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Multi-scaled identification of landscape character types and areas in Lushan National Park and its fringes, China

机译:庐山国家公园景观特点类型与地区的多缩放识别及其流苏

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摘要

China's national parks adopt a resource-oriented protection and planning approach that cannot restrain the continuous landscape fragmentation and deterioration, whereas, we propose to characterise the landscape in order to protect its integrity. This paper described a hierarchical identification of landscape character types and areas in Lushan National Park and its fringes according to a refined combination of the parametric and the holistic methods in a multiscalar approach. In terms of the functional hierarchy of landscape character, we decided to order the available data sources in 'downscaling'. At the broad scale, landscape typologies were delimited by raster datasets of four natural attributes: land cover, soil, vegetation, and altitude. At the intermediate scale, landscape typologies were determined by raster datasets of six natural and cultural attributes: aspect, slope, relief amplitude, heritage density, geology and land use. At these two scales, we adopted the principal component analysis (PCA) and two-step cluster analysis in SPSS software to visualise landscape types, to modify and integrate the results obtained in the eCognition software, as well as to rectify the visualisation with manual identifications. At the detailed scale, landscape typologies were demarcated by two raster and one vector datasets of cultural attributes: building density, visual influence and time depth. We performed the visualisation and integration with a similar method except for the PCA step. This multi-scaled identification will provide a nested framework facilitating the integration of the broad Lushan region in both spatial and administrative dimensions.
机译:中国国家公园采用资源导向的保护和规划方法,不能抑制不断景观碎片和恶化,而我们建议塑造景观以保护其完整性。本文描述了庐山国家公园的景观特征类型和地区的分层识别,并根据参数化的精致组合,并以多音响方法的方法。就景观角色的功能层次结构而言,我们决定在“缩减”中订购可用的数据源。在广泛的规模中,景观类型由四个自然属性的光栅数据集分隔:陆地覆盖,土壤,植被和高度。在中间规模,景观类型由六自然和文化属性的光栅数据集决定:方面,坡度,救济幅度,遗产密度,地质和土地利用。在这两个尺度上,我们采用了SPSS软件中的主成分分析(PCA)和两步集群分析来可视化横向类型,修改和集成在Ecognition软件中获得的结果,以及用手动标识纠正可视化。在详细的范围内,景观类型由两个栅格和一个传染媒介的文化属性数据集划分:建立密度,视觉影响和时间深度。除了PCA步骤之外,我们使用类似的方法执行可视化和集成。这种多缩放的识别将提供一种嵌套框架,促进了宽庐山区域在空间和行政方面的集成。

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