Numerical Assessments of Leaf Area Index in Tropical Savanna Rangelands, South Africa Using Landsat 8 OLI Derived Metrics and In-Situ Measurements
Department of Earth Sciences, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa
Graduate School of Agricultural and Life Sciences, University of Tokyo, 1-1-1 Yayoi, Bunkyo-Ku, Tokyo 113-8567, Japan
School of Geography, Archaeology and Environmental Studies, University of Witwatersrand, Private Bag 3, Johannesburg 2050, South Africa
South African National Parks, P.O. Box 787, Pretoria 0001, South Africa
Risk and Vulnerability Center, University of Limpopo, P Bag X1106, Sovenga 0727, South Africa
Department of Ecology and Resource Management, University of Venda, Private Bag X5050, Thohoyandou 0950, South Africa
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 829; https://doi.org/10.3390/rs11070829
Received: 20 February 2019 / Revised: 30 March 2019 / Accepted: 2 April 2019 / Published: 7 April 2019
(This article belongs to the Special Issue Leaf Area Index (LAI) Retrieval using Remote Sensing)
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.