Search Results (2)

Southern African grasslands with a rich flora, shaped by fire, grazing, climate and geology, as well as playing a role in carbon sequestration, are becoming more important in conservation. Fire is often used as a management tool to improve vegetation and to protect property against uncontrolled fire. We therefore attempt to determine the effect consecutive burning has on vegetation. Paired plots along firebreaks were used to collect vegetation data using the Braun-Blanquet cover abundance scale. Soil samples were also collected to determine the impact of fire on below-ground nitrogen (N) and carbon (C) stocks and ratios. The results indicate that there is no difference between the plant communities of the firebreaks and the adjacent grassland; however, there are certain species that are favoured by firebreaks and others by the adjacent grassland. There is also no difference in diversity between the firebreaks and adjacent grassland areas. Carbon and nitrogen stocks as well as C:N ratios did not differ significantly between the firebreaks and the adjacent grassland plots although trends indicate a decline in both C and N with repeated burning. Full article

Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread. Full article