1,721,638 research outputs found
Applying grid-group cultural theory to enhance management and business education and assessment through serious games
No full textOngoing transformation of higher education is being driven by automation and artificial intelligence, posing challenges and solutions for learning and assessment practices. The increased usage of generative AI tools also raises concerns about academic integrity and the potential erosion of critical thinking skills in business school graduates. Furthermore, UK National Student Survey results underscore the need for improved teaching and assessment methods, such as subjects being made to be engaging, improvements in marking and assessment, improvements in the efficacy of feedback and courses better developing the knowledge and skills needed for the future. The application of Grid-Group Cultural Theory is suggested as used to categorize learners into four thought style archetypes: Fatalists, Hierarchists, Egalitarians and Individualists. Each has distinct preferences for learning and assessment types, which can be used to inform effective teaching and assessment design. The use of serious games is suggested, t
Downscaling in remote sensing
No full textDownscaling has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation, (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through downscaling or area-to-point prediction) or categories are predicted (super-resolution mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and downscaling cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany downscaling predictions, and the fundamental limits on the representativeness of downscaling predictions. The paper ends with a look towards the grand challenge of downscaling in the context of time-series image stacks. The challenge here is to use all the available information to produce a downscaled series of images that is coherent between images and, thus, which helps to distinguish real changes (signal) from noise
Super-resolution target mapping from soft classified remotely sensed imagery
No full textA simple, efficient algorithm is presented for sub-pixel target mapping from remotely-sensed images. Following an initial random allocation of “soft” pixel proportions to “hard” sub-pixel binary classes, the algorithm works in a series of iterations, each of which contains three stages. For each pixel, for all sub-pixel locations, a distance-weighted function of neighboring sub-pixels is computed. Then, for each pixel, the sub-pixel representing the target class with the minimum value of the function, and the sub-pixel representing the background with the maximum value of the function are found. Third, these two sub-pixels are swapped if the swap results in an increase in spatial correlation between sub-pixels. The new algorithm predicted accurately when applied to simple simulated and real images. It represents an accessible tool that can be coded and applied readily by remote sensing investigators
A systematic review of vegetation phenology in Africa
No full textThe study of vegetation phenology is important because it is a sensitive indicator of climate changes and it regulates carbon, energy and water fluxes between the land and atmosphere. Africa, which has 17% of the global forest cover, contributes significantly to the global carbon budget and has been identified as potentially highly vulnerable to climate change impacts. In spite of this, very little is known about vegetation phenology across Africa and the factors regulating vegetation growth and dynamics. Hence, this review aimed to provide a synthesis of studies of related Africa’s vegetation phenology and classify them based on the methods and techniques used in order to identify major research gaps. Significant increases in the number of phenological studies in the last decade were observed, with over 70% of studies adopting a satellite-based remote sensing approach to monitor vegetation phenology. Whereas ground based studies that provides detailed characterisation of vegetation phenological development, occurred rarely in the continent. Similarly, less than 14% of satellite-based remote sensing studies evaluated vegetation phenology at the continental scale using coarse spatial resolution datasets. Even more evident was the lack of research focusing on the impacts of climate change on vegetation phenology. Consequently, given the importance and the uniqueness of both methods of phenological assessment, there is need for more ground-based studies to enable greater understanding of phenology at the species level. Likewise, finer spatial resolution satellite sensor data for regional phenological assessment is required, with a greater focus on the relationship between climate change and vegetation phenological changes. This would contribute greatly to debates over climate change impacts and, most importantly, climate change mitigation strategies
Geoinformatics and water-erosion processes
No full textGeomorphologists have commonly published conclusions about soil erosion and water movement based on experimental data obtained at the catchment scale. The underlying assumptions were that there exists little spatial variation in conditions at the hillslope scale (the fundamental unit) and that the catchments are representative of other catchments in the same region. These assumptions are unlikely to be tenable in practice. Indeed, we suggest that there is substantial spatial variation in geomorphological properties even at small distances when observed at fine spatial resolution and that modern geoinformatics approaches can be used to quantify and characterize this variation. This introduction reviews the ten papers that comprise this Special Issue on Studying Water-Erosion Processes with Geoinformatics, drawn from across the geomorphological sciences. The water erosion processes studied in these papers include sediment transport, fluvial processes, slope denudation, landsliding, bank erosion and bank line migration. The findings suggest that innovative measurement and modeling approaches such as GPS measurements, geostatistics, image processing techniques, and physically-based models deliver new data with which to study water erosion processes. These findings involve domains that are associated with fundamental aspects of geomorphology. Hence, there are strong grounds for claiming that geoinformatics can contribute to greater understanding of water erosion processes through characterization of space–time dynamics. We suggest that geomorphologists need to use more geoinformatics to collect more data relating to the outcomes of water erosion processes, to seek out and apply innovative processing methods and, finally, model the data to provide greater understanding of processes and to forecast and explore future scenarios
Modelling the effect of urbanization on the transmission of an infectious disease
No full textThis paper models the impact of urbanization on infectious disease transmission by integrating a CA land use development model, population projection matrix model and CA epidemic model in S-Plus. The innovative feature of this model lies in both its explicit treatment of spatial land use development, demographic changes, infectious disease transmission and their combination in a dynamic, stochastic model. Heuristically-defined transition rules in cellular automata (CA) were used to capture the processes of both land use development with urban sprawl and infectious disease transmission. A population surface model and dwelling distribution surface were used to bridge the gap between urbanization and infectious disease transmission. A case study is presented involving modelling influenza transmission in Southampton, a dynamically evolving city in the UK. The simulation results for Southampton over a 30-year period show that the pattern of the average number of infection cases per day can depend on land use and demographic changes. The modelling framework presents a useful tool that may be of use in planning application
Issues of uncertainty in super-resolution mapping and their implications for the design of an inter-comparison study
No full textSuper-resolution mapping is a relatively new field in remote sensing whereby classification is undertaken at a finer spatial resolution than that of the input remotely sensed multiple-waveband imagery. A variety of different methods for super-resolution mapping have been proposed, including spatial pixel-swapping, spatial simulated annealing, Hopfield neural networks, feed-forward back-propagation neural networks and geostatistical methods. The accuracy of all of these new approaches has been tested, but the tests have tended to focus on the new technique (i.e. with little benchmarking against other techniques) and have used different measures of accuracy. There is, therefore, a need for greater inter-comparison between the various methods available, and a super-resolution inter-comparison study would be a welcome step towards this goal. This paper describes some of the issues that should be considered in the design of such a study
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