Spectral diversity tracks initial restoration progress in the Eastern Amazon

Monitoring the rehabilitation of mined lands and forest restoration is often limited by high costs and labor-intensive field surveys. This study evaluated the potential of spectral diversity to estimate the Restoration Index (RI), a metric that integrates structural and compositional vegetation attributes to indicate the degree of ecological recovery in areas undergoing rehabilitation or restoration. The analyses included plots representing different stages of recovery from four compensation and three rehabilitation areas in Pará, Eastern Amazon, Brazil. Different measures of spectral diversity (richness, Shannon index, Rao’s Q, and functional composition indices) were computed from Sentinel-2 satellite imagery using BiodivMapR package in R software, and correlations and linear regressions with the RI were tested. Among the spectral metrics tested, spectral Shannon diversity and spectral richness showed the strongest correlations with the restoration index. A regression model estimated that each 1% increase in the RI corresponds to a 0.0198 increase in spectral richness, with a predicted value of 2.29 at 70% restoration index, providing a useful benchmark for restoration assessment. Two areas were field-surveyed twice, and although spectral richness does not show significant increases between inventories, successional differences along the chronosequences were well covered in datasets from all inventories. Spectral diversity computed from images from different months correlates moderately, suggesting the robustness of spectral diversity metrics across seasons. Thus, our results demonstrate that spectral diversity offers a scalable, cost-effective, and transparent tool for reproducible restoration monitoring at landscape scales.