Integration of remote sensing and conventional models for modeling grazing/browsing capacity in southern African savannas

Abstract

Woody vegetation encroachment into grasslands or bush thickening is transforming the southern African grassland systems into savanna-like landscapes. Estimation of woody plant density and canopy biomass is important to rangeland scientists for assessing the impact on grass production and for calculating grazing and browsing capacity. We investigated the utility of remote sensing data for modeling grazing and browsing capacity at landscape level. Tree density or tree equivalents (TEs) and total leaf mass (LMASS) data, derived using the biomass estimation for canopy volume program, were assessed. The random forest (RF) regression algorithm was evaluated to predict TE and LMASS using vegetation indices calculated on red and near-infrared bands of Satellite Pour l’Observation de la Terre-5. The RF model predicted LMASS with R2=0.64 and a root mean square error (RMSE) of 1256 kg/ha compared to a mean of 2291 kg/ha. TE was predicted with R2=0.56 and a RMSE of 1614 TE/ha compared to a mean of 3746 TE/ha. Next, maps of LMASS/ha and TE/ha were derived using separate RF models. The resultant maps were then used in conventional grazing and browsing capacity models. The study provides a sound platform for integrating available and future remote sensing data into rangeland carrying capacity modeling and monitoring.