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Accurate memory for an object or event requires that multiple diverse features are bound together and retained as an integrated representation. There is overwhelming evidence that healthy ageing is accompanied by an associative deficit in that older adults struggle to remember relations between items above any deficit exhibited in remembering the items themselves. However, the effect of age on the ability to bind features within novel objects (for example, their colour and shape) and retain correct conjunctions over brief intervals is less clear. The relatively small body of work that exists on this topic to-date has suggested no additional working memory impairment for conjunctions of features beyond a general age-related impairment in the ability to temporarily retain features. This is in stark contrast to the feature binding deficit observed in the early stages of Alzheimer’s disease. Nevertheless, there have been reports of age-related feature binding deficits in working memory under specific circumstances. Thus a major focus of the present work was to assess these potential boundary conditions. The change detection paradigm was used throughout this work to examine age-differences in visual working memory. Despite the popularity of this task important issues regarding the way in which working memory is probed have been left unaddressed. Chapter 2 reports three experiments with younger adults comparing two methods of testing recognition memory for features or conjunctions. Contrary to an influential study in the field, it appears that processing multiple items at test does not differentially impact on participants’ ability to detect binding changes. Chapters 3, 4, and 5 report a series of experiments motivated by previous findings of specific age-related feature binding deficits. These experiments, improving on previous methodology where possible, demonstrate that increasing the amount of time for which items can be studied (Chapter 3) or mixing feature-conjunction changes in trial-blocks with more salient changes to individual features (Chapters 4 and 5) does not differentially impact on healthy older adults’ ability to detect binding changes. Rather, the argument is made that specific procedural aspects of previous work led to the appearance of deficits that do not generalise. Chapter 5 also addresses the suggestion that healthy ageing specifically affects the retention of item-location conjunctions. The existing evidence for this claim is reviewed, and found wanting, and new data are presented providing evidence against it. To follow-up on the absence of a deficit for simple feature conjunctions, Chapter 6 contrasts two theoretically distinct binding mechanisms: one for features intrinsic to an object and another for extrinsic, contextual features. Preliminary evidence is reported that the cost associated with retaining pairings of features is specifically pronounced for older adults when the features are extrinsic to each other. In an attempt to separate out the contribution of working memory capacity and lapses of attention to age-differences in overall task performance, Chapter 7 reports the results of an exploratory analysis using processing models developed in Chapter 2. Analysis of two data sets from Chapters 4 and 5 demonstrates that lapses of attention make an important contribution to differences in change detection performance. Chapter 8 returns to the issue of measurement in assessing the evidence for specific age-related deficits. Simulations demonstrate that the choice of outcome measure can greatly affect conclusions regarding age-group by condition interactions, suggesting that some previous findings of such interactions in the literature may have been more apparent than real. In closing the General Discussion relates the present work to current theory regarding feature binding in visual working memory and to the wider literature on binding deficits in healthy and pathological ageing.

Interferometric Synthetic Aperture Radar (InSAR) is an established technique which has been applied to Earth surface displacement analysis and topographic reconstruction. Two complex coherent SAR acquisitions of the same scene are combined to form an interferogram from which surface displacement or terrain measurements are made. The similarities between both SAR signals is captured in the coherence and its magnitude is determined by the spatial separation between acquiring antennas and the changes (if any) to the physical characteristics of the scattering target in the duration between both SAR acquisitions. Both of these products derivable from the interferometric process have been applied in this study with the aim of enhancing monitoring and assessing changes in the coastal environment, with emphasis on the coastal geomorphology. A combination of remote sensing data acquired for Montrose Bay, NE Scotland, has been used to analyze changes to the geomorphology of the beach and dune system in terms of sediment volume analysis, erosion and accretion processes and shoreline changes over a short-term period of 4 years. The interferometric coherence was applied to detect changes to the dune morphology, which have been actively eroding at the southern flank of the Bay. The interferometric analysis presented in this thesis was based on SAR data acquired by the Sentinel-1 SAR antenna and the results demonstrated the limitations of the sensor for terrain mapping and DEM reconstruction. In addition, the significance of the vegetation on the interferometric coherence was demonstrated. However, the results have shown that temporal baseline remained a significant consideration in the application of interferometric coherence in highly dynamic environments such as the coastal environment.

