Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China
Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite
images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes
and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm
based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to
partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated
to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.
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minimum spanning tree;
urban spatial clusters
Document Type: Research Article
Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China
Department of Geography, University of Cincinnati, Cincinnati, OH, USA
Department of Geography and Geosciences, University of Louisville, Louisville, KY, USA
Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA, USA
Publication date: November 2, 2014
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