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Environmental Science

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Browsing Environmental Science by Author "Adjorlolo, Clement."

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    Assessing developmental footprint within an agricultural system using multi-temporal remotely sensed data.
    (2014) Dlamini, Zibusiso Nelson.; Odindi, John Odhiambo.; Adjorlolo, Clement.
    The advent of the new political dispensation in South Africa has seen an exponential growth in the rate of land transformation and encroachment by other land uses into agricultural land in the uMngeni Local Municipality. Accurate evaluation of the rate of transformation is necessary for effective monitoring and management of the natural agricultural resources. In this regard, the use of multi-temporal remote sensing data provides efficient and cost-effective method. The current research assesses the extent to which the development footprint in uMngeni Local Municipality has affected agricultural land categories or zones, using multi-temporal remote sensing data. The study endeavoured to map and quantify the magnitude of change in built-up land cover and other infrastructure by focusing on two time intervals: the periods from 1993 – 2003 and 2003 – 2013. Medium spatial resolution Landsat image data acquired for these periods were analysed to classify and extract the built-up features to appraise the level of change. Results revealed positive change in built-up infrastructure: ~13% increase between 1993 and 2003, ~38% increase from 2003 – 2013, with overall ~32% for the 20 years (1993 – 2013) period under consideration. Next, factors possibly contributing to the encroachment of other land uses into the agricultural landscape and the potential threats to the sustainability of the agricultural system are highlighted.
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    Remote sensing of the distribution and quality of subtropical C3 and C4 grasses.
    (2013) Adjorlolo, Clement.; Mutanga, Onisimo.; Cho, Moses Azong.
    Global climate change is expected to be accompanied by changes in the composition of plant functional types. Such changes are predicted to follow shifts in the percentage cover and abundance of grass species, following the C3 and C4 photosynthetic pathways. These two groups differ in a number of physiological, structural and biochemical aspects. It is important to measure these characteristic properties because they affect ecosystem processes, such as nutrient cycling. High spectral and spatial resolution remote sensing systems have been proven to offer data, which can be used to accurately detect, classify and map plant species. The major challenge, however, is that the spectral reflectance data obtained over many narrow contiguous channels (i.e. hyperspectral data) represent multiple classes that are often mixed for a limited training-sample size. This is commonly referred to as the Hughes phenomenon or “the curse of dimensionality”. In the context of hyperspectral data analysis, the Hughes phenomenon often introduces a high degree of multicollinearity, which is caused by the use of highly-correlated spectral predictors. Multicollinearity is a prominent problem in processing hyperspectral data for vegetation applications, due to similarities in the spectral reflectance properties of biophysical and biochemical attributes. This study explored an innovative method to solve the problems associated with spectral dimensionality and the related multicollinearity, by developing a user-defined inter-band correlation filter function to resample hyperspectral data. The proposed resampling technique convolves the spectral dependence information between a chosen band-centre and its shorter and longer wavelength neighbours. The utility of the new resampling technique was assessed for discriminating C3 (Festuca costata) and C4 (Themeda triandra and Rendlia altera) grasses and for predicting their nutrient content (nitrogen, protein, moisture, and fibre), using partial least squares and random forest regressions. In general, results obtained showed that the user-defined inter-band correlation filter technique can mitigate the problem of multicollinearity in both classification and regression analyses. Wavebands in the shortwave infrared region were found to be very important in regression and classification analyses, using field spectra-only datasets. Next, the analyses were up-scaled from field spectra to the new generation multispectral satellite, WorldView-2 imagery, which was acquired for the Cathedral Peak region of the Drakensberg Mountains. The results obtained, showed that the WV2 image data contain useful information for classifying the C3 and C4 grasses and for predicting variability in their nitrogen and fibre concentrations. This study makes a contribution by developing a user-defined inter-band correlation filter to resample hyperspectral data, and thereby mitigating the high dimensionality and multicollinearity problems, in remote sensing applications involving C3 and C4 grass species or communities.