Many tasks rely on our ability to hold information about a stimulus in mind after it is no longer visible and to compare this information with incoming perceptual information. This ability relies on a short-term form of memory known as visual working memory. Research and theory at the behavioral and neural levels has begun to provide important insights into the basic properties of the neuro-cognitive systems underlying this form of memory. However, to date, no neurally-plausible theory has been proposed that addresses both the storage of information in working memory and the comparison process in a single framework. To address these limitations, I have developed a new model where working memory is realized via peaks of activation in dynamic neural fields, and comparison emerges as a result of interactions among the model's layers. In a series of simulations, I show how the model can be used to capture each of the components underlying performance in simple visual comparison tasks--from the encoding, consolidation, and maintenance of information in working memory, to comparison and updating in response to changed inputs. Importantly, the proposed model demonstrates how these elementary perceptual and cognitive functions emerge from the coordinated activity of an integrated, dynamic neural system. The model also makes novel predictions that were tested in a series of behavioral experiments. Specifically, when similar items are stored, shared lateral inhibition produces a sharpening of the peaks of activation associated with each item in memory. In the context of the model, this leads to the prediction that change detection will be enhanced for similar versus dissimilar features. This prediction was confirmed in a series of change detection experiments exploring memory for both color and orientation. In addition to sharpening, shared lateral inhibition among similar items produces mutual repulsion between nearby peaks. This leads to the prediction that when similar features are held, they will be systematically biased away from each other over delays. This prediction was confirmed in a cued color recall experiment comparing memory for a "far" color with memory for two "close" colors.

This study has the objective of identifying the possible environmental constraints that has role for the continuous loss of heathland plant Genista pilosa. The study has assessed different environmental settings where the plant occurs by way of overlaying analysis based on multiple spatial data sets. Thereafter empirical change detection analyses on the land use of the study area have been performed on the GIS environment by combining temporal based remotely sensed spatial data. The result was then analyzed using land use dynamicity model and the rates of change on each land use type are identified. Expansion of human activity, especially the spreading of agricultural land and urbanization, is found to be the most determinant factor for the dramatic loss of the plant. Finally serious attention for the protection of the plant is recommended by mentioning the possible problem that would occur due to a loss of biodiversity.

The primary objective of this research was to evaluate the potential for monitoring forest change using Landsat ETM and Aster data. This was accomplished by performing eight change detection algorithms: pixel post-classification comparison (PCC), image differencing Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Transformed Difference Vegetation Index (TDVI), principal component analysis (PCA), multivariate alteration detection (MAD), change vector analysis (CVA) and tasseled cap analysis (TCA). Methods, Post-Classification Comparison and vegetation indices are straightforward techniques and easy to apply. In this study the simplified classification with only 4 forest classes namely close forest, open forest, bare land and grass land was used The overall classification accuracy obtained were 88.4%, 91.9% and 92.1% for the years 2000, 2003 and 2006 respectively. The Tasseled Cap green layer (GTC) composite of the three images was proposed to detect the change in vegetation of the study area. We found that the RBG-TCG worked better than RGBNDVI. For instance, the RBG-TCG detected some areas of changes that RGB-NDVI failed to detect them, moreover RBG-TCG displayed different changed areas with more strong colours. Change vector analysis (CVA) based on Tasseled Cap transformation (TCT) was also applied for detecting and characterizing land cover change. The results support the CVA approach to change detection. The calculated date to date change vectors contained useful information, both in their magnitude and their direction. A powerful tool for time series analysis is the principal components analysis (PCA). This method was tested for change detection in the study area by two ways: Multitemporal PCA and Selective PCA. Both methods found to offer the potential for monitoring forest change detection. A recently proposed approach, the multivariate alteration detection (MAD), in combination with a posterior maximum autocorrelation factor transformation (MAF) was used to demonstrate visualization of vegetation changes in the study area. The MAD transformation provides a way of combining different data types that found to be useful in change detection. Accuracy assessment is an important final step addressed in the study to evaluate the different change detection techniques. A quantitative accuracy assessment at level of change/no change pixels was performed to determine the threshold value with the highest accuracy. Among the various accuracy assessment methods presented the highest accuracy was obtained using the post-classification comparison based on supervised classification of each two time periods (2000 -2003 and 2003-2006), which were 90.6% and 87% consequently.

