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Masters Degrees (Geography)

Permanent URI for this collectionhttps://hdl.handle.net/10413/6616

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Browsing Masters Degrees (Geography) by Author "Adjorlolo, Clement."

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    Estimating woody vegetation cover in an African Savanna using remote sensing and geostatistics.
    (2008) Adjorlolo, Clement.; Mutanga, Onisimo.
    A major challenge in savanna rangeland studies is estimating woody vegetation cover and densities over large areas where field based census alone is impractical. It is therefore crucial that the management and conservation oriented research in savannas identify data sources that provides quick, timely and economical means to obtain information on vegetation cover. Satellite remote sensing can provide such information. Remote sensing investigations, however, require establishing statistical relationships between field and remotely sensed data. Usually regression is the empirical method applied to field and remotely sensed data for the spatial estimation of woody vegetation variables. Geostatistical techniques, which take spatial autocorrelation of variables into consideration, have rarely been used for this purpose. We investigated the possibility of improving woody biomass predictions in tropical savannas using cokriging. Cokriging was used to evaluate the cross-correlated information between SPOT (Satellites Pour l’Observation de la Terre or Earth-observing Satellites)-derived vegetation variables and field sampled woody vegetation percentage canopy cover and density. The main focus was to estimate woody density and map the distribution of woody cover in an African savanna environment. In order to select the best SPOT-derived vegetation variable that best correlate with field sampled woody variables, several spectral vegetation and texture indices were evaluated. Next, variogram models were developed: one for woody canopy cover and density, one for the best SPOT-derived vegetation variable, and a crossvariogram between woody variables and best SPOT-derived data. These variograms were then used in cokriging to estimate woody density and map its spatial distribution. Results obtained indicate that through cokriging, the estimation accuracy can be improved compared to ordinary kriging and stepwise linear regression. Cokriging therefore provided a method to combine field and remotely sensed data to accurately estimate woody cover variables.
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    Integrating remote sensing and geostatistics in mapping Seriphium plumosum (bankrupt bush) invasion.
    (2016) Mashalane, Morwapula Jurrian.; ; Odindi, John Odhiambo.; Adjorlolo, Clement.
    The impacts of plant species invasion in natural ecosystems have attracted geo-scientific studies globally. Several studies have demonstrated that the effects of invasive species can permanently alter an ecosystem structure and affect its provision of goods and services, e.g. the provision of food and fibre, aesthetics, recreation and tourism, and regulating the spread of diseases. Plant invasion causes transformation of ecosystems including replacement of native vegetation. This study focuses on invasive plant impacting on grasslands called Seriphium plumosum. The plant is known to have allelopathic effects, killing grass species and turning grazing lands into degraded shrublands. The major challenge in grassland management is the eradication and management of S. plumosum. Central to this challenge is locating, mapping and estimating the invasion status/cover over large areas. Remote sensing based earth observation approaches offer a viable method for invasion plants mapping. Moreover, mapping of vegetation requires robust statistical analysis to determine relationships between field and remotely sensed data. Such relationships can be achieved using spatial autocorrelation. In this study, Getis statistics transformed images and geostatistical techniques, which involve modelling the spatial autocorrelation of canopy variables have been used in mapping S. plumosum. Getis statistics was used to transform SPOT (Satellites Pour l’Observation de la Terre)-6 image bands into spatially dependent Getis indices layer variables for mapping S. plumosum. Stepwise multiple Regression, ordinary kriging and cokriging were used to evaluate the cross-correlated information between SPOT6-derived Getis indices transformed layer variables and field sampled S. plumosum canopy density and percentage. To select the best SPOT6-derived Getis indices to map S. plumosum, 308 spectral Getis indices transformed layer variables were statistically evaluated. Results indicated that Rook, Positive and Horizontal Getis indices are most suitable for mapping S. plumosum with 0.83, 0.828 and 0.828 importance. The most accurate Getis index obtained using 5x5 (Lag 5) moving window yielded 0.83 mapping importance. Cokriging with the most important Getis index yielded the best in S. plumosum density prediction with root mean square error (RMSE) of 25.8 compared to ordinary kriging with RMSE of 26.1 and regression with RMSE of 35.6. This study demonstrated that Getis statistics and geostatistics were successful in mapping and predicting S. plumosum. The current study provides insights critical for developing sound framework for planning and management of S. plumosum in agro-ecological systems.