ETD Collection

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    Using spatial rainfall and products from the MODIS sensor to improve an existing maize yield estimation system
    (2008-08-07T13:54:35Z) Frost, Celeste
    Abstract After deregulation of the agricultural markets in South Africa in 1997, the estimated maize crop could no longer be verified against the actual crop, due to the lack of control data from the Maize Control Board. This drove the need to explore remotely sensed data as a supplement to the current crop estimation methodology to improve crop estimations. Input data for the development of a Geographic Information System (GIS)-based model consisted of objective yield point data, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) images and rainfall grids. Rainfall grids were interpolated from weather station data. NDVI values were obtained from the MODIS sensor aboard the Terra platform. Objective yield point field survey data for the 2001/2002 growing season were utilised since dry-land or irrigated conditions were recorded for that season. MODIS NDVI values corresponded well with the growing stages and age of the maize plants after being adjusted to reflect the crop’s age rather than the Julian date. Rainfall values were extracted from rainfall grids and also aligned with the age of the maize plants. This is a suggested alternative to the traditional method of using the mean NDVI for several districts in a region over a Julian growing period of 11 months according to Julian dates. South African maize production areas extend over seven (7) provinces with eight (8) different temperature and rainfall zones (du Plessis, 2004). Planting-date zones based on the uniform age of the maize plants were developed from objective yield Global Positioning System (GPS) points for the 2001/2002 growing season and compared with the 2004/2005 growing season (Frost and Kneen, 2006). Planting dates were interpolated from these planting zones for objective yield GPS points which were missing planting dates in the survey database. MODIS imagery is affordable (free) and four (4) images cover the whole of South Africa daily, while one (1) image covers the study area daily. Several recommendations, such as establishing yield equations for a normal, dry, and wet season were made. It is also suggested that dry-land and irrigated areas continue to be evaluated separately in future.
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    Snow cover analysis for the High Drakensberg through remote sensing: Environmental implications
    (2008-05-22T11:27:46Z) Mulder, Nicholas Andrew Maurits
    Snow occurs in the High Drakensberg of southern Africa approximately eight times per annum. Snow cover is frequently captured by Landsat satellite imagery, which provide data for the monitoring of snow cover in other regions of the world. Together with a digital elevation model, repetitive snow cover data are used to analyse the distribution of snow cover in the High Drakensberg study area. The effect that the regional and local topography, latitude, and climatic conditions have on the spatial distribution of snow and the function that temperature, wind, altitude, aspect and slope gradient play in the preservation of snow cover are examined. The results of the spatial study allow for the identification of sites that support the accumulation of snow. Specific active and relict geomorphological features were surveyed and correlated spatially to the contemporary snow cover. Among such features are linear debris ridges on south-facing valley slopes in the High Drakensberg. These appeared similar to glacial features found elsewhere in the world and are thus significant in a long-standing and highly conjectured debate over the validity of possible plateau, cirque and niche glaciation in the region. Late-lying snow cover favours gently sloping south- and southeast-facing aspects at altitudes from 3000 m ASL to just below the highest peaks in the region near 3450 m ASL, above which higher insolation levels on the flat mountain summits provides unfavourable conditions. Snow cover immediately adjacent to the Drakensberg escarpment ablates quickly whilst snow cover at high altitudes in the Lesotho interior experiences better preservation conditions. Latitude has no obvious impact on the distribution of snow cover due to the dominant role of topography in the High Drakensberg other than a limiting of snowfall to regions south of 29°S in late spring. Various synoptic conditions produce snowfall in the region, with cold fronts associated with midlatitude cyclones producing the majority of snow cover. A strong correlation exists between the spatial distribution of snow cover and specific geomorphological features. Observed linear debris ridges are located on slopes that experience frequent contemporary snow cover, lending credence for a glacial origin of the ridges during a period of colder environmental conditions.
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    Ground vegetation biomass detection for fire prediction from remote sensing data in the lowveld region
    (2007-02-26T13:38:19Z) Goslar, Anthony
    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.