No abstract

An understanding of the response of indicators of rangeland degradation following rehabilitation is essential to the successful implementation of the Payment for Environmental Services initiative that is currently being developed in the communal rangelands of the Drakensberg mountains of South Africa. We evaluated the following four potential indicators of rangeland degradation: Range condition, basal cover, species diversity, and soil fertility. The indicators were measured in degraded and rehabilitated sites at Okhombe in Northern KwaZulu-Natal, South Africa. Two transects were established at each site for basal cover and species composition. Soil samples were collected from each site and their elements analysed. The results revealed that differences between the rehabilitated and degraded sites can be quantified using indicators of range condition, basal cover, and species diversity. There were highly significant differences in certain soil properties (that is, P 11.36 mg/kg, K 0.47 cmol/kg, pH 4.20, OC 6.33% and N 0.70%) after rehabilitation. Based on these results, we argue that these indicators have the potential to be used in monitoring and certifying the delivery of watershed services at a local level in this communal rangeland.

Land degradation is believed to be one of the most severe and widespread environmental problems. In South Africa, large areas of land have been identified as degraded, as shown by the lower vegetation cover. One of the major causes of grassland degradation is change in plant species composition that leads to presence of unpalatable grass species. Some grass species have been successfully used as indicators of different levels of grassland degradation in the country. This paper, therefore explores the possibility of mapping grassland degradation in Cathedral Peak, South Africa, using indicators of grass species and edaphic factors. Multispectral SPOT 5 data were used to produce a grassland degradation map based on the spatial distribution of decreaser (Themeda triandra) and increaser (Hyparrhenia hirta) species. To improve mapping accuracy, soil samples were collected from each species site and analysed for nutrient content. A t-test and machine learning random forest classification algorithm were applied for variable selection and classification using SPOT 5 data and edaphic variables. Results indicated that the decreaser and increaser grass species can be mapped with modest accuracy using SPOT 5 data (overall accuracy of 75.30%, quantity disagreement = 2 and allocation disagreement = 23). The classification accuracy was improved to 88.60%, 1 and 11 for overall accuracy, quantity and allocation disagreements, respectively, when SPOT 5 bands and edaphic factors were combined. The study demonstrated that an approach based on the integration of multispectral data and edaphic variables, which increased the overall classification accuracy by about 13%, is a suitable when adopting remote sensing to monitor grassland degradation.

The development of new multispectral sensors with unique band settings is critical for mapping the spatial distribution of increaser vegetation species in disturbed rangelands. The objective of this study was to evaluate the potential of WorldView-2 imagery for spectral classification of four increaser species, namely Hyparrhenia hirta, Eragrostis curvula, Sporobolus africanus, and Aristida diffusa, in the Okhombe communal rangelands of South Africa. The 8-bands were extracted from the WorldView-2 image, and 24 of the most widely used vegetation indices in estimating grassland biophysical parameters were calculated. The random forest algorithm and forward variable method were applied to identify the optimal variables (WorldView-2 spectral bands, vegetation indices, and a combination of bands and indices) for classifying the species. The random forest algorithm could classify species with an overall accuracy of 82.6% (KHAT½an estimate of κ ¼ 0.76) using six of the WorldView-2 spectral bands and an overall accuracy of 90% (KHAT ¼ 0.87) using a subset of vegetation indices (n ¼ 9). Three bands selected were located at the new WorldView-2 spectral regions of coastal blue, yellow, and the red-edge. There was no significant improvement in increaser species classification by using a combination of bands and indices. Overall, the study demonstrated the potential of the WorldView-2 data for improving increaser separability at species level.