Browsing by Subject "fire"

Now showing 1 - 4 of 4
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    Open Access
    Do trees suppress grass fuel loads? : canopy cover effects in South African savannas
    (2011) Donaldson, Jason
    Continental scale analysis of the savanna biome indicated that fire did not spread at tree canopy cover above 40%. This study investigates this relationship in a field study. It is possible that the type of tree (forest vs. savanna) may influence the amount of shade experienced by the understory and therefore this study also explores differences in LAI between congeneric pairs of forest and savanna tree species. Data were collected in two major South African savanna parks. Plots were set out to measure grass biomass in reference to canopy cover in both Kruger National Park (n=60) and the Hluhluwe-iMfolozi Game Reserve (n=82). Seven congeneric pairs were selected to compare leaf area and LAI between forest and savanna tree species using a destructive method. Against expectations, it was only when canopy cover reached 80% that grass fuel load was too low to support fire spread in all Kruger National Park plots (Pr=O) and 89% of the Hluhluwe-iMfolozi Game Reserve plots (Pr=0.11). No consistent, general relationships were evident with leaf area or LAI in comparisons between forest-savanna congeneric pairs. The significance of these findings and future direction is discussed.
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    Open Access
    EFFECTS OF FIRE AND HERBIVORY ON THE STABILITY OF SAVANNA ECOSYSTEMS
    (2003) Van Langevelde, Frank; Van De Vijver, Claudius A D M; Kumar, Lalit; Van De Koppel, Johan; De Ridder, Nico; Van Andel, Jelte; Skidmore, Andrew K; Hearne, John W; Stroosnijder, Leo; Bond, William J; Prins, Herbert H T; Rietkerk, Max
    Savanna ecosystems are characterized by the co-occurrence of trees and grass-es. In this paper, we argue that the balance between trees and grasses is, to a large extent, determined by the indirect interactive effects of herbivory and fire. These effects are based on the positive feedback between fuel load (grass biomass) and fire intensity. An increase in the level of grazing leads to reduced fuel load, which makes fire less intense and, thus, less damaging to trees and, consequently, results in an increase in woody vegetation. The system then switches from a state with trees and grasses to a state with solely trees. Similarly, browsers may enhance the effect of fire on trees because they reduce woody biomass, thus indirectly stimulating grass growth. This consequent increase in fuel load results in more intense fire and increased decline of biomass. The system then switches from a state with solely trees to a state with trees and grasses. We maintain that the interaction between fire and herbivory provides a mechanistic explanation for observed discontinuous changes in woody and grass biomass. This is an alternative for the soil degradation mechanism, in which there is a positive feedback between the amount of grass biomass and the amount of water that infiltrates into the soil. The soil degradation mechanism predicts no discontinuous chang-es, such as bush encroachment, on sandy soils. Such changes, however, are frequently ob-served. Therefore, the interactive effects of fire and herbivory provide a more plausible explanation for the occurrence of discontinuous changes in savanna ecosystems.
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    Remote sensing for detecting rapid post-fire recovery as Groundwater-Dependent Ecosystems in the Cape Floristic Region
    (2021) Chenge, Simcelile; Smit, Julian; West, Adam G; Singh, Kaveer
    Groundwater Dependent Ecosystems (GDEs) concentrate high levels of biodiversity and several species not found anywhere else. They prevail in the landscape through the ecological contribution of groundwater. They, GDEs, are vulnerable to drastic changes in groundwater depth. If, for example, bulk groundwater pumping drastically increases the groundwater depth and GDEs can no longer access it, they would die out. In the Cape Floristic Region (CFR), South Africa, there is limited information about the spatial distribution of groundwater dependent ecosystems. With the CFR having multiple locations with current and subsequent bulk groundwater pumping, identifying the spatial distribution of GDEs is a prerequisite for establishing their groundwater requirements. This dissertation presents a proposed novel method to identify rapid recovering wetlands predicted to be GDEs and uses Random Forest (RF) to predict their spatial distribution. The proposed novel approach leveraged the periodic fire disturbances in the CFR and applied the remote sensing index; Normalised Difference Vegetation Index (NDVI) extracted from high spatial resolution (1 m) aerial orthoimages. The proposed novel approach involves three levels of analysis. The first two levels used a one-way Analysis of Variance (ANOVA) to analyse the sensitivity