Bepaling van gronderosiepotensiaal in die Nsikazi-distrik (Mpumalanga) met afstandwaarneming en GIS
- Authors: Wentzel, Karen Chantal
- Date: 2012-09-12
- Subjects: Soil erosion , Erosion -- Experiments , Remote sensing , Geographic information systems
- Type: Thesis
- Identifier: uj:10207 , http://hdl.handle.net/10210/7581
- Description: M.Sc. , The aim of the present study is to determine the influence of human activities on fluvial erosion in the Nsikazi District (Mpumalanga). Recommendations for the optimal use of available resources in the study area are made after considering the soil erosion potential of the area, and by comparing the Nsikazi District with the nearby conservation control area, the Kruger National Park (KNP). Soil erosion can be optimally prevented by employing the most suitable management practice; therefore reliable information is required concerning the location, causes and extent of soil erosion. During the present study, satellite remote sensing is evaluated as a cost effective and timely source of information to fulfill this requirement. Due to the fact that soil erosion is a natural process, which can be amplified by certain natural physical factors, it is necessary to determine the natural soil erosion potential of an area before any assumptions can be made regarding the cause of soil erosion. In this study the integration of the natural soil erosion potential map and bare soil map resulted in the establishment of the overall soil erosion potential map for the area. Landuse can be described as the most significant contributing factor in the occurrence of bare soil (devoid of vegetation), and therefore this is an indicator not only of soil degradation, but also of human impact. The data used during the present study consist of digital satellite images (Landsat TM and SPOT) and aerial photographs provided by the Institute for Soil, Climate and Water (ISCW). Additional information was also collected from pre-existing soil and topographical maps of the area. The ILWIS 2.1 computer programme was then employed for image processing and GIS analysis of the data. The study was carried out in two analytical phases. A data processing phase, which was carried out as follows: - The physical erosion factors, which determine soil erosion potential, namely soil erodibility, slope, slope length, slope form and slope aspect as well as plant cover, were converted to GIS data layers and mapped , followed by - the identification and mapping of the presence of bare soil and landuse patterns. A data intergration phase was included, whereby the above mentioned data layers were integrated to determine the natural and overall soil erosion potential, as well as the evaluation of landuse, thereby indicating the soil cultivation potential for the study area.
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Identification and mapping of contamination plumes at Venetia Mine utilising remote sensing techniques and geographical information systems as environmental monitoring tools
- Authors: Louw, Ansu
- Date: 2012-02-28
- Subjects: Environmental monitoring , Remote sensing , Geographic information systems
- Type: Mini-Dissertation
- Identifier: uj:2090 , http://hdl.handle.net/10210/4436
- Description: M.A. , The mining industry is an important contributor to the South African economy, but is also a major contributor to surface water and groundwater contamination. It is therefore essential for mining operations to comply with legislative requirements in terms of preventing and/or rehabilitating areas impacted by mining activities. Ensuring this compliance requires effective and frequent monitoring of impacts and associated rehabilitation caused by mining operations. Remote sensing and geographical information systems (GIS) offer innovative tools for executing effective monitoring efforts related to mining impacts; and although they do not replace conventional methods of environmental monitoring, these tools can enhance the analysis and decision-making of such efforts. This study is aimed at identifying and mapping the extent and direction of flow of contamination plumes originating from slimes and tailings dams at the Venetia Mine in the Limpopo Province by using remote sensing and GIS tools to illustrate these tools’ effectiveness and to create a simplified database and support system for continued monitoring. With the implementation of a number of image enhancement techniques such as the false-colour composite, the Tasselled Cap transformation and the normalised difference vegetation index (NDVI) on LANDSAT satellite imagery; together with the interpolation of borehole water quality data, contamination plumes could be mapped and interpreted. The results and interpretation of the processed satellite imagery (indicating a decrease in plant growth from 2001 to 2006) were verified by the results of the interpolated water quality data (which indicated high concentrations of total dissolved solids in 2006). The final reclassified image of the NDVI provided a simplified version of the findings which could be presented to the laymen whereby decision-making could be augmented. Accordingly, the study concluded that the utilisation of more innovative monitoring tools such as remote sensing and GIS could enhance monitoring efforts and decision-making with regard to environmental management plans and legal compliance.
