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Urban land classification and its uncertainties using principal component and cluster analyses: A case study for the UK West Midlands

机译：基于主成分和聚类分析的城市土地分类及其不确定性：以英国西米德兰兹郡为例

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摘要

An urban land-cover classification of the 900 km super(2) comprising the UK West Midland metropolitan area was generated for the purpose of facilitating stratified environmental survey and sampling. The classification grouped the 900 km super(2) into eight urban land-cover classes. Input data to the classification algorithms were derived from spatial land-cover data obtained from the UK Centre for Ecology and Hydrology, and from the UK Ordnance Survey. These data provided a description of each km super(2) in terms of the contributions to the land cover of 25 attributes (e.g. open land, urban, villages, motorway, etc.). The dimensionality of the land-cover dataset was reduced using principal component analysis, and eight urban classes were derived by cluster analysis using an agglomeration technique on the extracted components. The resulting urban land- cover classes reflected groupings of 1 km super(2) pixels with similar urban land morphology. Uncertainties associated with this agglomerative classification were investigated in detail using fuzzy-type analyses. Our study is the first report of a quantitative investigation of uncertainty associated with a classification of this type. The resulting classification for the UK West Midland metropolitan area offers an impartial basis for a wide range of environmental and ecological surveys. The methods used can be adapted readily to other metropolitan areas where generic urban features (e.g. roads, housing density) are gridded.
机译：为了促进分层环境调查和采样，生成了包括英国西米德兰大都会地区在内的900公里super（2）的城市土地覆盖分类。分类将900 km super（2）分为八个城市土地覆盖类别。分类算法的输入数据来自从英国生态水文中心和英国兵器测量局获得的空间土地覆盖数据。这些数据以25个属性（例如空地，城市，村庄，高速公路等）对土地覆盖的贡献来描述每公里super（2）。使用主成分分析降低了土地覆盖数据集的维数，并使用聚结技术对提取的成分进行聚类分析，得出了八个城市类别。由此产生的城市土地覆盖类别反映了具有相似城市土地形态的1 km super（2）像素分组。使用模糊类型分析详细研究了与这种聚集分类相关的不确定性。我们的研究是对此类型分类相关的不确定性进行定量研究的第一份报告。由此得出的英国西米德兰都市区的分类为广泛的环境和生态调查提供了公正的基础。所使用的方法可以很容易地适应其他城市区域，在这些城市区域中一般城市特征（例如道路，房屋密度）被网格化。

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