Estimating leaf area index of the Yellowwood tree (Podocarpus spp.) in an indigenous Southern African Forest, using Sentinel 2 Multispectral Instrument data and the Random Forest regression ensemble

Yellowwood or Podocarpus (spp.) holds the esteemed biodiversity status, as key forest species in the mist-belt Afromontane forests of southern Africa. The podocarps are listed as endangered species owing to extensive logging. The forest species support large communities of plants and birds, attributing to the maintenance of biodiversity. Therefore, there is a need to understand the condition of such keystone species if effective and comprehensive biodiversity conservation measures are to be drawn for these dwindling forests. Leaf area index is a crucial eco-physiological parameter applied in the evaluation of the growth and productivity of forest trees, hence it is a suitable proxy for understanding the condition of Yellowwood trees. This study, therefore, sought to estimate the leaf area index of the Yellowwood spp. using Sentinel 2 Multispectral instrument (S2 MSI) data in concert with the Random Forest regression ensemble. Specifically, individual wavebands and vegetation indices were used in developing leaf area index prediction models based on two approaches. The multistage approach, categorised the predictors according to the generalised order of progression, from standard spectral bands to vegetation indices. The second approach involved using a pooled set of predictors, with the backward elimination of poorly performing wavebands and vegetation indices. Results showed that the backward elimination method produced a better model (R² = 0.59; RMSE = 0.48) when compared to the multistage approach (R² = 0.50; RMSE = 0.48). The most influential predictor variables in both models were Band 5 and NDVI Red Edge 2. Results of this study underscore the prospects of Sentinel 2 MSI data in characterising the productivity of critical forest species such as the Yellowwoods of the Afromontane forest in southern Africa. The findings of this study are a fundamental step towards understanding forest health and productivity, required in deriving comprehensive monitoring and management strategies in biodiversity conservation.