15 research outputs found

    The University of Iowa 2020-21 General Catalog

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    The University of Iowa 2019-20 General Catalog

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    The University of Iowa 2018-19 General Catalog

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    The University of Iowa 2017-18 General Catalog

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    Exploring the ecological and social benefits of the Khayelitsha Wetlands Park

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    In a world confronted by rapid urbanization linked with dramatic population growth rates, there is a general consensus that quality urban green spaces are important components of urban landscapes. Urban green spaces are defined as open spaces in urban areas primarily covered with vegetation, which are available to users within the community. They have the ability to shape the image of cities and provide various important socioecological benefits, which can contribute to improving the quality of life within these urban communities. In Cape Town, the provision of readily accessible quality urban green spaces is often overridden by other conflicting demands, such as biodiversity conservation and infrastructure development demands. The literature suggests that Cape Town has ample available green spaces. However, the accessibility of this green space is linked to issues of poor management and maintenance, and as a result poor urban spaces are often associated with criminal activities, and are therefore unavailable to benefit urban communities. This is particularly evident in areas which have a low socioeconomic status. This study explores the ecosystem services offered by the Khayelitsha Wetlands Park in the Khayelitsha Township on the Cape Flats. A variety of methods were used to establish the condition of the Wetlands Park and assess the impacts of various uses (e.g. recreation, agriculture etc.) on the vegetation structure and water quality. Qualitative semi-structured interviews were also conducted to assess the local community's uses and perceptions of this green space. A Complex Adaptive Landscape (CAL) approach was adopted to derive the positive and negative social-ecological impacts of the Khayelitsha Wetlands Park. The vegetation structure assessment results showed a dominance of emergent and invasive vegetation, such as Typha capensis and Acacia cyclops, and indicates a high level of degradation and a lack of indigenous vegetation species. The water quality analysis reveals high concentrations of physiochemical and microbial pollutants, where a majority exceeded the Targeted Water Quality Ranges (TWQR) recommended by the Department of Water Affairs for livestock watering, irrigation and human use. Findings from the semi-structured interviews, revealed that a majority of users v visit the Park for multiple activities offered by the Park. These include relaxation, creating and maintaining social relations, sports and recreation and agricultural use. The CAL framework revealed negative and positive feedback mechanisms at play in this urban green space. The negative feedback effects are illustrated and confirmed by poor water quality and a predominantly alien infested vegetation structure. The poor ecological condition of the Wetland is linked to a number of anthropogenic influences, including the discharge of treated waste and untreated waste from both agricultural and urban waste sources, indicating the complexity of managing the Khayelitsha Wetlands Park. Since a number of users and management institutions are connected to the Khayelitsha Wetlands Park, their involvement in the management thereof is crucial for effectively solving the issues identified

    The University of Iowa General Catalog 2016-17

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    Uydu görüntülerinden yer kontrol noktasız sayısal yüzey haritaları.

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    Generation of Digital Surface Models (DSMs) from stereo satellite (spaceborne) images is classically performed by Ground Control Points (GCPs) which require site visits and precise measurement equipment. However, collection of GCPs is not always possible and such requirement limits the usage of spaceborne imagery. This study aims at developing a fast, fully automatic, GCP-free workflow for DSM generation. The problems caused by GCP-free workflow are overcome using freely-available, low resolution static DSMs (LR-DSM). LR-DSM is registered to the reference satellite image and the registered LR-DSM is used for i) correspondence generation and ii) initial estimate generation for 3-D reconstruction. Novel methods are developed for bias removal for LR-DSM registration and bias equalization for projection functions of satellite imaging. The LR-DSM registration is also shown to be useful for computing the parameters of simple, piecewise empirical projective models. Recent computer vision approaches on stereo correspondence generation and dense depth estimation are tested and adopted for spaceborne DSM generation. The study also presents a complete, fully automatic scheme for GCPfree DSM generation and demonstrates that GCP-free DSM generation is possible and can be performed in much faster time on computers. The resulting DSM can be used in various remote sensing applications including building extraction, disaster monitoring and change detection.Ph.D. - Doctoral Progra

    The University of Iowa General Catalog 2011-12

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    The University of Iowa General Catalog 2010-11

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    Hybrid And Hierarchical Image Registration Techniques

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    A large number of image registration techniques have been developed for various types of sensors and applications, with the aim to improve the accuracy, computational complexity, generality, and robustness. They can be broadly classified into two categories: intensity-based and feature-based methods. The primary drawback of the intensity-based approaches is that it may fail unless the two images are misaligned by a moderate difference in scale, rotation, and translation. In addition, intensity-based methods lack the robustness in the presence of non-spatial distortions due to different imaging conditions between images. In this dissertation, the image registration is formulated as a two-stage hybrid approach combining both an initial matching and a final matching in a coarse-to-fine manner. In the proposed hybrid framework, the initial matching algorithm is applied at the coarsest scale of images, where the approximate transformation parameters could be first estimated. Subsequently, the robust gradient-based estimation algorithm is incorporated into the proposed hybrid approach using a multi-resolution scheme. Several novel and effective initial matching algorithms have been proposed for the first stage. The variations of the intensity characteristics between images may be large and non-uniform because of non-spatial distortions. Therefore, in order to effectively incorporate the gradient-based robust estimation into our proposed framework, one of the fundamental questions should be addressed: what is a good image representation to work with using gradient-based robust estimation under non-spatial distortions. With the initial matching algorithms applied at the highest level of decomposition, the proposed hybrid approach exhibits superior range of convergence. The gradient-based algorithms in the second stage yield a robust solution that precisely registers images with sub-pixel accuracy. A hierarchical iterative searching further enhances the convergence range and rate. The simulation results demonstrated that the proposed techniques provide significant benefits to the performance of image registration
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