The emergence of the Internet has made the global information communications much easier than before. Users can navigate the desired information over the Internet by means of search engines. Even though search engine can help users search specified topic in a primary way, users usually cannot gain the overall idea of what the entire navigated results mean. In addition, information over the Internet keeps changing. Users cannot even keep track of the changes, let alone to comprehend the meanings of such changes. Consequently, this research proposes a two-stage incremental approach to figuring out the concept structure that represents the main concepts of the search results in the first stage, and keeping track of the concept changes with time based on spreading activation theory to assist users in the second stage. Experiments are conducted to examine the feasibility of our proposed approach. The first experiment is to evaluate the results from the first stage. It shows that the performance on recall and precision is quite satisfactory based on human experts¡¦ results. The second experiment is to examine the changing results from the entire proposed approach. It shows that high degree of agreement with our results is achieved from domain experts. Both experiments justify the feasibility of our proposed approach in real applications. That is, applying our proposed approach, users can easily focus on the topic they are interested in and learn its trend with great support. Keywords: Internet, Concepts Extraction, Concept Change Detection, Spreading Activation Theory.

To manage the information flood in the Internet, we usually navigate specific information using the provided search engines. Search engines are convenient but with limited functions. For example, it is impractical and impossible to browse through the entire collected information for us to gain an overall picture about what the navigated information stands for. To do so, we need an appropriate approach to automatically extracting concepts from the navigated information to assist users to easily and quickly gain the primary understanding toward a topic that interests users. In this research, we propose an approach to extracting concepts from the navigated web information and detecting the concept changes over time. It basically includes two stages. In the first stage, information is decomposed into paragraphs and they are clustered with key terms identified through the aid of latent semantic indexing method. Concepts are represented in the form of paragraph summary and associated key terms, which allows the user to easily comprehend what they describe. The second stage is to adaptively modify the concept structure to detect concept changes. With new information added, the concepts could be merging, splitting, or even emerging with time. Three experiments are conducted in this research to verify the proposed approach. Results of the first and second experiments show both high recall and high precision that matches the predefined concept categories. The last one is an illustrated real case application on the tsunami event. It shows that we can easily grasp different concepts of the tsunami reports and realize their changes by using our approach. The feasibility of employing our approach is thus justified.

Information acquisition over the Internet has become popular recently. Users, however, have difficulty in understanding the overall concept resulting from the searched information about a specific topic of their interests in the Internet. Moreover, such pieces of information keep changing over time. Therefore, in this thesis, an approach is proposed to help users further realize the searched results of their interested topic, and detect implications of the information changes over time. The first part of this approach is to gather information of a user-specified topic and analyze the overall meaning and the relations represented by those pieces of information. In this manner, users can gain the general concept of what the search results indicate. Here the keyword extraction approach, called RCBKE, is proposed to identify keywords with their relationships. Evaluations are performed and the results show that RCBKE can discover representative keywords. The second part is to track and investigate the information change of the topic in a certain time period. As a result, users can easily recognize the change patterns of the specified topic. An example to illustrate our approach is shown accordingly. The feasibility of our proposed approach is then justified.

Obtaining up-to-date information concernmg the environment at reasonable costs is a challenge faced by many institutions today. Satellite images meet both demands and thus present a very attractive source of information. The following thesis deals with the comparison of satellite images and a vector based land use data base of the City of Vienna. The satellite data is transformed using the spectral mixture analysis, which allows an investigation at a sub-pixel level. The results of the transformation are used to determine how suitable this spectral mixture analysis is to distinguish different land use classes in an urban area. In a next step the results of the spectral mixture analysis of two different images (recorded in 1986 and 1991) are used to undertake a change detection. The aim is to show those areas, where building activities have taken place. This information may aid the update of data bases, by limiting a detailed examination of an area to those areas, which show up as changes in the change detection. The proposed method is a fast and inexpensive way of analysing large areas and highlighting those areas where changes have taken place. lt is not limited to urban areas but may easily be adapted for different environments. (author's abstract) / Series: Research Reports of the Institute for Economic Geography and GIScience