Thesis (PhD)(Botany))-Universiteit van Pretoria, 1992. / Summary in English and Afrikaans. Includes bibliographical references.

Thesis (M.S.)--Oregon State University, 2005. / Printout. Includes bibliographical references (leaves 77-82). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [248]-277).

Thesis (doctoral)--University of Tartu, 2005. / Vita. Includes bibliographical references.

Thesis (M.S.)--Oregon State University, 1983. / Typescript (photocopy). Includes bibliographical references (leaves 76-80). Also available via the World Wide Web.

Thesis (M.S.)--Oregon State University, 2003. / Typescript (photocopy). Includes bibliographical references (leaves 100-103). Also available on the World Wide Web.

No description available.

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science (Geographical Information Systems and Remote Sensing) at the School of Geography, Archaeology & Environmental Studies) Johannesburg. 2017 / Vegetation mapping using remote sensing is a key concern in environmental application using remote sensing. The new high resolution generation has made possible, the mapping of spatial distribution of vegetation communities. The aim of this research is to test the use of new generation multispectral data for vegetation classification in Ezemvelo Game Reserve, Bronkhorspruit. Sentinel-2 and RapidEye images were used covering the study area with nine vegetation classes: eight from grassland (Mixed grassland, Wetland grass, Aristida congesta, Cynadon dactylon, Eragrostis gummiflua, Eragrostis Chloromelas, Hyparrhenia hirta, Serephium plumosum) and one from woodland (Woody vegetation). The images were pre-processed, geo-referenced and classified in order to map detailed vegetation classes of the study area. Random Forest and Support Vector Machines supervised classification methods were applied to both images to identify nine vegetation classes. The softwares used for this study were ENVI, EnMAP, ArcGIS and R statistical packages (R Development Core, 2012) .These were used for Support Vector Machines and Random Forest parameters optimization. Error matrix was created using the same reference points for Sentinel-2 and RapidEye classification. After classification, results were compared to find the best approach to create a current map for vegetation communities. Sentinel-2 achieved higher accuracies using RF with overall accuracy of 86% and Kappa value of 0.84. Sentinel-2 also achieved overall accuracy of 85% with a Kappa value of 0.83 using SVM. RapidEye achieved lower accuracies using RF with an overall accuracy of 82% and Kappa value of 0.79. RapidEye using SVM produced overall accuracy of 81% and a Kappa value of 0.79. The study concludes that Sentinel-2 multispectral data and RF have the potential to map vegetation communities. The higher accuracies achieved in the study can assist management and decision makers on assessing the current vegetation status and for future references on Ezemvelo Game Reserve. Keywords Random forest, Support Vector Machines, Sentinel-2, RapidEye, remote sensing, multispectral, hyperspectral and vegetation communities / LG2018

Thesis (Dr. rer. nat.)--Universität Regensburg, 2006. / Title from PDF title page (viewed on Apr. 25, 2007). Includes bibliographical references (p. 169-185).

Thesis (M.S.)--University of Pretoria, 1999. / Includes bibliographical references (leaves 122-135).