TY - JOUR

T1 - Urban land classification and its uncertainties using principal component and cluster analyses: A case study for the UK West Midlands

AU - Owen, S. M.

AU - Mackenzie, Rob

AU - Bunce, R. G. H.

AU - Stewart, Hope

AU - Donovan, R. G.

AU - Stark, G.

AU - Hewitt, C. N.

PY - 2006

Y1 - 2006

N2 - An urban land-cover classification of the 900 km(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(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(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(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. (c) 2005 Elsevier B.V. All rights reserved.

AB - An urban land-cover classification of the 900 km(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(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(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(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. (c) 2005 Elsevier B.V. All rights reserved.

KW - land classification

KW - stratified sampling and surveys

KW - fuzzy analysis of uncertainty

KW - urban land cover

KW - GREAT-BRITAIN

KW - COVER

KW - PATTERNS

KW - IMAGES

KW - AVIRIS

KW - AREAS

KW - MODEL

KW - MAP

U2 - 10.1016/j.landurbplan.2005.11.002

DO - 10.1016/j.landurbplan.2005.11.002

M3 - Journal article

VL - 78

SP - 311

EP - 321

JO - Landscape and Urban Planning

JF - Landscape and Urban Planning

SN - 0169-2046

IS - 4

ER -