore, remote sensing data have increasingly gained attention as a reliable, efficient, and cost-effective source for biodiversity assessment and monitoring. This study aims to evaluate the capability of Sentinel-2 satellite data in estimating plant biodiversity indices in semi-steppe rangelands. To this end, eight sampling sites were selected based on management conditions, vegetation cover, and ecological characteristics, and three 30*30 m2 macroplots were established at each site. Vegetation cover sampling was performed using a systematic-random method with 2*2 m2 plots along three transects. After calculating plant diversity indices including alpha diversity, beta diversity, and functional diversity, the relationships between these indices and vegetation indices derived from Sentinel-2 data were examined and statistically analyzed. Data analysis was conducted using linear regression and correlation tests in the R software environment. The results clearly demonstrate that vegetation indices derived from Sentinel-2 satellite imagery are capable of predicting different components of biodiversity in semi-steppe rangelands. Among the indices, EVI showed the strongest correlation with alpha diversity (R²=0.20, P-value=0.02) and functional diversity (functional richness) (R²=0.34, P-value=0.001), whereas NDVI exhibited the highest correlation with beta diversity (Bray-Curtis Similarity and distance indices index) (R²=0.21, P-value=0.01). Other indices such as MSAVI2, AVI, and SAVI also revealed positive and significant correlations with various biodiversity components, although their correlation coefficients were lower than those of the primary indices.
Keywords
)