The characterisation of the Yellowwoods’ leaf area index, within a southern African mistbelt forest, using Geographic Information Systems and Remote Sensing

Forests hold a plethora of ecological functions, that are critical in the maintenance of biodiversity. For instance, the mistbelt forests are small and fragmented forest ecosystems, which are dominated by South Africa’s national tree Podocarpus latifolius or the ‘real’ Yellowwood. The Yellowwood tree s are regarded as a priority species, because they support large communities of plants, birds and mammals, attributing to the maintenance of biodiversity. They also store carbon and provide habitat as well as resources for pollinators. Meanwhile, they produce durable, high quality timber which is harvested for cultural, medicinal and commercial use. Subsequently, the podocarps have been listed as endangered species owing to extensive logging. Current forest species productivity information is urgently required in the characterisation of forest condition, especially that of disturbed habitats which support a variety of indigenous species and contribute immensely to ecosystem resilience. Leaf area index (LAI) is a widely applied measure of photosynthetic capacity and productivity, that may reveal this information. Meanwhile, local level floral biodiversity and productivity is driven by topographic heterogeneity. Micro landscape level processes associated with topography are important in the determination of leaf area index, that may be applied as proxy measures of productivity. However, this is not well understood, especially for unique forest types such as the Afromontane mistbelt forest. Recently, Geographic Information Systems and Remote Sensing, have progressed to assume a pivotal role in the monitoring of vegetation, offering increased spatial coverage and modern, automated processing techniques. The applicability of digital elevation models such as the Shuttle Radar Topographic Mission, facilitates the retrieval of objective topographic measurements, that can be used in the explanation of leaf area. Meanwhile, the onset of “new generation” sensors, such as Sentinel 2 Multispectral Instrument has provided opportunity for investigating leaf area index, which cannot be achieved using traditional field surveys. This progression is supplemented by the application of machine learning algorithms, in the modelling of ecological relationships. Applying these algorithms in this regard, has demonstrated strength in managing data of high dimensionality, with speed and accurate results. The study therefore set out to characterise the leaf area index of the Yellowwood within a southern African mistbelt forest, using Geographic Information Systems and Remote Sensing techniques. Specifically, the study evaluated the role of Shuttle Radar Topographic Mission digital elevation model derived topographic variables, in the explanation of LAI, using Principal Component Analysis (PCA) and regression analysis. It also assessed the utility of Sentinel 2 imagery, in the estimation LAI, using the Random Forest (RF) regression ensemble. The stepwise linear regression analysis showed that topographic indices accounted for approximately 85 % of leaf area index variance (SEE = 35m 2 /m 2 ; R 2 = 0.85). Optimal topographic variables included the mass balance index and sky view factor. These can explain the micro landscape properties relating to ecosystem budgets and visible sky. Results also indicated that the backward elimination method optimally predicted the leaf area index of Yellowwood trees (RMSE = 0.48; R 2 = 0.59). Further, predictors derived from the Red Edge bands were the most influential variables in predicting leaf area index. The study demonstrated the applicability of remotely sensed data and Geographic Information Systems in the retrieval of forest productivity proxies, such as leaf area index. Abstract 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. Surface topography affects abiotic properties relating to soil, hydrology, drainage, and microclimate, that influences vegetation growth and contributes to environmental complexity. Meanwhile, the Yellowwoods contribute to the maintenance of biodiversity by providing a habitat to many forest species, such as the endangered Cape Parrot ( Poicephalus robustus ), that are heavily dependent on them. In this regard, understanding the influence of topography on the productivity and health condition of these trees will provide a framework required in drawing up effective conservation strategies for indigenous forest species. Tree properties such as leaf area index, are important indicators of plant condition and productivity, which are constrained by local topographic conditions. This study sought to assess the influence of topographic variables on the leaf area index of Yellowwood tree species. Principal component analysis and the stepwise linear regression were used to assess the influence of Shuttle Radar Topography Mission digital elevation model derived topographic variables, on leaf area index. Results of this study showed the catchment area, mass balance, sky view factor, convergence index, maximum curvature and aspect (SEE = 35m 2 /m 2 and R 2 = .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 towards drawing up comprehensive frameworks for inventorying and monitoring indigenous forest species. ’s exploratory power for emergent tree properties such as, leaf area combined with variable selection techniques are appropriate in studying vegetation condition. Findings could have applications in explaining landscape patterns of LAI in forested environments, in South Africa. The analysis of landscape level topography is important in understanding forest structure determinants, for conservation and forest management. Abstract Podocarpus (spp.) holds the esteemed biodiversity status, as key forest species in the mistbelt Afromontane forest 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 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. The index serves as an indicator of canopy condition and photosynthetic capacity; 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 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 Red Edge wavebands and 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 (R 2 = 0.59; RMSE = 0.48) when compared to the multistage approach (R 2 = .50; RMSE = .48). Furthermore, the most influential predictor variables in both models were Band 5 and NDVI Red Edge 2 , both characterised by the Red Edge region of the electromagnetic spectrum. Results of this study underscore the prospects of Sentinel 2 data i.e. Red Edge wavebands and indices, 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.