Extracting Urban Subzonal Land Uses through Morphological and Spatial Arrangement Analyses Using Geographic Data and Remotely Sensed Imagery

Abstract

Land use (LU) information is of significant value for various urban studies and is needed for a wide variety of decision-making initiatives in the range of global, regional, and urban areas. However, it has been difficult to collect LU information in an efficient and cost-effective way in the past, which forces planners and engineers to use other information (e.g., population and employment) as proxy in their modeling practice. This paper intends to establish a hybrid geographic information and remote sensing (GI/RS) expert system to extract LU at a very detailed subzonal level. Three LU classification algorithms including fuzzy-decision tree (FDT), logistic-decision tree (LDT), and artificial neural network (ANN) are designed to classify urban subzones, dissemination blocks (DBs), the smallest census zone, into single LUs using very high resolution (VHR) aerial imagery and geographic vector data. A novel, hybrid pixel- and object-based land-cover classification system is developed to extract the information of parking lot, bare soil, and vegetation from aerial imagery. Morphological properties at the zonal level are derived from the geographic data and the land-cover classification results. Statistical analyses, such as scatter graph and nonparametric Kruskal-Wallis test, are used to examine the separability of each pair LUs with respect to the derived DB properties. Selected morphological properties are then used as either independent or input variables of the designed FDT, LDT, and ANN classification algorithms. FDT and LDT are used in a five-level decision tree system and ANN is used directly for LU recognition. The performances of the three designed classification systems are then compared through accuracy assessments; the logistic-decision tree has the best performance with an overall accuracy of 97.05%. In addition, spatial arrangement analysis is used to study the interrelationships of buildings within zones (DB) based on nearest neighbor and Gabriel graph analysis, which show a significant potential of extracting different LUs from mixed-LU zones.