Delineating Urban Fringe Area by Land Cover Information Entropy—An Empirical Study of Guangzhou-Foshan Metropolitan Area, China

Abstract

Abstract:Rapid urbanization has caused many environmental problems, such as the heat island effect, intensifying air pollution, pollution from runoff, loss of wildlife habitat, etc. Accurate evaluations of these problems demand an accurate delineation of the spatial extent of the urban fringe. Conceptual and analytical ambiguity of the urban fringe and a general lack of consensus among researchers have made its measurement very difficult. This study reports a compound and reliable method to delineate the urban fringe area using a case study. Based on the 'fringe effect' theory in landscape ecology, the existing land cover information entropy model for defining the urban fringe is renewed by incorporating scale theory, cartography and urban geography theory. Results show that the urban fringe area of Guangzhou and Foshan metropolitan area covers an area of 2031 km 2 , and it occupies over 31% of the total study area. Result evaluation by industry structure data shows satisfactory correspondence with different land cover types. This paper reports the method and outcome of an attempt to provide an objective, repeatable and generally applicable method for mapping its spatial extent from remote sensing imageries, and could be beneficial to relevant urban studies and urban fringe management projects.

Extraction and Spatial–Temporal Evolution of Urban Fringes: A Case Study of Changchun in Jilin Province, China

“…In comparison with the method of extracting the edge area by using the ratio of built-up area, SHDI can reveal the homogeneity and heterogeneity of landscape spaces and quantify the dispersion and fragmentation degree of urban landscapes. SHDI is influenced by the scale variation and selecting an optimal scale for calculating and analyzing the land cover information entropy is essential to further preserve the pattern and reduce data redundancy [7]. In accordance with previous studies, the 960-meter scale is the optimal scale for land use data interpreted by using Landsat.…”

“…SHDI is influenced by the scale variation and selecting an optimal scale for calculating and analyzing the land cover information entropy is essential to further preserve the pattern and reduce data redundancy [7]. In accordance with previous studies [7], the 960-meter scale is the optimal scale for land use data interpreted by using Landsat. The research area was divided into 4154 grids of size 960 m × 960 m. The area ratios of the different use types of each grid were calculated, and then, the SHDI values of the corresponding grid were calculated in accordance with Equation (1).…”

“…The urban fringe depicts significant differences between two continuous areas. Population diversity, and economic and social development factors are the most obvious characteristics of urban fringes in China [4,7]. Therefore, some scholars have adopted economic and social indicators to divide the urban ISPRS Int.…”

“…Researchers used fuzzy comprehensive evaluation method or a logistic regression model to define the urban fringe area, but these statistical data are updated slowly. In addition, the administrative boundary is also used as the statistical unit, which is not consistent with the reality of urban fringes [7][8][9][10][11]. The land use structure in an urban fringe is complex relative to the core area of a city.…”

Thermodynamics-Based Evaluation of Various Improved Shannon Entropies for Configurational Information of Gray-Level Images

“…Shannon entropy (also called information entropy) is widely recognized as a cornerstone of information theory [19], and it has been used in various fields such as physics recognized as a cornerstone of information theory [19], and it has been used in various fields such as physics e.g., [20], chemistry e.g., [21], and biology e.g., [22]. Although Shannon entropy was originally used to quantify the information (i.e., disorder) of a one-dimensional message (e.g., a telegram message consisting of a series of letters), it has also been actively utilized as a measure of information content for gray-level (or grayscale) images, which can be considered as two-dimensional messages, in various applications including registration, segmentation, and fusion [23][24][25][26][27][28].…”