Abstract
The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts.
Highlights
If today, almost 50% of the world’s population is urban, cities in developing countries are especially well-known for their rapid urbanization rate and their high concentration of population [1].The rapid increases of urban settlements, the spontaneous growth of precarious habitations, as well as self-construction, are some consequences of non-regulatory urbanization processes, population migrations from poor or unsafe regions or non-appropriate planning provision regarding the strength of urban migration fluxes or natural growth rate
This method contains three steps: (1) We firstly define subset areas of interest in order to determine representative objects and characterized urban structures; (2) Secondly, we process to spatial aggregation level of image acquired from a fine spatial resolution in order to obtain a set of images at several spatial resolutions; for (3) determining the optimization criterions at aggregation multi-level by local variance calculation
The results show that the range of Optimal Spatial Resolution (OSR) for urban objects or districts is related closely to the urban structure and spectral variability
Summary
Almost 50% of the world’s population is urban, cities in developing countries are especially well-known for their rapid urbanization rate and their high concentration of population [1]. Remotely sensed imageries represent a very significant data source in this context and provide homogeneous, regular, and up-to-date data to support all tasks of urban planning They have been accepted as an operational tool for mapping, monitoring and modeling of environmental variables and processes. Cities are characterized by a high heterogeneity of materials and urban objects in terms of size, forms and urban fabric morphology This heterogeneity becomes very important in the case of cities in developing countries because of self-construction processes [6]. We examine: (1) how the threshold of homogeneity/heterogeneity of urban structure through fractal dimensions evolves by changing pixel size; and (2) the possible link between the fractal dimensions of such threshold of homogeneity/heterogeneity and the OSR result? The conclusion and perspectives are developed at the end of paper
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