Integrating remote sensing and conventional grazing/browsing models for modelling carrying capacity in southern African rangelands

C. Adjorlolo; J. O. Botha; P. Mhangara; O. Mutanga; J. Odindi

doi:10.1117/12.2066330

11 November 2014 Integrating remote sensing and conventional grazing/browsing models for modelling carrying capacity in southern African rangelands

C. Adjorlolo, J. O. Botha, P. Mhangara, O. Mutanga, J. Odindi

Author Affiliations +

Proceedings Volume 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI; 92390B (2014) https://doi.org/10.1117/12.2066330
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

Woody vegetation encroachment into grasslands or bush thickening, a global phenomenon, is transforming the Southern African grassland systems into savanna-like landscapes. Estimation of woody vegetation is important to rangeland scientists and land managers for assessing its impact on grass production and calculating its grazing and browsing capacity. Assessment of grazing and browsing components is often challenging because agro-ecological landscapes of this region are largely characterized by small scale and heterogeneous land-use-land-cover patterns. In this study, we investigated the utility of high spatial resolution remotely sensing data for modelling grazing and browsing capacity at landscape level. Woody tree density or Tree Equivalents (TE) and Total Leaf Mass (LMASS) data were derived using the Biomass Estimation for Canopy Volume (BECVOL) program. The Random Forest (RF) regression algorithm was assessed to establish relationships between these variables and vegetation indices (Simple Ratio and Normalized Difference Vegetation Index), calculated using the red and near infrared bands of SPOT5. The RF analysis predicted LMASS with R² = 0.63 and a Root Mean Square Error (RMSE) of 1256 kg/ha compared to a mean of 2291kg/ha. TE was predicted with R² = 0.55 and a RMSE = 1614 TE/ha compared to a mean of 3746 TE/ha. Next, spatial distribution maps of LMASS/ha and TE/ha were derived using separate RF regression models. The resultant maps were then used as input data into conventional grazing and browsing capacity models to calculate grazing and browsing capacity maps for the study area. This study provides a sound platform for integrating currently available and future remote sensing satellite data into rangeland carrying capacity modelling and monitoring.

Citation Download Citation

C. Adjorlolo, J. O. Botha, P. Mhangara, O. Mutanga, and J. Odindi "Integrating remote sensing and conventional grazing/browsing models for modelling carrying capacity in southern African rangelands", Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92390B (11 November 2014); https://doi.org/10.1117/12.2066330

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