Mapping leaf area index of the Yellowwood tree species in an Afromontane mistbelt forest of southern Africa using topographic variables

Forming a holistic understanding of tropical indigenous forest vegetation dynamics is crucial for effective conservation monitoring and management strategies. Local surface topography assumes a pivotal role in the distribution and productivity of specialist forest species, such as the Yellowwood tree. Meanwhile, the Yellowwoods contribute to the maintenance of biodiversity by providing habitat to many forest species, such as the endangered Cape Parrot (Poicephalus robustus), which are heavily dependent on them. Tree properties such as leaf area indices are important indicators of plant condition and productivity, which are constrained by local topographic conditions. Therefore, this study sought to assess the influence of topographic variables on the leaf area index of Yellowwood tree species. Principal component analysis (PCA) and the stepwise linear regression (LR) were used to assess the influence of Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) derived topographic variables, on leaf area index. Specifically, 29 topographic indices were used to estimate the leaf area index in this study. Based on the PCA, variables from four components were then selected based on their eigen values and used to estimate LAI using LR. Results of this study showed that the catchment area, mass balance, sky view factor, convergence index, maximum curvature and aspect (Standard Error of Estimate (SEE) = 0.35 m2/m2 and R2 = 0.85) explained the spatial variation of the observed leaf area index of Yellowwood tree species in the Ingeli State Forest. The findings of this study are a fundamental step toward drawing up comprehensive frameworks for inventorying and monitoring indigenous forest species.