Student Number : 0310612G - MSc research report - School of Geography, Archaeology and Environmental Studies - Faculty of Science / Wildfire prediction and management is an issue of safety and security for many rural communities in South Africa. Wildfire prediction and early warning systems can assist in saving lives, infrastructure and valuable resources in these communities. Timely and accurate data are required for accurate wildfire prediction on both weather conditions and the availability of fuels (vegetation) for wildfires. Wildfires take place in large remote areas in which land use practices and alterations to land cover cannot easily be modelled. Remote sensing offers the opportunity to monitor the extent and changes of land use practices and land cover in these areas. In order for effective fire prediction and management, data on the quantity and state of fuels is required. Traditional methods for detecting vegetation rely on the chlorophyll content and moisture of vegetation for vegetation mapping techniques. Fuels that burn in wildfires are however predominantly dry, and by implication are low in chlorophyll and moisture contents. As a result, these fuels cannot be detected using traditional indices. Other model based methods for determining above ground vegetation biomass using satellite data have been devised. These however require ancillary data, which are unavailable in many rural areas in South Africa. A method is therefore required for the detection and quantification of dry fuels that pose a fire risk. ASTER and MAS (MODIS Airborne Simulator) imagery were obtained for a study area within the Lowveld region of the Limpopo Province, South Africa. Two of the ASTER and two of the MAS images were dated towards the end of the dry season (winter) when the quantity of fuel (dry vegetation) is at its highest. The remaining ASTER image was obtained during the middle of the wet season (summer), against which the results could be tested. In situ measurements of above ground biomass were obtained from a large number of collection points within the image footprints. Normalised Difference Vegetation Index and Transformed Vegetation Index vegetation indices were calculated and tested against the above ground biomass for the dry and wet season images. Spectral response signatures of dry vegetation were evaluated to select wavelengths, which may be effective at detecting dry vegetation as opposed to green vegetation. Ratios were calculated using the respective bandwidths of the ASTER and MAS sensors and tested against above ground biomass to detect dry vegetation. The findings of this study are that it is not feasible, using ASTER and MAS remote sensing data, to estimate brown and green vegetation biomass for wildfire prediction purposes using the datasets and research methodology applied in this study. Correlations between traditional vegetation indices and above ground biomass were weak. Visual trends were noted, however no conclusive evidence could be established from this relationship. The dry vegetation ratios indicated a weak correlation between the values. The removal of background noise, in particular soil reflectance, may result in more effective detection of dry vegetation. Time series analysis of the green vegetation indices might prove a more effective predictor of biomass fuel loads. The issues preventing the frequent and quick transmission of the large data sets required are being solved with the improvements in internet connectivity to many remote areas and will probably be a more viable path to solving this problem in the near future.

Research Doctorate - Doctor of Philosophy (PhD) / Amidst growing concerns about global warming, efforts to reduce atmospheric CO2 concentrations (i.e. C sequestration) have received widespread attention. One approach to C sequestration is to increase the amount of C stored in terrestrial ecosystems, through improved land management. Terrestrial ecosystems represent a critical element of the C interchange system, however a lack of understanding of the C cycle at regional and sub-regional scales means that they represent a source of primary uncertainty in the overall C budget. This thesis aims to address this deficiency by developing an understanding of catchment-scale processes critical for accurate quantification of C in the landscape. An investigation into the spatial and temporal dynamics of soil organic carbon (SOC) was conducted for a 150ha temperate grassland catchment in the Upper Hunter Valley, New South Wales, Australia. The major factors controlling the movement, storage, and loss of SOC were investigated, including climate, vegetation cover, soil redistribution processes, topography, land use, and soil type. This study falls into four broad areas. In the first part of this study the spatio-temporal dynamics of soil moisture and temperature at the catchment scale are assessed for a range of soil depths. Data recorded from a network of monitoring sites located throughout the study catchment was compared with independently derived soil moisture and temperature data sets. The data indicates that soil moisture and temperature in surface soil layers were highly dynamic, in their response to rainfall and incoming solar radiation, respectively. Deeper soil layers however were less dynamic, with longer lag times observed with increasing soil depth, as topography, soil type, and landscape position were the dominant controlling factors. Climate related variables are important factors affecting plant growth and net primary productivity. The second part of the study quantified spatial and temporal vegetation patterns using both field-based measurements of above-ground biomass and remotely sensed vegetation indices from the MODIS and Landsat TM 5 platforms. A strong and statistically significant relationship was found between climate variables and MODIS derived NDVI, leading to the development of a predictive vegetation cover model using ground-based soil moisture, soil temperature, and sunshine hours data. The ability of remotely sensed data to capture vegetation spatial patterns was found to be limited, while it was found to be a good predictor of temporal above-ground biomass trends, enabling net primary productivity to be quantified over the three-year study period. In the third part of the thesis soil redistribution patterns and erosion rates were quantified using the caesium-137 method and empirical and physically-based modelling approaches. The impact of soil redistribution processes on SOC distribution was investigated, and the amount of erosion derived SOC loss quantified. A significant proportion of SOC stored within the catchment was found below a soil depth of 0.30m, which is the depth of sampling set out in the IPCC and Australian Greenhouse Office guidelines for carbon accounting. Soil depth was identified as a key factor controlling the spatial distribution of SOC, which is in turn determined by position in the landscape (i.e. topography). The fourth and final part of the study describes how data on erosion derived SOC loss were used in conjunction with net primary productivity estimates, to establish a SOC balance. This involved mapping the spatial distribution of SOC using a high resolution digital elevation model of the catchment, in conjunction with soil depth measurements, and quantifying the total SOC store of the catchment. It was observed that temporal changes in SOC were minimal over the limited three-year study period, however, the continuity of catchment management practices over the previous decades suggest that steady-state conditions have perhaps been reached. The study concludes that the key to increasing the amount of SOC and enhancing carbon sequestration in the soil, is to increase the amount of SOC stored at depth within the soil profile, where factors such as soil moisture and temperature, which control decomposition rates, are less dynamic in space and time, and where SOC concentrations will be less vulnerable to changes occurring at the surface in response to global warming and climate change.