Abstract:
In recent decades, natural rangelands have emerged as vital sources of livelihood and ecological services, particularly in Southern Africa, supporting communities in developing regions. However, the escalating global demand for food, driven by a growing human population, has led to the extensive expansion of cultivated areas, resulting in continuous nutrient leaching in rangelands. To ensure the long-term viability of these ecosystems, there is a need to develop effective approaches for managing and monitoring the seasonality of forage quality. This study aims to achieve this by utilizing multispectral Sentinel-1 (S1) and Sentinel-2 (S2) data to monitor the seasonal distribution and occurrence of carbon (C), nitrogen (N), acid detergent fiber (ADF), and the (C:N:ADF) ratio in mesic rangelands. Six sites were randomly selected from Welgevonden and Hoogland private game reserves in Limpopo, South Africa, representing varying vegetation cover and standing biomass. Transects, each with ten fixed sample sites (30 × 30 m) characterized by homogeneous vegetation, were established. The grass samples and aboveground biomass were collected during each season and analyzed for biochemical parameters using a near-infrared spectroscopy (NIRS) machine. S1 and S2 data from Google Earth Engine (GEE) were employed, and the random forest (RF) modelling algorithm revealed significant seasonality impacts on the distribution of forage C:N:ADF ratios. The study demonstrates that integrating S1 and S2 data enhances the estimation of forage nutrients. This study offers valuable insights for a diverse range of stakeholders, including ecologists, resource managers, farmers, and park managers. By giving an understanding of nutrient limitations and facilitating a deeper understanding of resource availability and animal distribution in rangelands, this research serves as a crucial tool for informed decision-making and sustainable management practices.
