Estimating woody vegetation cover in an African Savanna using remote sensing and geostatistics.
| dc.contributor.advisor | Mutanga, Onisimo. | |
| dc.contributor.author | Adjorlolo, Clement. | |
| dc.date.created | 2008 | |
| dc.date.issued | 2008 | |
| dc.description | Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2008. | en_US |
| dc.description.abstract | 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. | |
| dc.identifier.uri | http://hdl.handle.net/10413/420 | |
| dc.language.iso | en | en_US |
| dc.subject | Savanna ecology--South Africa--Kruger National Park--Geographical distribution--Remote sensing. | en_US |
| dc.subject | Savanna ecology--South Africa--Kruger National Park--Geographical distribution--Statistical methods. | en_US |
| dc.subject | Woody plants--South Africa--Kruger National Park--Geographical distribution--Remote sensing. | en_US |
| dc.subject | Woody plants--South Africa--Kruger National Park--Geographical distribution--Statistical methods. | en_US |
| dc.subject | Kriging. | en_US |
| dc.subject | Theses--Geography. | en_US |
| dc.title | Estimating woody vegetation cover in an African Savanna using remote sensing and geostatistics. | en_US |
| dc.type | Thesis | en_US |