Conducting single frame orthorectification on satellite images to create an ortho-image requires four basic components, namely an image, a geometric sensor model, elevation data (for example a digital elevation model (DEM)) and ground control points (GCPs). For this study, orthorectification was executed numerous times (in three stages) and each time components were altered to test the geometric accuracy of the resulting ortho-image. Most notably, the distribution and number of ground control points, the quality of the elevation source and the geometric sensor model or lack thereof were altered. Results were analysed through triangulating and comparing the geolocation accuracy of the ortho-images. The application of these different methods to perform orthorectification encompass the aim of this paper, which was to investigate and compare the positional accuracies of ortho-images under various orthorectification scenarios and provide improved geometric accuracies of VHR satellite imagery when diverse ground control and elevation data sources are available. By investigating the influence that the distribution and number of GCPs and the quality of DEMs have on the positional accuracy of an ortho-image, it became clear that a reasonable increase in the number of uniformly distributed GCPs combined with progressively accurate DEMs will ultimately improve the quality of the orthorectified product. The results also showed that when more GCPs were applied, the smaller the difference in accuracy was between the different DEMs utilised. It was interesting to note that when it is suitable to manually collect well-distributed GCPs using a GPS handheld device over the study area then a very accurate result can be expected. Nonetheless, it is also important to note that if it is not possible/practical to achieve the latter, satellite based GCP collection do provide a very good alternative. It was also determined that utilising GCPs which were extracted from vector road layers to only cover specific areas in the image scene produced less favourable results. Several contributions towards improved orthorectification procedures were made in this study. These include the development of an automatic GCP extraction script (A-GCP-ES), written in the Python scripting language with the purpose to ease the process of manually placing GCPs on an input image when repeatedly performing orthorectification. / Thesis (PhD)--University of Pretoria, 2016. / Geography, Geoinformatics and Meteorology / PhD / Unrestricted

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The thesis content study of land use in municipality Lelekovice and its connection with software for geographic information system. It describes processing data especially historical cadastre maps, archival aerial imagery and orthophotos which were used for analyzing of this area. Analysis were performed in software ArcGIS. Appendixes of thesis are graphs and visualization of development of study area.

Thesis (MA (Geography and Environmental Studies))--University of Stellenbosch, 2005. / Salinisation is a major environmental hazard that reduces agricultural yields and degrades arable land. Two main categories of salinisation are: primary and secondary soil salinisation. While primary soil salinisation is caused by natural processes, secondary soil salinisation is caused by human factors. Incorrect irrigation practices are the major contributor to secondary soil salinisation. Because of low costs and less time that is associated with the use of remote sensing techniques, remote sensing data is used in this study to identify and map salinised irrigated land between Upington and Keimoes, Northern Cape Province, in South Africa. The aim of this study is to evaluate the potential of digital aerial imagery in identifying salinised cultivated land. Two methods were used to realize this aim. The first method involved visually identifying salinised areas on NIR, and NDVI images and then digitizing them onscreen. In the second method, digital RGB mosaicked, stacked, and NDVI images were subjected to unsupervised image classification to identify salinised land. Soil samples randomly selected and analyzed for salinity were used to validate the results obtained from the analysis of aerial photographs. Both techniques had difficulties in identifying salinised land because of their inability to differentiate salt induced stress from other forms of stress. Visual image analysis was relatively successful in identifying salinised land than unsupervised image classification. Visual image analysis correctly identified about 55% of salinised land while only about 25% was identified by unsupervised classification. The two techniques predict that an average of about 10% of irrigated land is affected by salinisation in the study area. This study found that although visual analysis was time consuming and cannot differentiate salt induced stress from other forms; it is fairly possible to identify areas of crop stress using digital aerial imagery. Unsupervised classification was not successful in identifying areas of crop stress.

Made available in DSpace on 2016-12-08T16:55:48Z (GMT). No. of bitstreams: 1 Sinara.pdf: 7213170 bytes, checksum: 532417886f356ca3ecc311dcfdce19a1 (MD5) Previous issue date: 2011-08-04 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Many cities and rural communities are settled in areas with natural dynamics that represent danger to people, such as floods, landslides, high winds, earthquakes, among others. These dangerous phenomena with high magnitude and / or frequency can lead to accidents and even disasters with many losses and damages, including loss of life. The development of many regions and countries could be affected by the occurrence of such disasters, as economic resources are lost when the event occurs, and also spent on reconstruction, which leads to a vicious circle, since the economic resource is spent on emergencies and reconstruction and not on prevention. The best way to minimize the negative impacts of natural disasters is to create methods of prevention. Mapping is an essential tool for planning, management and monitoring areas considered at risk. For case study, it was chosen the sub-basin of the river Ribeirão Sorocaba located in Luis Alves, Santa Catarina, a region that was highly affected by the rains in November 2008. The methodological approach of the research was to automatically extract the Digital Elevation Model (DEM) through IKONOS stereo pair image ans its metadata files. With DEM it was possible to orthorectify the image and evaluate the cartographic quality of both generated products accordingly to the Cartographic Exactness Standard (PEC, in Portuguese). Evaluation showed that the objective of obtaining a scale better than 1:50.000 was achieved. The products can obtain the scale of 1:10.000 in planimetry and 1:25.000 in altimetry, both in class A of PEC / Muitas cidades e comunidades rurais estão assentadas em sítios naturais sujeitos a fenômenos da dinâmica natural que representam risco para a população, como: inundações, deslizamentos, ventos fortes, terremotos, entre outros. A deflagração desses fenômenos perigosos com alta magnitude e/ou frequência pode provocar desde acidentes até catástrofes com muitas perdas e danos, inclusive com perdas de vidas humanas. O desenvolvimento de muitas regiões e países pode ser prejudicado pela ocorrência dessas catástrofes, pois recursos econômicos são perdidos no momento da ocorrência do evento e também gastos na reconstrução, o que leva a um círculo vicioso, uma vez que o recurso econômico é gasto em emergências e reconstrução e não em prevenção. A melhor forma de minimizar os impactos causados pelos desastres naturais é criar métodos de prevenção. Nesse sentido, essa pesquisa divulga a cartografia como instrumento fundamental para o planejamento, gestão e monitoramento das áreas consideradas de risco. Para estudo de caso foi escolhida a sub-bacia do Ribeirão Sorocaba localizada no município de Luís Alves, em Santa Catarina, região esta que foi muito afetada pelas chuvas de novembro de 2008. A metodologia da pesquisa se resume na extração automática do Modelo Digital de Elevação (MDE) por meio do par estereoscópico da imagem IKONOS e dos seus arquivos de Metadados. Com o MDE foi possível ortorretificar a imagem e avaliar a qualidade cartográfica dos dois produtos gerados segundo o Padrão de Exatidão Cartográfica (PEC). A avaliação mostrou que o objetivo de alcançar uma escala melhor que 1:50.000 foi alcançado. Os produtos podem chegar a escala de 1:10.000 na planimetria e 1:25.000 na altimetria, ambos na classe A do PEC