Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1974. / Bibliography: leaves 322-332. / xvii, 332 leaves ill. (some col.), maps

Thesis (Ph.D.)--University of Wyoming, 2009. / Title from PDF title page (viewed on Apr. 15, 2010). Includes bibliographical references.

Thesis (M.S.)--University of Pretoria, 2004. / Title from PDF t.p. (viewed Mar. 10, 2005). Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 275-295).

Thesis (Ph. D.)--Uppsala University, 1985. / Includes bibliographical references (p. 103-110).

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Resource Conservation Biology. Johannesburg, 29 May 2017. / An understanding of animal foraging behaviour is key to proper management strategies that ensure the survival and species persistence within nature reserves. Here the foraging behaviour of ruminant (hartebeest and wildebeest) and non-ruminant (zebra) grazers were observed and compared between two areas with distinctively different vegetation structure, the natural vegetation (NL) and previously cultivated land (PCL), in Telperion and Ezemvelo Nature Reserves (TENR). Natural vegetation was dominated by tall grass of low greenness with patches of short to very short grass, while the PCL was dominated by areas of very short to short grass (grazing lawns) with patches of medium to tall grass. Step rate (SR) and foraging time spent per feeding station (FTFS) were used as indices of foraging behaviour. I also measured the characteristics of the grass sward (grass height and greenness) grazed on by the three species. Both ruminants had high SR and low FTFS. Despite having similar SR and FTFS, ruminants grazed on grass of different height. Hartebeest preferred tall grass with low greenness content (0-10%), while wildebeest preferred short to very short grass and were significantly selective of areas with relative high greenness (11-50%) on PCL, more so than any other species. Compared to ruminant grazers the non-ruminant (zebra) had low SR and high FTFS and like hartebeest they grazed on medium to tall grass of very low greenness content (0 10%). This study did not reveal any difference in feeding behaviour within species between the two study sites. The finding of this study confirms that ruminant and non-ruminant species have different foraging behaviour, and habitat heterogeneity is necessary for the reserve to support different grazing species. Key words: digestive physiology, feeding station, step rate, wildebeest, hartebeest, zebra / GR2018

Thesis (M.S.)--Oregon State University, 2006. / Printout. Includes bibliographical references (leaves 27-31). Also available on the World Wide Web.

A dissertation submitted to the faculty of Science, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science. / Unable to load abstract.

Thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy. School of Animal, Plant and Environmental Science, University of the Witwatersrand, Johannesburg, South Africa. October 2016. / Mining has negative impacts on the environment in many different ways. One method developed to quantify some of these impacts is Landscape Function Analysis (LFA) and this has been accepted by some mining companies and regulators. In brief, LFA aims at quantifying the organization of vegetative and landscape components in a landscape into patches along a transect and quantifying, in a relative manner, three basic processes important to landscape functioning, namely: soil stability or susceptibility to erosion, infiltration or runoff, and nutrient cycling or organic matter decomposition. However, LFA is limited in large heterogeneous environments, such as those around mining operations, due to its localized nature, and the man hours required to collect a representative set of measurements for such large and complex environments. Remote sensing using satellite-acquired data can overcome these limitations by sampling the entire environment in a rapid and objective manner. What is required is a method of connecting these satellite-based measurements to LFA measurements and then being able to extrapolate these measurements across the entire mine surface. The aim of this research was to develop a method to use satellite-based hyperspectral imagery to predict landscape function analysis (LFA) using partial least squares regression (PLSR). This was broken down into three objectives: (1) Collection of the LFA data in the field and validation of the LFA indices against other environmental variables collected at the same time, (2) validation of PLSR models predicting LFA indices and various environmental variables from ground-based spectra, and (3) production of risk maps based on predicting LFA indices and above-ground biomass using PLSR models and Hyperion satellite-based hyperspectral imagery. Although the study was based in grasslands at two mining regions, West Wits and Vaal River, a suitable Hyperion image was only available for Vaal River. A minimum of 374 points were sampled for LFA indices, ground-based spectra, above-ground biomass and soil cores along 2880 m of LFA transect from both mine sites. Soil cores were weighed fresh before sieving with a 2 mm sieve to separate root and stone fractions. The sieved soil fraction was tested for pH, EC, SOM, and for the West Wits samples, organic nitrogen and total extractable inorganic nitrogen. There was one modification to the LFA method where grass patches were collapsed into homogenous units as it was deemed not feasible to sample 180 m transects at grass tuft scales of 10 – 30 cm, but other patch definitions followed the LFA manual (Tongway and Hindley, 2004). Evidence suggested that some of the different patch types, in particular the bare/biological soil crust – bare grass – sparse grass patch types, represented successional stages in a continuum although this was not conclusive. There also was evidence that the presence or absence of cattle play a role in some processes active in these grasslands and erosion is mainly through deflation, rain splash and sheet wash. Generally the environmental variables supported the LFA indices although the nutrient cycling index was representative of above-ground nutrient cycling but not below-ground nutrient cycling. Models derived with PLSR to predict the LFA indices from ground-based spectral measurements were strong at both mine sites (West Wits: LFA stability r2 = 0.63, P < 0.0001; LFA infiltration r2 = 0.75, P < 0.0001; LFA nutrient cycling r2 = 0.73, P < 0.0001; Vaal River: LFA stability r2 = 0.39, P < 0.0001, LFA infiltration r2 = 0.72, P < 0.0001, LFA nutrient cycling r2 = 0.54, P < 0.0001), as were PLSR models predicting above-ground biomass (West Wits above-ground biomass r2 = 0.55, P = 0.0003; Vaal River above-ground biomass r2 = 0.79, P < 0.0001) and soil moisture (West Wits soil moisture r2 = 0.45, P = 0.0017; Vaal River soil moisture r2 = 0.68, P < 0.0001). However, for soil organic matter (r2 = 0.50, P < 0.0001) and EC (r2 = 0.63, P < 0.0001), Vaal River had strong prediction models while West Wits had weak models for these variables (r2 = 0.31, P = 0.019 and r2 = 0.10 and P < 0.18, respectively). For EC, the wide range of soil values at Vaal River in association with gypsum crusts, and low values throughout West Wits explained these model results but for soil organic matter, no clear explanation for these site differences was identified. Patch-based models could accurately discriminate between spectrally well-defined patch types such S. plumosum patches but were less successful with patch types that were spectrally similar such as the bare/biological soil crust – bare grass – sparse grass patch continuum. Clustering similar patch types together before PLSR modelling did improve these patch-based spectral models. To test the method proposed to predict LFA indices from satellite-based hyperspectral imagery, a Hyperion image matching 6 transects at Vaal River was acquired by NASA’s EO-1 satellite and downloaded from the USGS Glovis website. LFA transects were partitioned to match and extract pixel spectra from the Hyperion data cube. Thirty-one spectra were separated into calibration (20) and validation (11) data. PLSR models were derived from the calibration data, tested with validation data to select the optimum model, and then applied to the entire Hyperion data cube to produce prediction maps for five LFA indices and above-ground biomass. The patch area index (PAI) produced particularly strong models (r2 = 0.79, P = 0.0003, n =11) with validation data, whereas the landscape organization index (LOI) produced weak models. It is argued that this difference between these two essentially similar indices is related to the fact that the PAI is a 2-dimensional index and the LOI is a 1-dimensional index. This difference in these two indices allowed the PAI to compensate for some burned pixels on the transects by “seeing” the density pattern of grass tufts and patches whereas the linear nature of the LOI was more susceptible to the changing dimensions of patch structure due to the effects of fire. Although validation models for the three LFA indices of soil stability, infiltration and nutrient cycling were strong (r2 = 0.72, P = 0.004; r2 = 0.66, P = 0.008; r2 = 0.70, P = 0.005, n = 9 respectively), prediction maps were confounded by the presence of fire on some transects. The poor quality of the Hyperion imagery also meant great care had to be taken in the selection of models to avoid poor quality prediction maps. The 31 bands from the VNIR (478 – 885 nm) portion of the Hyperion spectra were generally the best for PLSR modelling and prediction maps, presumably because of better signal-to-noise ratios due to higher energy in the shorter wavelengths. With two satellite-based hyperspectral sensors already operational, namely the US Hyperion and the Chinese HJ-1A HSI, and a number expected to be launched by various space agencies in the next few years, this research presents a method to use the strengths of LFA and hyperspectral imagery to model and predict LFA index values and thereby produce risk maps of large, heterogeneous landscapes such as mining environments. As this research documents a method of partitioning the landscape rather than the pixel spectra into pure endmembers, it makes a valuable contribution to the fields of landscape ecology and hyperspectral remote sensing. / LG2017

Ph.D., Faculty of Science, University of the Witwatersrand, 2000