Abstract
Knowledge on rangeland condition, productivity patterns and possible thresholds of potential concern, as well as the escalation of risks in the face of climate change and variability over savanna grasslands is essential for wildlife/livestock management purposes. The estimation of leaf area index (LAI) in tropical savanna ecosystems is therefore fundamental for the proper planning and management of this natural capital. In this study, we assess the spatio-temporal seasonal LAI dynamics (dry and wet seasons) as a proxy for rangeland condition and productivity in the Kruger National Park (KNP), South Africa. The 30 m Landsat 8 Operational Land Imager (OLI) spectral bands, derived vegetation indices and a non-parametric approach (i.e., random forest, RF) were used to assess dry and wet season LAI condition and variability in the KNP. The results showed that RF optimization enhanced the model performance in estimating LAI. Moderately high accuracies were observed for the dry season (R2 of 0.63–0.72 and average RMSE of 0.60 m2/m2) and wet season (0.62–0.63 and 0.79 m2/m2). Derived thematic maps demonstrated that the park had high LAI estimates during the wet season when compared to the dry season. On average, LAI estimates ranged between 3 and 7 m2/m2 during the wet season, whereas for the dry season most parts of the park had LAI estimates ranging between 0.00 and 3.5 m2/m2. The findings indicate that Kruger National Park had high levels of productivity during the wet season monitoring period. Overall, this work shows the unique potential of Landsat 8-derived metrics in assessing LAI as a proxy for tropical savanna rangelands productivity. The result is relevant for wildlife management and habitat assessment and monitoring.
Highlights
Savanna ecosystems comprise socio-economically and ecologically important biodiversity resources
The presence of vast amounts of forage resources is linked to wildlife productivity, and this can contribute to the economy through tourism, which accounts for about 9% of employment in South Africa [4]
We assessed the spatio-temporal dynamics of leaf area index (LAI) for the dry and wet seasons, as a proxy for rangeland condition and productivity in the Kruger National Park, South Africa
Summary
Savanna ecosystems comprise socio-economically and ecologically important biodiversity resources. Productivity in these ecosystems is more often assessed using two plant biophysical properties, which are the leaf area index (LAI) and aboveground biomass (AGB) [2] These parameters play a major role in animal productivity and can help provide insights on rangelands condition, in terms of the quality and quantity of natural capital. A couple of studies have estimated or predicted LAI using remotely sensed data, and most of these were not linked to tropical savanna ecosystems or rangeland productivity. The medium-resolution Landsat 8 Operational Land Imager (OLI) sensor is one of the key primary data sources It is highly suitable and practical for regional LAI analysis, especially in resource-limited areas. Remote Sens. 2019, 11, x; doi: FOR PEER REVIEW Remote Sens. 2019, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/remotesensing www.mdpi.com/journal/remotesensing
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