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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92334


    Title: Spatial statistical analysis of tree deaths using airborne digital imagery
    Authors: Chang, Ya-Mei;Baddeley, Adrian;Wallace, Jeremy;Canci, Michael
    Contributors: 淡江大學統計學系
    Keywords: Covariate effect;Kernel estimation;Morphological image analysis;Partial residual;Spatial point pattern;Tree location detection;Point process;Logistic regression;Leverage;Influence
    Date: 2013-04
    Issue Date: 2013-10-01 15:48:51 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: High resolution digital airborne imagery offers unprecedented opportunities for observation and monitoring of vegetation, providing the potential to identify, locate and track individual vegetation objects over time. Analytical tools are required to quantify relevant information. In this paper, locations of trees over a large area of native woodland vegetation were identified using morphological image analysis techniques. Methods of spatial point process statistics were then applied to estimate the spatially-varying tree death risk, and to show that it is significantly non-uniform. [Tree deaths over the area were detected in our previous work (Wallace et al., 2008).] The study area is a major source of ground water for the city of Perth, and the work was motivated by the need to understand and quantify vegetation changes in the context of water extraction and drying climate. The influence of hydrological variables on tree death risk was investigated using spatial statistics (graphical exploratory methods, spatial point pattern modelling and diagnostics).
    Relation: International Journal of Applied Earth Observation and Geoinformation 21, pp.418–426
    DOI: 10.1016/j.jag.2012.04.006
    Appears in Collections:[統計學系暨研究所] 期刊論文

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