of mean NDVI to discriminate wetland and non-wetland classes in burned and unburned study sites, and a post-hoc test: Tukey's Honest Significant Differences (HSD) pair-wise comparison to detect differences between the wetland and non-wetland mean NDVI and infer an NDVI threshold of wetland classes. In unburned sites, ANOVAshowed no statistical significance between wetland and non-wetland classes, F (2,15) = 3.53, p = 0.055. In burned sites, however, ANOVA showed there was a significant difference between wetland and non-wetland classes, F (2,15) = 9.66, p = 0.002. ANOVA and Tukey showed there were significant differences betweenwetland and non-wetland classes, with wetlands having between 0.22 and 0.37 greater NDVI than non-wetlands. The last level of analysis employed a kernel density estimator function to assess the recovery rate post-burn and use it to detect faster recovery as potential of wetlands to be GDEs; results showed that potential wetland GDEs experience rapid NDVI recovery > 236 days post-fire. In the fire prone CFR, leveraging fire data to detect GDEs provides a potentially simple and efficient way of building a local database for GDEs. The proposed novel approach showed leveraging fire data is a simple alternative to laborious field data to identify and map GDEs in the CFR. But because of the finite spectral bands in aerial orthoimages, Sentinel-2A multi-epochs dataset was utilised to carry out random forest for predicting the spatial distribution of potential wetland GDEs in the Kogelberg Nature Reserve. Sentinel-2A bands: Short-Wave Infrared (SWIR), NearInfrared (NIR), Red-edge, Red, Green, NDVI and Normalised Difference Wetness Index (NDWI) predictors and the potential wetland GDEs/non-wetland classes as the response. I tuned RF using five-fold repeated spatial cross-validation instead of the typical cross-validation tuning to account for the spatial structure of the data. The overall predictive accuracy of RF was between 59%-71%. This predictive accuracy may have been reduced by the application of spatial cross-validation that accounted for the spatial autocorrelation in the multi-date data. The dissertation showed that Sentinel-2A multi-date data applies in predicting the distribution of potential wetland GDEs but might not be effective for smaller (< 100 m2) wetlands. These small wetlands showed rapid post-fire recovery (less than a year post-fire) and were effectively detected with high resolution aerial orthoimages (1 m) spatial resolution.
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    The use of histological methods to distinguish between burned remains of human and non-human bone
    (2022) Sebolai, Masego Jessica; Mole, Calvin
    As part of a medico-legal analysis it is necessary to identify if bone tissue is animal or human in nature. This process is complicated when bone is highly fragmented or burned. Previous research has established the ability to differentiate human from non-human bone histologically, however, further research is necessary to determine if this is still applicable in the case of burned remains. In South Africa, approximately 500 deaths and 15 000 fire related injuries occur annually in Cape Town and such fires ranged between 600°C to 1000°C. The aim of this research was to study the qualitative and quantitative characteristics of femur bone microstructure of human and animal bones exposed to different temperatures and to determine the possibility of distinguishing them. The study consisted of 17 femoral bone samples collected from four different species namely; humans (Homo sapiens), pig (Sus scrofa), wildebeest (Connochaetes gnou) and cow (Bos taurus). Unburned samples were compared to bone samples burned at 600°C, 700°C, 800°C and 900°C in a muffle furnace for 20 minutes. Bone samples were processed into thin sections for histological analysis. During analysis, each bone specimen was divided into four quadrants and two periosteal regions. For histomorphometric analysis, quantitative characteristics were assessed by measuring the area, perimeter, and minimum and maximum diameter of the Haversian system and Haversian canals as well as osteon circularity and osteon density. According to the qualitative results, the main structural bone tissue observed in all quadrants and two periosteal regions of unburned animal bone was primary vascular plexiform bone and irregular Haversian bone. Human bone consisted of dense Haversian bone. Quantitative results indicated a statistically significant difference in most parameters between species within burned as well as unburned samples (p<0.001). Statistically significant differences in quantitative parameters within human and wildebeest bone were noted at different burn temperatures (p<0.001). Overall, the results showed that heat exposure to bones can affect the bones' quantitative and qualitative characteristics but human and non-human bones can still be differentiated. This histological method can be used in forensic fire cases.