High resolution remote sensing for biodiversity assessment and monitoring: A case study of dominant tree species in an old-growth forest

Abstract

Biodiversity assessment and monitoring in old-growth forests (OGFs) is crucial for their effective management and conservation amongst various natural and human-induced challenges. In the present study, the spatial and temporal species diversity were assessed in the mixed stands of Białowieża Forest (BF), a renowned UNESCO world heritage site recognized as an OGF. The BF has encountered challenges due to pest outbreaks peaking in 2016–2017 with adverse impacts on species composition. Utilizing airborne laser scanning (ALS) and color infrared (CIR) datasets from 2015 and 2019 (prior and following the peak, respectively), 30 variables were extracted and employed as input for the Random Forest algorithm to identify 14 species of broadleaved and coniferous individuals at plot levels (458 plots) within the BF. We calculated biodiversity indices (including α, β, and γ diversity), Importance Value Index (IVI), bivariate scale-dependent density of species, and temporal β diversity indices independently for data acquired from both field observation (FO) and species maps obtained through remote sensing analysis (RSA). The study found no significant differences in α diversity indices between the FO and RSA across the BF, however, notable discrepancies existed in species richness. Limited species detection was attributed to low species frequency and therefore, insufficient reference individuals. Average β and γ diversities from both datasets showed no significant differences. The RSA accurately identified top species and their changes in terms of IVI. Additionally, the RSA explored scale-dependent density changes of broadleaved individuals around conifers. Consistency was observed between FO and RSA in identifying dominance shifts from losses to gains within the BF communities. The comparison of diversity indices obtained from FO and RSA suggests that the RSA provided comparable results to FO in capturing the diversity of dominant tree species within the BF, providing a promising approach for advancing our understanding of forest biodiversity in OGFs.