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Journal : Indonesian Journal of Geography

THE RELATIONS BETWEEN URBAN PARKS AND PERSONAL WELL-BEING IN BRISBANE, SOUTH-EAST QUEENSLAND, AUSTRALIA Công Trí, Hoàng; Thanh Hùng, Nguyễn; Kamal, Muhammad
Indonesian Journal of Geography Vol 44, No 1 (2012): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

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Abstract

The positive impacts of urban parks on human health have been analysed in many studies,but nearly none of them provide a suitable method to explain quantitatively the satisfactionand dissatisfaction of park uses on personal health. Bayesian Belief Network (BBN) wasemployed to examine individually well-being spirit in relation to the changes of quality ofparks and the joyfulness on access to parks. This study aims to find answers for questions‘why and where are people happy or unhappy with their health in connections to urbanparks?’ The data for Brisbane area were extracted from the quality of life survey in South-East Queensland, Australia. 70% data was used for learning model parameters; the rest wasfor model testing. The generated model had 73.17% accuracy, and it was imported to ArcGISfor constructing probabilistic maps. Due to the high density of sample points, InverseDistance Weighted (IDW) interpolation was chosen to illustrate the probable happiness andunhappiness on personal health. The result shows that quality of urban parks controlledstrongly the fulfilment of personal health. Local governors can successfully enrich the qualityof urban lives by improving the quality of parks in some specific regions.
SUB-PIXEL IMAGE CLASSIFICATION OF HYPER-SPECTRAL DATA FOR VEGETATION AND SOIL MAPPINGIN SEMI-ARID ENVIRONMENT Kamal, Muhammad
Indonesian Journal of Geography Vol 41, No 2 (2009): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

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Abstract

The HyMap hyper-spectral data was used to classify photosyntheticvegetation (PV), non-photosynthetic vegetation (NPV), and exposed soils in a semiaridsavannah environment of McKinlay, northern Queensland, and Australia. Thisstudy aimed to understandhow effective the sub-pixel classificationapproach appliedon hyper-spectral data to distinguish the vegetation and soil features in semi-aridenvironment. In contrast to the per-pixel approach this approach treats the pixelvalue as reflectance sum of its composite features, and shows its componentabundance. The most commonly used sub-pixel classification technique was used inthis research, namely Linear Spectral Unmixing (LSU). End members were used asthe input class, and the result was compared with the standard maximum likelihoodclassification (MLC) using post-classification comparison method The result of thisstudy shows that LSU produced a patchy distribution of classes throughout theimage. The brown soil tends to be over-estimated with respect to other classes. PVfeatures were relatively well-mapped compare to other classes. NPV features haveproblem with domination of exposed soil reflectance. This is equivalent to theprevious studies result that background soil dominates the spectral reflectance inthis environment. According to the qualitative accuracy assessment, LSU hashigher accuracy in representing PV and NPV compare to the traditional MLCclassification.