Thesis (M.S.)--University of Wisconsin--Madison. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 176-183).

Thesis (Ph. D.)--Oregon State University, 2006. / Printout. Includes bibliographical references (leaves 144-157). Also available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 71-75). Also available on the World Wide Web.

Thesis (M.S.)--Oregon State University, 2006. / Printout. Includes bibliographical references (leaves 145-151). Also available on the World Wide Web.

Concommittant with an increasing trend towards the ecological classification of forest land in British Columbia is the need for more detailed vegetation inventories and larger mapping scales. Although existing classification schemes (biogeoclimatic, provincial biophysical and habitat type classification) usually present a useful initial stratification of broad zonal vegetation patterns, they seldom provide, or were intended to provide a classification suitable for detailed vegetation inventory and mapping in a particular study area. In most instances, primary vegetation data must be collected and classified at a level of detail compatible with the scale of mapping and the variability of the vegetation landscape. Limited access and steep mountainous terrain are additional problems contributing to the acquisition, classification, interpretation and mapping of vegetation at large scales. Dissimilarity Analysis is a numerical classification analysis programmed and studied by the provincial government as a means to stratify large volumes of vegetation data in a relatively objective and efficient manner. As a divisive-polythetic classification strategy it demonstrates several advantages over other numerical analyses. Although it is now used as a routine analysis by the provincial biophysical survey, it has not yet been thoroughly evaluated or formally presented with regard to its suitability for vegetation classification and mapping on an operational basis. This study investigated four related questions: a. What methods can be employed for detailed vegetation mapping (scale 1:15,840) in mountainous terrain with limited access? b. What is the value of Dissimilarity Analysis for the classification of vegetation in primary survey? c. What is the predictive capability of the pretyping (prestratification) approach developed for vegetation mapping? d. What is the reliability of the vegetation maps. The study was divided into two separate but related investigations: the operational classification and mapping of vegetation in two small mountainous watersheds and a detailed systematic sampling study of two representative areas within one of the watersheds to assess the vegetation mapping procedure and map reliability. A detailed vegetation mapping procedure was developed which utilized permanent physiographic landscape features directly observable or inferred from black and white stereo aerial photographs (scale 1:15,840), macro and meso physiognomic vegetation features, a simple concept relating the above features to the available moisture for vegetation, and information about existing vegetation (e.g. forest cover maps; concepts and maps of vegetation zonation). Dissimilarity Analysis was found to be an objective and efficient method of vegetation stratification by reducing personal bias and ensuring an optimum and consistent utilization of the available information in the data set. It was felt to be an appropriate technique for stratifying primary vegetation data since it maximizes differences between groups, defines limits to classes and facilitates the formation of a hierarchical identification procedure. It was concluded that the vegetation pretyping approach developed for operational mapping provided a methodical, preliminary stratification of the landscape upon which improved mapping criteria could be added to better predict present vegetation condition. A quantitative assessment of map reliability in two representative areas of one of the watersheds resulted in a value of 79% relative to an independent chance of agreement of 6.2% and an optimum chance of agreement of 29%. It was felt that these values were representative of the map reliability in the remainder of the watershed. / Forestry, Faculty of / Graduate

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Geographical Information Systems and Remote Sensing, 2019 / Prosopis species (also known as Mesquite), in particular P. glandulosa (Honey Mesquite) have a negative impact on indigenous biodiversity and the livelihood of communities in the semi-arid and arid parts of South Africa. The spread of these species is a threat to the environments in which they have been introduced as they spread at high rates, increase the mortality of indigenous trees and disrupt important ecosystem processes such as hydrological and nutrient cycles. Due to the negative impacts of Prosopis on important ecosystem services and South Africa’s native biodiversity, it is essential for the distribution of these species to be identified, controlled and monitored in order to mitigate their spread and restore damaged ecosystems. The objectives of this study were to use Remote Sensing and Geographic Information Systems (GIS) tools to: (i) delineate the distribution of Prosopis using high resolution satellite imagery, (ii) determine the changes in spatial distribution of these species in the period 2003-2017, and (iii) use moderate spatial resolution satellite imagery and ancillary environmental data to predict areas susceptible to future invasion.. The study area used in this investigation is the Square Kilometre Array (SKA SA) site, situated in Northern Cape Province, South Africa. Satellite images were classified using Multi-layer Perceptron (MLP) Neural Network classification algorithm to improve the land use land cover classification accuracy. A WordView-3 image with 1.24 m spatial resolution was used to delineate the distribution of Prosopis in the study area for the year 2016. Landsat images from the years 2003, 2008, 2013 and 2017 were used to conduct a change detection analysis. The prediction model developed in the study was able to predict Prosopis cover for the years 2017 and 2022 cover using ancillary environmental data and land use land cover maps. The study was also able to quantify the area covered by Prosopis species for the years 2017 and 2022. / XN2020

No description available.

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016. / Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-6

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 (Ph. D.)--Oregon State University, 2003. / Typescript (photocopy). Includes bibliographical references. Also available online.