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Image processing techniques for hazardous weather detection
- Authors: Hardy, Caroline Hazel
- Date: 2012-06-05
- Subjects: Imaging systems in meteorology , Hazardous weather , Remote sensing , Satellite imagery , Image processing , Multispectral image analysis
- Type: Thesis
- Identifier: uj:2390 , http://hdl.handle.net/10210/4844
- Description: M.Ing. , Globally, hazardous weather phenomena such as violent storms, oods, cyclones, tornadoes, snow and hail contribute to signi cant annual xed property damages, loss of movable property and loss of life. The majority of global natural disasters are related to hydro-meteorological events. Hazardous storms are destructive and pose a threat to life and property. Forecasting, monitoring and detecting hazardous storms are complex and demanding tasks, that are however essential. In this study automatic hazardous weather detection utilizing remotely sensed meteorological data has been investigated. Image processing techniques have been analyzed and applied to multispectral meteorological satellite image data obtained from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments on-board the Meteosat Second Generation (MSG) geostationary meteorological satellites Meteosat-8 and Meteosat-9. The primary focus of this study is the detection of potentially hazardous hydrometeorological phenomena in South Africa. A methodology for detecting potentially hazardous storms over South Africa using meteorological satellite imagery from MSG/SEVIRI is presented. An index indicative of the hazardous potential of a storm is de ned to aid in the identi cation of a ected geographical areas and to quantify the destructive potential of the detected storm. The Hazardous Potential Index (HPI) is generated through the use of image processing techniques such as cloud masking, cloud tracking and an image-based analysis of the constituent elements of a severe convective storm. A retrospective review was performed with respect to 20 case studies of documented storms which had adversely a ected areas of South Africa. A red-green-blue (RGB) composite image analysis technique, that may be utilized in the identi cation of severe convective storms using SEVIRI image data, was also applied to these case studies.
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Realizing future intelligent networks via spatial and multi-temporal data acquisition in disdrometer networks
- Authors: Periola, Ayodele , Ogudo, Kingsley , Alonge, Akintunde
- Date: 2020
- Subjects: Remote sensing , Quantitative precipitation estimation , Disdrometer Networks
- Language: Ennglish
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/445459 , uj:38971 , Citation: Periola, A., Ogudo, K. & Alonge, A. 2020. Realizing future intelligent networks via spatial and multi-temporal data acquisition in disdrometer networks.
- Description: Abstract: Data acquisition and qualitative precipitation estimation (QPE) via disdrometers play an important role in estimating rain-induced attenuation in wireless networks. However, existing disdrometer observations do not provide sufficient information for modelling intelligent wireless networks. The design of intelligent wireless networks requires that QPE parameters for a location be known at different epochs. This requires that disdrometers with spatial variability should be capable of multi-temporal QPE observations. A disdrometer architecture that addresses this challenge is presented in this paper. The proposed multi–temporal disdrometer incorporates a computing payload for storing QPE related data at multiple epochs. Performance evaluation shows that the use of the proposed multi–temporal disdrometer in QPE related data acquisition increases data suitable for QPE related modelling by up to 52.2% and 49.4% in the short term and long term respectively.
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The evolution and significance of the Bongolava-Ranotsara shear zone, Madagascar
Wetland assessment using Unmanned Aerial Vehicle (UAV) photogrammetry
- Authors: Boon, M.A. , Greenfield, R. , Tesfamichael, S.
- Date: 2016
- Subjects: Unmanned Arial Vehicle Photogrammetry , Remote sensing , 3D point clouds and surface models
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/214778 , uj:21324 , Citation: Boon, M.A., Greenfield, R. & Tesfamichael, S. 2016. Wetland assessment using Unmanned Aerial Vehicle (UAV) photogrammetry.
- Description: Abstract: The use of Unmanned Arial Vehicle (UAV) photogrammetry is a valuable tool to enhance our understanding of wetlands. Accurate planning derived from this technological advancement allows for more effective management and conservation of wetland areas. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool to enhance the assessment of wetland ecosystems. The UAV images were collected during a single flight within 2½ hours over a 100 ha area at the Kameelzynkraal farm, Gauteng Province, South Africa. An AKS Y-6 MKII multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. Twenty ground control points (GCPs) were surveyed using a Trimble GPS to achieve geometrical precision and georeferencing accuracy. Structure-from-Motion (SfM) computer vision techniques were used to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The geometric accuracy of the data based on the 20 GCP’s were 0.018 m for the overall, 0.0025 m for the vertical root mean squared error (RMSE) and an over all root mean square reprojection error of 0.18 pixel. The UAV products were then edited and subsequently analysed, interpreted and key attributes extracted using a selection of tools/ software applications to enhance the wetland assessment. The results exceeded our expectations and provided a valuable and accurate enhancement to the wetland delineation, classification and health assessment which even with detailed field studies would have been difficult to achieve.
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